@conference {,
	title = {AI Benchmarking for Science: Efforts from the MLCommons Science Working Group},
	booktitle = {Lecture Notes in Computer Science},
	volume = {13387},
	year = {2023},
	month = {2023-01},
	pages = {47 - 64},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {With machine learning (ML) becoming a transformative tool for science, the scientific community needs a clear catalogue of ML techniques, and their relative benefits on various scientific problems, if they were to make significant advances in science using AI. Although this comes under the purview of benchmarking, conventional benchmarking initiatives are focused on performance, and as such, science, often becomes a secondary criteria.

In this paper, we describe a community effort from a working group, namely, MLCommons Science Working Group, in developing science-specific AI benchmarking for the international scientific community. Since the inception of the working group in 2020, the group has worked very collaboratively with a number of national laboratories, academic institutions and industries, across the world, and has developed four science-specific AI benchmarks. We will describe the overall process, the resulting benchmarks along with some initial results. We foresee that this initiative is likely to be very transformative for the AI for Science, and for performance-focused communities.},
	isbn = {978-3-031-23219-0},
	doi = {10.1007/978-3-031-23220-610.1007/978-3-031-23220-6_4},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-23220-6_4},
	author = {Thiyagalingam, Jeyan and von Laszewski, Gregor and Yin, Junqi and Emani, Murali and Papay, Juri and Barrett, Gregg and Luszczek, Piotr and Tsaris, Aristeidis and Kirkpatrick, Christine and Wang, Feiyi and Gibbs, Tom and Vishwanath, Venkatram and Shankar, Mallikarjun and Fox, Geoffrey and Hey, Tony},
	editor = {Anzt, Hartwig and Bienz, Amanda and Luszczek, Piotr and Baboulin, Marc}
}
@booklet {,
	title = {Earth Virtualization Engines - A Technical Perspective},
	year = {2023},
	month = {2023-09},
	abstract = {Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At their core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change.},
	url = {https://arxiv.org/abs/2309.09002},
	author = {Torsten Hoefler and Bjorn Stevens and Andreas F. Prein and Johanna Baehr and Thomas Schulthess and Thomas F. Stocker and John Taylor and Daniel Klocke and Pekka Manninen and Piers M. Forster and Tobias K{\"o}lling and Nicolas Gruber and Hartwig Anzt and Claudia Frauen and Florian Ziemen and Milan Kl{\"o}wer and Karthik Kashinath and Christoph Sch{\"a}r and Oliver Fuhrer and Bryan N. Lawrence}
}
@conference {,
	title = {Mixed Precision Algebraic Multigrid on GPUs},
	booktitle = { Parallel Processing and Applied Mathematics (PPAM 2022)},
	volume = {13826},
	year = {2023},
	month = {2023-04},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {In this paper, we present the first GPU-native platform-portable algebraic multigrid (AMG) implementation that allows the user to use different precision formats for the distinct multigrid levels. The AMG we present uses an aggregation size 2 parallel graph match as the AMG coarsening strategy. The implementation provides a high level of flexibility in terms of configuring the bottom-level solver and the precision format for the distinct levels. We present convergence and performance results on the GPUs from AMD, Intel, and NVIDIA, and compare against corresponding functionality available in other libraries.},
	keywords = {Algebraic multigrid, GPUs, mixed precision, Portability},
	isbn = {978-3-031-30441-5},
	doi = {10.1007/978-3-031-30442-2_9},
	url = {https://link.springer.com/10.1007/978-3-031-30442-2},
	author = {Tsai, Yu-Hsiang Mike and Natalie Beams and Anzt, Hartwig},
	editor = {Wyrzykowski, Roman and Dongarra, Jack and Deelman, Ewa and Karczewski, Konrad}
}
@conference {,
	title = {Preconditioners for Batched Iterative Linear Solvers on GPUs},
	booktitle = {Smoky Mountains Computational Sciences and Engineering Conference},
	volume = {169075},
	year = {2023},
	month = {2023-01},
	pages = {38 - 53},
	publisher = {Springer Nature Switzerland},
	organization = {Springer Nature Switzerland},
	abstract = {Batched iterative solvers can be an attractive alternative to batched direct solvers if the linear systems allow for fast convergence. In non-batched settings, iterative solvers are often enhanced with sophisticated preconditioners to improve convergence. In this paper, we develop preconditioners for batched iterative solvers that improve the iterative solver convergence without incurring detrimental resource overhead and preserving much of the iterative solver flexibility. We detail the design and implementation considerations, present a user-friendly interface to the batched preconditioners, and demonstrate the convergence and runtime benefits over non-preconditioned batched iterative solvers on state-of-the-art GPUs for a variety of benchmark problems from finite difference stencil matrices, the Suitesparse matrix collection and a computational chemistry application.},
	isbn = {978-3-031-23605-1},
	doi = {10.1007/978-3-031-23606-810.1007/978-3-031-23606-8_3},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-23606-8_3},
	author = {Aggarwal, Isha and Nayak, Pratik and Kashi, Aditya and Anzt, Hartwig},
	editor = {Doug, Kothe and Al, Geist and Pophale, Swaroop and Liu, Hong and Parete-Koon, Suzanne}
}
@conference {,
	title = {Reducing Data Motion and Energy Consumption of Geospatial Modeling Applications Using Automated Precision Conversion},
	booktitle = {2023 IEEE International Conference on Cluster Computing (CLUSTER)},
	year = {2023},
	month = {2023-11},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Santa Fe, NM, USA},
	abstract = {The burgeoning interest in large-scale geospatial modeling, particularly within the domains of climate and weather prediction, underscores the concomitant critical importance of accuracy, scalability, and computational speed. Harnessing these complex simulations{\textquoteright} potential, however, necessitates innovative computational strategies, especially considering the increasing volume of data involved. Recent advancements in Graphics Processing Units (GPUs) have opened up new avenues for accelerating these modeling processes. In particular, their efficient utilization necessitates new strategies, such as mixed-precision arithmetic, that can balance the trade-off between computational speed and model accuracy. This paper leverages PaRSEC runtime system and delves into the opportunities provided by mixed-precision arithmetic to expedite large-scale geospatial modeling in heterogeneous environments. By using an automated conversion strategy, our mixed-precision approach significantly improves computational performance (up to 3X) on Summit supercomputer and reduces the associated energy consumption on various Nvidia GPU generations. Importantly, this implementation ensures the requisite accuracy in environmental applications, a critical factor in their operational viability. The findings of this study bear significant implications for future research and development in high-performance computing, underscoring the transformative potential of mixed-precision arithmetic on GPUs in addressing the computational demands of large-scale geospatial modeling and making a stride toward a more sustainable, efficient, and accurate future in large-scale environmental applications.},
	doi = {10.1109/CLUSTER52292.2023.00035},
	url = {https://ieeexplore.ieee.org/document/10319946/},
	author = {Cao, Qinglei and Abdulah, Sameh and Ltaief, Hatem and Genton, Marc G. and Keyes, David and Bosilca, George}
}
@article {,
	title = {Accelerating Geostatistical Modeling and Prediction With Mixed-Precision Computations: A High-Productivity Approach With PaRSEC},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	volume = {33},
	year = {2022},
	month = {2022-04},
	pages = {964 - 976},
	abstract = {Geostatistical modeling, one of the prime motivating applications for exascale computing, is a technique for predicting desired quantities from geographically distributed data, based on statistical models and optimization of parameters. Spatial data are assumed to possess properties of stationarity or non-stationarity via a kernel fitted to a covariance matrix. A primary workhorse of stationary spatial statistics is Gaussian maximum log-likelihood estimation (MLE), whose central data structure is a dense, symmetric positive definite covariance matrix of the dimension of the number of correlated observations. Two essential operations in MLE are the application of the inverse and evaluation of the determinant of the covariance matrix. These can be rendered through the Cholesky decomposition and triangular solution. In this contribution, we reduce the precision of weakly correlated locations to single- or half- precision based on distance. We thus exploit mathematical structure to migrate MLE to a three-precision approximation that takes advantage of contemporary architectures offering BLAS3-like operations in a single instruction that are extremely fast for reduced precision. We illustrate application-expected accuracy worthy of double-precision from a majority half-precision computation, in a context where uniform single-precision is by itself insufficient. In tackling the complexity and imbalance caused by the mixing of three precisions, we deploy the PaRSEC runtime system. PaRSEC delivers on-demand casting of precisions while orchestrating tasks and data movement in a multi-GPU distributed-memory environment within a tile-based Cholesky factorization. Application-expected accuracy is maintained while achieving up to 1.59X by mixing FP64/FP32 operations on 1536 nodes of HAWK or 4096 nodes of Shaheen II , and up to 2.64X by mixing FP64/FP32/FP16 operations on 128 nodes of Summit , relative to FP64-only operations. This translates into up to 4.5, 4.7, ...},
	keywords = {Computational modeling, Covariance matrices, Data models, Maximum likelihood estimation, Predictive models, runtime, Task analysis},
	issn = {1045-9219},
	doi = {10.1109/TPDS.2021.3084071},
	url = {https://ieeexplore.ieee.org/document/9442267/https://ieeexplore.ieee.org/ielam/71/9575177/9442267-aam.pdfhttp://xplorestaging.ieee.org/ielx7/71/9575177/09442267.pdf?arnumber=9442267},
	author = {Abdulah, Sameh and Qinglei Cao and Pei, Yu and George Bosilca and Jack Dongarra and Genton, Marc G. and Keyes, David E. and Ltaief, Hatem and Sun, Ying}
}
@inbook {,
	title = {Batch QR Factorization on GPUs: Design, Optimization, and Tuning},
	booktitle = { Lecture Notes in Computer Science},
	volume = {13350},
	year = {2022},
	month = {2022-06},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {QR factorization of dense matrices is a ubiquitous tool in high performance computing (HPC). From solving linear systems and least squares problems to eigenvalue problems, and singular value decompositions, the impact of a high performance QR factorization is fundamental to computer simulations and many applications. More importantly, the QR factorization on a batch of relatively small matrices has acquired a lot of attention in sparse direct solvers and low-rank approximations for Hierarchical matrices. To address this interest and demand, we developed and present a high performance batch QR factorization for Graphics Processing Units (GPUs). We present a multi-level blocking strategy that adjusts various algorithmic designs to the size of the input matrices. We also show that following the LAPACK QR design convention, while still useful, is significantly outperformed by unconventional code structures that increase data reuse. The performance results show multi-fold speedups against the state of the art libraries on the latest GPU architectures from both NVIDIA and AMD.},
	keywords = {Batch linear algebra, GPU computing, QR factorization},
	isbn = {978-3-031-08750-9},
	doi = {10.1007/978-3-031-08751-6_5},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-08751-6_5},
	author = {Abdelfattah, Ahmad and Stanimire Tomov and Dongarra, Jack},
	editor = {Groen, Derek and de Mulatier, C{\'e}lia and Paszy{\'n}ski, Maciej and Krzhizhanovskaya, Valeria V. and Dongarra, Jack J. and Sloot, Peter M. A.}
}
@conference {,
	title = {Batched sparse iterative solvers on GPU for the collision operator for fusion plasma simulations},
	booktitle = {2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
	year = {2022},
	month = {2022-07},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Lyon, France},
	abstract = {Batched linear solvers, which solve many small related but independent problems, are important in several applications. This is increasingly the case for highly parallel processors such as graphics processing units (GPUs), which need a substantial amount of work to keep them operating efficiently and solving smaller problems one-by-one is not an option. Because of the small size of each problem, the task of coming up with a parallel partitioning scheme and mapping the problem to hardware is not trivial. In recent history, significant attention has been given to batched dense linear algebra. However, there is also an interest in utilizing sparse iterative solvers in a batched form, and this presents further challenges. An example use case is found in a gyrokinetic Particle-In-Cell (PIC) code used for modeling magnetically confined fusion plasma devices. The collision operator has been identified as a bottleneck, and a proxy app has been created for facilitating optimizations and porting to GPUs. The current collision kernel linear solver does not run on the GPU-a major bottleneck. As these matrices are well-conditioned, batched iterative sparse solvers are an attractive option. A batched sparse iterative solver capability has recently been developed in the Ginkgo library. In this paper, we describe how the software architecture can be used to develop an efficient solution for the XGC collision proxy app. Comparisons for the solve times on NVIDIA V100 and A100 GPUs and AMD MI100 GPUs with one dual-socket Intel Xeon Skylake CPU node with 40 OpenMP threads are presented for matrices representative of those required in the collision kernel of XGC. The results suggest that GINKGO{\textquoteright}s batched sparse iterative solvers are well suited for efficient utilization of the GPU for this problem, and the performance portability of Ginkgo in conjunction with Kokkos (used within XGC as the heterogeneous programming model) allows seamless execution for exascale oriented heterogeneous architectures at the various leadership supercomputing facilities.},
	doi = {10.1109/IPDPS53621.2022.00024},
	url = {https://ieeexplore.ieee.org/document/9820663},
	author = {Kashi, Aditya and Nayak, Pratik and Kulkarni, Dhruva and Scheinberg, Aaron and Lin, Paul and Anzt, Hartwig}
}
@article {,
	title = {Computational science for a better future},
	journal = {Journal of Computational Science},
	volume = {62},
	year = {2022},
	month = {2022-07},
	pages = {101745},
	issn = {18777503},
	doi = {10.1016/j.jocs.2022.101745},
	url = {https://www.sciencedirect.com/science/article/pii/S1877750322001351},
	author = {Kovalchuk, Sergey V. and Krzhizhanovskaya, Valeria V. and Paszy{\'n}ski, Maciej and Kranzlm{\"u}ller, Dieter and Dongarra, Jack and Sloot, Peter M.A.}
}
@conference {,
	title = {A Framework to Exploit Data Sparsity in Tile Low-Rank Cholesky Factorization},
	booktitle = {IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
	year = {2022},
	month = {2022-07},
	doi = {10.1109/IPDPS53621.2022.00047},
	url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=9820680\&isnumber=9820610},
	author = {Qinglei Cao and Rabab Alomairy and Yu Pei and George Bosilca and Hatem Ltaief and David Keyes and Jack Dongarra}
}
@article {,
	title = {OpenMP application experiences: Porting to accelerated nodes},
	journal = {Parallel Computing},
	volume = {109},
	year = {2022},
	month = {2022-03},
	abstract = {As recent enhancements to the OpenMP specification become available in its implementations, there is a need to share the results of experimentation in order to better understand the OpenMP implementation{\textquoteright}s behavior in practice, to identify pitfalls, and to learn how the implementations can be effectively deployed in scientific codes. We report on experiences gained and practices adopted when using OpenMP to port a variety of ECP applications, mini-apps and libraries based on different computational motifs to accelerator-based leadership-class high-performance supercomputer systems at the United States Department of Energy. Additionally, we identify important challenges and open problems related to the deployment of OpenMP. Through our report of experiences, we find that OpenMP implementations are successful on current supercomputing platforms and that OpenMP is a promising programming model to use for applications to be run on emerging and future platforms with accelerated nodes.},
	issn = {01678191},
	doi = {10.1016/j.parco.2021.102856},
	url = {https://www.sciencedirect.com/science/article/pii/S0167819121001009},
	author = {Bak, Seonmyeong and Bertoni, Colleen and Boehm, Swen and Budiardja, Reuben and Chapman, Barbara M. and Doerfert, Johannes and Eisenbach, Markus and Finkel, Hal and Hernandez, Oscar and Huber, Joseph and Iwasaki, Shintaro and Kale, Vivek and Kent, Paul R.C. and Kwack, JaeHyuk and Lin, Meifeng and Luszczek, Piotr and Luo, Ye and Pham, Buu and Pophale, Swaroop and Ravikumar, Kiran and Sarkar, Vivek and Scogland, Thomas and Tian, Shilei and Yeung, P.K.}
}
@inproceedings {,
	title = {Reshaping Geostatistical Modeling and Prediction for Extreme-Scale Environmental Applications},
	journal = {2022 International Conference for High Performance Computing, Networking, Storage and Analysis (SC22)},
	year = {2022},
	month = {2022-11},
	publisher = {IEEE Press},
	address = {Dallas, TX},
	abstract = {We extend the capability of space-time geostatistical modeling using algebraic approximations, illustrating application-expected accuracy worthy of double precision from majority low-precision computations and low-rank matrix approximations. We exploit the mathematical structure of the dense covariance matrix whose inverse action and determinant are repeatedly required in Gaussian log-likelihood optimization. Geostatistics augments first-principles modeling approaches for the prediction of environmental phenomena given the availability of measurements at a large number of locations; however, traditional Cholesky-based approaches grow cubically in complexity, gating practical extension to continental and global datasets now available. We combine the linear algebraic contributions of mixed-precision and low-rank computations within a tilebased Cholesky solver with on-demand casting of precisions and dynamic runtime support from PaRSEC to orchestrate tasks and data movement. Our adaptive approach scales on various systems and leverages the Fujitsu A64FX nodes of Fugaku to achieve up to 12X performance speedup against the highly optimized dense Cholesky implementation.},
	keywords = {climate/weather prediction, dynamic runtime systems, high performance computing., low- rank matrix approximations, mixed-precision computations, space-time geospatial statistics, Task-based programming models},
	isbn = {9784665454445},
	url = {https://dl.acm.org/doi/abs/10.5555/3571885.3571888},
	author = {Cao, Qinglei and Abdulah, Sameh and Rabab Alomairy and Pei, Yu and Pratik Nag and George Bosilca and Dongarra, Jack and Genton, Marc G. and Keyes, David and Ltaief, Hatem and Sun, Ying}
}
@article {,
	title = {Resiliency in numerical algorithm design for extreme scale simulations},
	journal = {The International Journal of High Performance Computing Applications},
	volume = {36371337212766180823},
	year = {2022},
	month = {2022-03},
	pages = {251 - 285},
	keywords = {Fault tolerance, Numerical algorithms, parallel computer architecture, resilience},
	issn = {1094-3420},
	doi = {10.1177/10943420211055188},
	url = {http://journals.sagepub.com/doi/10.1177/10943420211055188http://journals.sagepub.com/doi/pdf/10.1177/10943420211055188http://journals.sagepub.com/doi/pdf/10.1177/10943420211055188http://journals.sagepub.com/doi/full-xml/10.1177/10943420211055188},
	author = {Agullo, Emmanuel and Altenbernd, Mirco and Anzt, Hartwig and Bautista-Gomez, Leonardo and Benacchio, Tommaso and Bonaventura, Luca and Bungartz, Hans-Joachim and Chatterjee, Sanjay and Ciorba, Florina M and DeBardeleben, Nathan and Drzisga, Daniel and Eibl, Sebastian and Engelmann, Christian and Gansterer, Wilfried N and Giraud, Luc and G{\"o}ddeke, Dominik and Heisig, Marco and J{\'e}z{\'e}quel, Fabienne and Kohl, Nils and Li, Xiaoye Sherry and Lion, Romain and Mehl, Miriam and Mycek, Paul and Obersteiner, Michael and Quintana-Ort{\'\i}, Enrique S and Rizzi, Francesco and R{\"u}de, Ulrich and Schulz, Martin and Fung, Fred and Speck, Robert and Stals, Linda and Teranishi, Keita and Thibault, Samuel and Th{\"o}nnes, Dominik and Wagner, Andreas and Wohlmuth, Barbara}
}
@article {,
	title = {20 years of computational science: Selected papers from 2020 International Conference on Computational Science},
	journal = {Journal of Computational Science},
	volume = {53},
	year = {2021},
	pages = {101395{\textendash}101395},
	abstract = {We thank the authors of the selected papers for their valuable contributions, the reviewers of this special section for their in-depth reviews and constructive comments, the ICCS program committee members, and workshop organizers for their diligent work ensuring the high standard of accepted ICCS papers. As always, we also thank Springer for publishing the conference proceedings and Elsevier for their continuous support and inspiration during the preparation and publishing of this virtual special issue.},
	doi = {10.1016/j.jocs.2021.101395},
	author = {Kovalchuk, Sergey V and Krzhizhanovskaya, Valeria V and Sloot, PMA and Z{\'a}vodszky, G{\'a}bor and Lees, Michael H and Paszy{\'n}ski, M and Jack Dongarra}
}
@article {,
	title = {Efficient exascale discretizations: High-order finite element methods},
	journal = {The International Journal of High Performance Computing Applications},
	year = {2021},
	pages = {10943420211020803},
	abstract = {Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free/partially assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale Computing Project that is focused on the development of next-generation discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications.},
	keywords = {co-design, high-order discretizations, High-performance computing, PDEs, unstructured grids},
	doi = {10.1177/10943420211020803},
	author = {Kolev, Tzanio and Fischer, Paul and Min, Misun and Jack Dongarra and Brown, Jed and Dobrev, Veselin and Warburton, Tim and Stanimire Tomov and Shephard, Mark S and Abdelfattah, Ahmad and others}
}
@article {,
	title = {Gingko: A Sparse Linear Algebrea Library for HPC},
	year = {2021},
	month = {2021-04},
	publisher = {2021 ECP Annual Meeting},
	author = {Hartwig Anzt and Natalie Beams and Terry Cojean and Fritz G{\"o}bel and Thomas Gr{\"u}tzmacher and Aditya Kashi and Pratik Nayak and Tobias Ribizel and Yuhsiang M. Tsai}
}
@conference {,
	title = {Leveraging PaRSEC Runtime Support to Tackle Challenging 3D Data-Sparse Matrix Problems},
	booktitle = {35th IEEE International Parallel \& Distributed Processing Symposium (IPDPS 2021)},
	year = {2021},
	month = {2021-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Portland, OR},
	abstract = {The task-based programming model associated with dynamic runtime systems has gained popularity for challenging problems because of workload imbalance, heterogeneous resources, or extreme concurrency. During the last decade, lowrank matrix approximations, where the main idea consists of exploiting data sparsity typically by compressing off-diagonal tiles up to an application-specific accuracy threshold, have been adopted to address the curse of dimensionality at extreme scale. In this paper, we create a bridge between the runtime and the linear algebra by communicating knowledge of the data sparsity to the runtime. We design and implement this synergistic approach with high user productivity in mind, in the context of the PaRSEC runtime system and the HiCMA numerical library. This requires to extend PaRSEC with new features to integrate rank information into the dataflow so that proper decisions can be taken at runtime. We focus on the tile low-rank (TLR) Cholesky factorization for solving 3D data-sparse covariance matrix problems arising in environmental applications. In particular, we employ the 3D exponential model of Matern matrix kernel, which exhibits challenging nonuniform {\textasciiacute}high ranks in off-diagonal tiles. We first provide a dynamic data structure management driven by a performance model to reduce extra floating-point operations. Next, we optimize the memory footprint of the application by relying on a dynamic memory allocator, and supported by a rank-aware data distribution to cope with the workload imbalance. Finally, we expose further parallelism using kernel recursive formulations to shorten the critical path. Our resulting high-performance implementation outperforms existing data-sparse TLR Cholesky factorization by up to 7-fold on a large-scale distributed-memory system, while minimizing the memory footprint up to a 44-fold factor. This multidisciplinary work highlights the need to empower runtime systems beyond their original duty of task scheduling for servicing next-generation low-rank matrix algebra libraries.},
	keywords = {asynchronous executions and load balancing, dynamic runtime system, environmental applications, High-performance computing, low-rank matrix computations, task-based programming model, user productivity},
	author = {Qinglei Cao and Yu Pei and Kadir Akbudak and George Bosilca and Hatem Ltaief and David Keyes and Jack Dongarra}
}
@article {,
	title = {libCEED: Fast algebra for high-order element-based discretizations},
	journal = {Journal of Open Source Software},
	volume = {6},
	number = {63},
	year = {2021},
	pages = {2945},
	abstract = {Finite element methods are widely used to solve partial differential equations (PDE) in science and engineering, but their standard implementation (Arndt et al., 2020; Kirk et al., 2006; Logg et al., 2012) relies on assembling sparse matrices. Sparse matrix multiplication and triangular operations perform a scalar multiply and add for each nonzero entry, just 2 floating point operations (flops) per scalar that must be loaded from memory (Williams et al., 2009). Modern hardware is capable of nearly 100 flops per scalar streamed from memory (Rupp, 2020) so sparse matrix operations cannot achieve more than about 2\% utilization of arithmetic units.
Matrix assembly becomes even more problematic when the polynomial degree p of the basis functions is increased, resulting in O(pd) storage and O(p2d) compute per degree of freedom (DoF) in d dimensions. Methods pioneered by the spectral element community (Deville et al., 2002; Orszag, 1980) exploit problem structure to reduce costs to O(1) storage and O(p)
compute per DoF, with very high utilization of modern CPUs and GPUs. Unfortunately, highquality implementations have been relegated to applications and intrusive frameworks that are often difficult to extend to new problems or incorporate into legacy applications, especially when strong preconditioners are required.
libCEED, the Code for Efficient Extensible Discretization (Abdelfattah et al., 2021), is a lightweight library that provides a purely algebraic interface for linear and nonlinear operators and preconditioners with element-based discretizations. libCEED provides portable performance via run-time selection of implementations optimized for CPUs and GPUs, including
support for just-in-time (JIT) compilation. It is designed for convenient use in new and legacy software, and offers interfaces in C99 (International Standards Organisation, 1999), Fortran77 (ANSI, 1978), Python (Python, 2021), Julia (Bezanson et al., 2017), and Rust (Rust, 2021). Users and library developers can integrate libCEED at a low level into existing applications in
place of existing matrix-vector products without significant refactoring of their own discretization infrastructure. Alternatively, users can utilize integrated libCEED support in MFEM (Anderson et al., 2020; MFEM, 2021).
In addition to supporting applications and discretization libraries, libCEED provides a platform for performance engineering and co-design, as well as an algebraic interface for solvers research like adaptive p-multigrid, much like how sparse matrix libraries enable development and deployment of algebraic multigrid solvers},
	keywords = {finite elements, high-order methods, High-performance computing, matrix-free, spectral elements},
	doi = {10.21105/joss.02945},
	url = {https://doi.org/10.21105/joss.02945},
	author = {Jed Brown and Ahmad Abdelfattah and Valeria Barra and Natalie Beams and Jean-Sylvain Camier and Veselin Dobrev and Yohann Dudouit and Leila Ghaffari and Tzanio Kolev and David Medina and Will Pazner and Thilina Ratnayaka and Jeremy Thompson and Stanimire Tomov}
}
@article {,
	title = {Materials fingerprinting classification},
	journal = {Computer Physics Communications},
	year = {2021},
	month = {Jan-05-2021},
	pages = {108019},
	abstract = {Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates. Methods for visualizing the local atomic structure, such as atom probe tomography (APT), which routinely generate datasets comprised of millions of atoms, are an important step in realizing this goal. However, state-of-the-art APT instruments generate noisy and sparse datasets that provide information about elemental type, but obscure atomic structures, thus limiting their subsequent value for materials discovery. The application of a materials fingerprinting process, a machine learning algorithm coupled with topological data analysis, provides an avenue by which here-to-fore unprecedented structural information can be extracted from an APT dataset. As a proof of concept, the material fingerprint is applied to high-entropy alloy APT datasets containing body-centered cubic (BCC) and face-centered cubic (FCC) crystal structures. A local atomic configuration centered on an arbitrary atom is assigned a topological descriptor, with which it can be characterized as a BCC or FCC lattice with near perfect accuracy, despite the inherent noise in the dataset. This successful identification of a fingerprint is a crucial first step in the development of algorithms which can extract more nuanced information, such as chemical ordering, from existing datasets of complex materials.},
	keywords = {Atom probe tomography, High entropy alloy, Machine Learning, Materials discovery, Topological data analysis},
	issn = {00104655},
	doi = {10.1016/j.cpc.2021.108019},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0010465521001314},
	author = {Spannaus, Adam and Law, Kody J.H. and Piotr Luszczek and Nasrin, Farzana and Micucci, Cassie Putman and Liaw, Peter K. and Santodonato, Louis J. and Keffer, David J. and Maroulas, Vasileios}
}
@techreport {,
	title = {P1673R3: A Free Function Linear algebra Interface Based on the BLAS},
	journal = {ISO JTC1 SC22 WG22},
	number = {P1673R3},
	year = {2021},
	month = {2021-04},
	publisher = {ISO},
	type = {standard},
	abstract = {We believe this proposal is complementary to P1385, a proposal for a C++ Standard linear algebra library that introduces matrix and vector classes and overloaded arithmetic operators. In fact, we think that our proposal would make a natural foundation for a library like what P1385 proposes. However, a free function interface -- which clearly separates algorithms from data structures -- more naturally allows for a richer set of operations such as what the BLAS provides. A natural extension of the present proposal would include accepting P1385{\textquoteright}s matrix and vector objects as input for the algorithms proposed here. A straightforward way to do that would be for P1385{\textquoteright}s matrix and vector objects to make views of their data available as basic_mdspan.},
	keywords = {C++, linear algebra},
	url = {http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2021/p1673r3.pdf},
	author = {Mark Hoemmen and Daisy Hollman and Christian Trott and Daniel Sunderland and Nevin Liber and Li-Ta Lo and Damien Lebrun-Grandie and Graham Lopez and Peter Caday and Sarah Knepper and Piotr Luszczek and Timothy Costa}
}
@article {,
	title = {A Set of Batched Basic Linear Algebra Subprograms and LAPACK Routines},
	journal = {ACM Transactions on Mathematical Software (TOMS)},
	volume = {47},
	number = {3},
	year = {2021},
	pages = {1{\textendash}23},
	abstract = {This article describes a standard API for a set of Batched Basic Linear Algebra Subprograms (Batched BLAS or BBLAS). The focus is on many independent BLAS operations on small matrices that are grouped together and processed by a single routine, called a Batched BLAS routine. The matrices are grouped together in uniformly sized groups, with just one group if all the matrices are of equal size. The aim is to provide more efficient, but portable, implementations of algorithms on high-performance many-core platforms. These include multicore and many-core CPU processors, GPUs and coprocessors, and other hardware accelerators with floating-point compute facility. As well as the standard types of single and double precision, we also include half and quadruple precision in the standard. In particular, half precision is used in many very large scale applications, such as those associated with machine learning.},
	keywords = {Computations on matrices, Mathematical analysis, Mathematics of computing, Numerical analysis},
	doi = {10.1145/3431921},
	author = {Abdelfattah, Ahmad and Costa, Timothy and Jack Dongarra and Mark Gates and Haidar, Azzam and Hammarling, Sven and Higham, Nicholas J and Kurzak, Jakub and Piotr Luszczek and Stanimire Tomov and others}
}
@techreport {,
	title = {CEED ECP Milestone Report: Improve Performance and Capabilities of CEED-Enabled ECP Applications on Summit/Sierra},
	journal = {ECP Milestone Reports},
	year = {2020},
	month = {2020-05},
	publisher = {Zenodo},
	doi = {https://doi.org/10.5281/zenodo.3860804},
	url = {https://doi.org/10.5281/zenodo.3860804},
	author = {Kolev, Tzanio and Fischer, Paul and Abdelfattah, Ahmad and Ananthan, Shreyas and Valeria Barra and Natalie Beams and Bleile, Ryan and Brown, Jed and Carson, Robert and Camier, Jean-Sylvain and Churchfield, Matthew and Dobrev, Veselin and Jack Dongarra and Dudouit, Yohann and Karakus, Ali and Kerkemeier, Stefan and Lan, YuHsiang and Medina, David and Merzari, Elia and Min, Misun and Parker, Scott and Ratnayaka, Thilina and Smith, Cameron and Sprague, Michael and Stitt, Thomas and Thompson, Jeremy and Tomboulides, Ananias and Stanimire Tomov and Tomov, Vladimir and Vargas, Arturo and Warburton, Tim and Weiss, Kenneth}
}
@book {,
	title = {Computational Science {\textendash} ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3{\textendash}5, 2020, Proceedings, Part III},
	series = {Lecture Notes in Computer Science},
	number = {12139},
	year = {2020},
	month = {2020-06},
	pages = {648},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	edition = {1},
	isbn = {978-3-030-50420-5},
	doi = {https://doi.org/10.1007/978-3-030-50420-5},
	author = {Valeria Krzhizhanovskaya and G{\'a}bor Z{\'a}vodszky and Michael Lees and Jack Dongarra and Peter Sloot and S{\'e}rgio Brissos and Jo{\~a}o Teixeira}
}
@book {,
	title = {Computational Science {\textendash} ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3{\textendash}5, 2020, Proceedings, Part I},
	series = {Lecture Notes in Computer Science},
	number = {12137},
	year = {2020},
	month = {2020-06},
	pages = {707},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	edition = {1},
	isbn = {978-3-030-50371-0},
	doi = {https://doi.org/10.1007/978-3-030-50371-0},
	author = {Valeria Krzhizhanovskaya and G{\'a}bor Z{\'a}vodszky and Michael Lees and Jack Dongarra and Peter Sloot and S{\'e}rgio Brissos and Jo{\~a}o Teixeira}
}
@book {,
	title = {Computational Science {\textendash} ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3{\textendash}5, 2020, Proceedings, Part IV},
	series = {Lecture Notes in Computer Science},
	number = {12140},
	year = {2020},
	month = {2020-06},
	pages = {668},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	edition = {1},
	isbn = {978-3-030-50423-6},
	doi = {https://doi.org/10.1007/978-3-030-50423-6},
	author = {Valeria Krzhizhanovskaya and G{\'a}bor Z{\'a}vodszky and Michael Lees and Jack Dongarra and Peter Sloot and S{\'e}rgio Brissos and Jo{\~a}o Teixeira}
}
@book {,
	title = {Computational Science {\textendash} ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3{\textendash}5, 2020, Proceedings, Part II},
	series = {Lecture Notes in Computer Science},
	number = {12138},
	year = {2020},
	month = {2020-06},
	pages = {697},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	edition = {1},
	isbn = {978-3-030-50417-5},
	doi = {https://doi.org/10.1007/978-3-030-50417-5},
	author = {Valeria Krzhizhanovskaya and G{\'a}bor Z{\'a}vodszky and Michael Lees and Jack Dongarra and Peter Sloot and S{\'e}rgio Brissos and Jo{\~a}o Teixeira}
}
@book {,
	title = {Computational Science {\textendash} ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3{\textendash}5, 2020, Proceedings, Part VII},
	series = {Lecture Notes in Computer Science},
	number = {12143},
	year = {2020},
	month = {2020-06},
	pages = {775},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	edition = {1},
	isbn = {978-3-030-50436-6},
	doi = {https://doi.org/10.1007/978-3-030-50436-6},
	author = {Valeria Krzhizhanovskaya and G{\'a}bor Z{\'a}vodszky and Michael Lees and Jack Dongarra and Peter Sloot and S{\'e}rgio Brissos and Jo{\~a}o Teixeira}
}
@book {,
	title = {Computational Science {\textendash} ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3{\textendash}5, 2020, Proceedings, Part VI},
	series = {Lecture Notes in Computer Science},
	number = {12142},
	year = {2020},
	month = {2020-06},
	pages = {667},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	edition = {1},
	isbn = {978-3-030-50433-5},
	doi = {https://doi.org/10.1007/978-3-030-50433-5},
	author = {Valeria Krzhizhanovskaya and G{\'a}bor Z{\'a}vodszky and Michael Lees and Jack Dongarra and Peter Sloot and S{\'e}rgio Brissos and Jo{\~a}o Teixeira}
}
@book {,
	title = {Computational Science {\textendash} ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3{\textendash}5, 2020, Proceedings, Part V},
	series = {Lecture Notes in Computer Science},
	number = {12141},
	year = {2020},
	month = {2020-06},
	pages = {618},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	edition = {1},
	isbn = {978-3-030-50426-7},
	doi = {https://doi.org/10.1007/978-3-030-50426-7},
	author = {Valeria Krzhizhanovskaya and G{\'a}bor Z{\'a}vodszky and Michael Lees and Jack Dongarra and Peter Sloot and S{\'e}rgio Brissos and Jo{\~a}o Teixeira}
}
@conference {,
	title = {Extreme-Scale Task-Based Cholesky Factorization Toward Climate and Weather Prediction Applications},
	booktitle = {Platform for Advanced Scientific Computing Conference (PASC20)},
	year = {2020},
	month = {2020-06},
	publisher = {ACM},
	organization = {ACM},
	address = {Geneva, Switzerland},
	abstract = {Climate and weather can be predicted statistically via geospatial Maximum Likelihood Estimates (MLE), as an alternative to running large ensembles of forward models. The MLE-based iterative optimization procedure requires the solving of large-scale linear systems that performs a Cholesky factorization on a symmetric positive-definite covariance matrix---a demanding dense factorization in terms of memory footprint and computation. We propose a novel solution to this problem: at the mathematical level, we reduce the computational requirement by exploiting the data sparsity structure of the matrix off-diagonal tiles by means of low-rank approximations; and, at the programming-paradigm level, we integrate PaRSEC, a dynamic, task-based runtime to reach unparalleled levels of efficiency for solving extreme-scale linear algebra matrix operations. The resulting solution leverages fine-grained computations to facilitate asynchronous execution while providing a flexible data distribution to mitigate load imbalance. Performance results are reported using 3D synthetic datasets up to 42M geospatial locations on 130, 000 cores, which represent a cornerstone toward fast and accurate predictions of environmental applications.},
	doi = {https://doi.org/10.1145/3394277.3401846},
	author = {Qinglei Cao and Yu Pei and Kadir Akbudak and Aleksandr Mikhalev and George Bosilca and Hatem Ltaief and David Keyes and Jack Dongarra}
}
@conference {,
	title = {High-Order Finite Element Method using Standard and Device-Level Batch GEMM on GPUs},
	booktitle = {2020 IEEE/ACM 11th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA)},
	year = {2020},
	month = {2020-11},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present new GPU implementations of the tensor contractions arising from basis-related computations for highorder finite element methods. We consider both tensor and nontensor bases. In the case of tensor bases, we introduce new kernels based on a series of fused device-level matrix multiplications (GEMMs), specifically designed to utilize the fast memory of the GPU. For non-tensor bases, we develop a tuned framework for choosing standard batch-BLAS GEMMs that will maximize performance across groups of elements. The implementations are included in a backend of the libCEED library. We present benchmark results for the diffusion and mass operators using libCEED integration through the MFEM finite element library and compare to those of the previously best-performing GPU backends for stand-alone basis computations. In tensor cases, we see improvements of approximately 10-30\% for some cases, particularly for higher basis orders. For the non-tensor tests, the new batch-GEMMs implementation is twice as fast as what was previously available for basis function order greater than five and greater than approximately 105 degrees of freedom in the mesh; up to ten times speedup is seen for eighth-order basis functions.},
	keywords = {Batched linear algebra, finite elements, gpu, high-order methods, matrix-free FEM, Tensor contractions},
	author = {Natalie Beams and Ahmad Abdelfattah and Stanimire Tomov and Jack Dongarra and Tzanio Kolev and Yohann Dudouit}
}
@book {,
	title = {Parallel Processing and Applied Mathematics: 13th International Conference, PPAM 2019, Bialystok, Poland, September 8{\textendash}11, 2019, Revised Selected Papers, Part I},
	series = { Lecture Notes in Computer Science},
	number = {12043},
	year = {2020},
	month = {2020-03},
	pages = {581},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	edition = {1},
	isbn = {978-3-030-43229-4},
	doi = {https://doi.org/10.1007/978-3-030-43229-4},
	author = {Roman Wyrzykowski and Ewa Deelman and Jack Dongarra and Konrad Karczewski}
}
@book {,
	title = {Parallel Processing and Applied Mathematics: 13th International Conference, PPAM 2019, Bialystok, Poland, September 8{\textendash}11, 2019, Revised Selected Papers, Part II},
	series = {Lecture Notes in Computer Science},
	number = {12044},
	year = {2020},
	month = {2020-03},
	pages = {503},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	isbn = {978-3-030-43222-5},
	doi = {https://doi.org/10.1007/978-3-030-43222-5},
	author = {Roman Wyrzykowski and Ewa Deelman and Jack Dongarra and Konrad Karczewski}
}
@conference {,
	title = {Predicting MPI Collective Communication Performance Using Machine Learning},
	booktitle = {2020 IEEE International Conference on Cluster Computing (CLUSTER)},
	year = {2020},
	month = {2020-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Kobe, Japan},
	abstract = {The Message Passing Interface (MPI) defines the semantics of data communication operations, while the implementing libraries provide several parameterized algorithms for each operation. Each algorithm of an MPI collective operation may work best on a particular system and may be dependent on the specific communication problem. Internally, MPI libraries employ heuristics to select the best algorithm for a given communication problem when being called by an MPI application. The majority of MPI libraries allow users to override the default algorithm selection, enabling the tuning of this selection process. The problem then becomes how to select the best possible algorithm for a specific case automatically. In this paper, we address the algorithm selection problem for MPI collective communication operations. To solve this problem, we propose an auto-tuning framework for collective MPI operations based on machine-learning techniques. First, we execute a set of benchmarks of an MPI library and its entire set of collective algorithms. Second, for each algorithm, we fit a performance model by applying regression learners. Last, we use the regression models to predict the best possible (fastest) algorithm for an unseen communication problem. We evaluate our approach for different MPI libraries and several parallel machines. The experimental results show that our approach outperforms the standard algorithm selection heuristics, which are hard-coded into the MPI libraries, by a significant margin.},
	keywords = {Auto-tuning, GAM, KNN, Machine Learning, message passing interface, Performance Prediction, XGBoost},
	doi = {https://doi.org/10.1109/CLUSTER49012.2020.00036},
	author = {Sascha Hunold and Abhinav Bhatele and George Bosilca and Peter Knees}
}
@article {,
	title = {A Set of Batched Basic Linear Algebra Subprograms},
	journal = {ACM Transactions on Mathematical Software},
	year = {2020},
	month = {2020-10},
	abstract = {This paper describes a standard API for a set of Batched Basic Linear Algebra Subprograms (Batched BLAS or BBLAS). The focus is on many independent BLAS operations on small matrices that are grouped together and processed by a single routine, called a Batched BLAS routine. The matrices are grouped together in uniformly sized groups, with just one group if all the matrices are of equal size. The aim is to provide more efficient, but portable, implementations of algorithms on high-performance many-core platforms. These include multicore and many-core CPU processors, GPUs and coprocessors, and other hardware accelerators with floating-point compute facility. As well as the standard types of single and double precision, we also include half and quadruple precision in the standard. In particular half precision is used in many very large scale applications, such as those associated with machine learning.},
	author = {Ahmad Abdelfattah and Timothy Costa and Jack Dongarra and Mark Gates and Azzam Haidar and Sven Hammarling and Nicholas J. Higham and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov and Mawussi Zounon}
}
@article {,
	title = {SLATE: Software for Linear Algebra Targeting Exascale (POSTER)},
	year = {2020},
	month = {2020-02},
	publisher = {2020 Exascale Computing Project Annual Meeting},
	address = {Houston, TX},
	author = {Mark Gates and Ali Charara and Jakub Kurzak and Asim YarKhan and Mohammed Al Farhan and Dalal Sukkari and Jack Dongarra}
}
@article {1464,
	title = {SLATE Tutorial},
	year = {2020},
	month = {2020-02},
	publisher = {2020 ECP Annual Meeting},
	address = {Houston, TX},
	author = {Mark Gates and Jakub Kurzak and Asim YarKhan and Ali Charara and Jamie Finney and Dalal Sukkari and Mohammed Al Farhan and Ichitaro Yamazaki and Panruo Wu and Jack Dongarra}
}
@techreport {1278,
	title = {SLATE Users{\textquoteright} Guide},
	journal = {SLATE Working Notes},
	number = {10, ICL-UT-19-01},
	year = {2020},
	month = {2020-07},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	type = {SLATE Working Notes},
	author = {Mark Gates and Ali Charara and Jakub Kurzak and Asim YarKhan and Mohammed Al Farhan and Dalal Sukkari and Jack Dongarra}
}
@conference {,
	title = {Task Bench: A Parameterized Benchmark for Evaluating Parallel Runtime Performance},
	booktitle = {International Conference for High Performance Computing Networking, Storage, and Analysis (SC20)},
	year = {2020},
	month = {2020-11},
	publisher = {ACM},
	organization = {ACM},
	abstract = {We present Task Bench, a parameterized benchmark designed to explore the performance of distributed programming systems under a variety of application scenarios. Task Bench dramatically lowers the barrier to benchmarking and comparing multiple programming systems by making the implementation for a given system orthogonal to the benchmarks themselves: every benchmark constructed with Task Bench runs on every Task Bench implementation. Furthermore, Task Bench{\textquoteright}s parameterization enables a wide variety of benchmark scenarios that distill the key characteristics of larger applications.

To assess the effectiveness and overheads of the tested systems, we introduce a novel metric, minimum effective task granularity (METG). We conduct a comprehensive study with 15 programming systems on up to 256 Haswell nodes of the Cori supercomputer. Running at scale, 100μs-long tasks are the finest granularity that any system runs efficiently with current technologies. We also study each system{\textquoteright}s scalability, ability to hide communication and mitigate load imbalance.},
	url = {https://dl.acm.org/doi/10.5555/3433701.3433783},
	author = {Elliott Slaughter and Wei Wu and Yuankun Fu and Legend Brandenburg and Nicolai Garcia and Wilhem Kautz and Emily Marx and Kaleb S. Morris and Qinglei Cao and George Bosilca and Seema Mirchandaney and Wonchan Lee and Sean Treichler and Patrick McCormick and Alex Aiken}
}
@conference {1483,
	title = {Twenty Years of Computational Science},
	booktitle = {International Conference on Computational Science (ICCS 2020)},
	year = {2020},
	month = {2020-06},
	address = {Amsterdam, Netherlands},
	author = {Valeria Krzhizhanovskaya and G{\'a}bor Z{\'a}vodszky and Michael Lees and Jack Dongarra and Peter Sloot and S{\'e}rgio Brissos and Jo{\~a}o Teixeira}
}
@techreport {1400,
	title = {BDEC2 Platform White Paper},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-19-11},
	year = {2019},
	month = {2019-09},
	publisher = {University of Tennessee},
	author = {Todd Gamblin and Pete Beckman and Kate Keahey and Kento Sato and Masaaki Kondo and Gerofi Balazs}
}
@techreport {1433,
	title = {CEED ECP Milestone Report: Performance Tuning of CEED Software and 1st and 2nd Wave Apps},
	year = {2019},
	month = {2019-10},
	publisher = {Zenodo},
	doi = {https://doi.org/10.5281/zenodo.3477618},
	author = {Stanimire Tomov and Ahmad Abdelfattah and Valeria Barra and Natalie Beams and Jed Brown and Jean-Sylvain Camier and Veselin Dobrev and Jack Dongarra and Yohann Dudouit and Paul Fischer and Ali Karakus and Stefan Kerkemeier and Tzanio Kolev and YuHsiang Lan and Elia Merzari and Misun Min and Aleks Obabko and Scott Parker and Thilina Ratnayaka and Jeremy Thompson and Ananias Tomboulides and Vladimir Tomov and Tim Warburton}
}
@techreport {1434,
	title = {CEED ECP Milestone Report: Public release of CEED 2.0},
	year = {2019},
	month = {2019-04},
	publisher = {Zenodo},
	doi = {10.5281/zenodo.2641316},
	url = {https://doi.org/10.5281/zenodo.2641316},
	author = {Jed Brown and Ahmad Abdelfattah and Valeria Barra and Veselin Dobrev and Yohann Dudouit and Paul Fischer and Tzanio Kolev and David Medina and Misun Min and Thilina Ratnayaka and Cameron Smith and Jeremy Thompson and Stanimire Tomov and Vladimir Tomov and Tim Warburton}
}
@techreport {1398,
	title = {A Collection of Presentations from the BDEC2 Workshop in Kobe, Japan},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-19-09},
	year = {2019},
	month = {2019-02},
	publisher = {University of Tennessee, Knoxville},
	author = {Rosa M. Badia and Micah Beck and Fran{\c c}ois Bodin and Taisuke Boku and Franck Cappello and Alok Choudhary and Carlos Costa and Ewa Deelman and Nicola Ferrier and Katsuki Fujisawa and Kohei Fujita and Maria Girone and Geoffrey Fox and Shantenu Jha and Yoshinari Kameda and Christian Kniep and William Kramer and James Lin and Kengo Nakajima and Yiwei Qiu and Kishore Ramachandran and Glenn Ricart and Kim Serradell and Dan Stanzione and Lin Gan and Martin Swany and Christine Sweeney and Alex Szalay and Christine Kirkpatrick and Kenton McHenry and Alainna White and Steve Tuecke and Ian Foster and Joe Mambretti and William. M Tang and Michela Taufer and Miguel V{\'a}zquez}
}
@techreport {1399,
	title = {A Collection of White Papers from the BDEC2 Workshop in Poznan, Poland},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-19-10},
	year = {2019},
	month = {2019-05},
	publisher = {University of Tennessee, Knoxville},
	author = {Gabriel Antoniu and Alexandru Costan and Ovidiu Marcu and Maria S. P{\'e}rez and Nenad Stojanovic and Rosa M. Badia and Miguel V{\'a}zquez and Sergi Girona and Micah Beck and Terry Moore and Piotr Luszczek and Ezra Kissel and Martin Swany and Geoffrey Fox and Vibhatha Abeykoon and Selahattin Akkas and Kannan Govindarajan and Gurhan Gunduz and Supun Kamburugamuve and Niranda Perera and Ahmet Uyar and Pulasthi Wickramasinghe and Chathura Widanage and Maria Girone and Toshihiro Hanawa and Richard Moreno and Ariel Oleksiak and Martin Swany and Ryousei Takano and M.P. van Haarlem and J. van Leeuwen and J.B.R. Oonk and T. Shimwell and L.V.E. Koopmans}
}
@techreport {1408,
	title = {A Collection of White Papers from the BDEC2 Workshop in San Diego, CA},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-19-13},
	year = {2019},
	month = {2019-10},
	publisher = {University of Tennessee},
	author = {Ilkay Altintas and Kyle Marcus and Volkan Vural and Shweta Purawat and Daniel Crawl and Gabriel Antoniu and Alexandru Costan and Ovidiu Marcu and Prasanna Balaprakash and Rongqiang Cao and Yangang Wang and Franck Cappello and Robert Underwood and Sheng Di and Justin M. Wozniak and Jon C. Calhoun and Cong Xu and Antonio Lain and Paolo Faraboschi and Nic Dube and Dejan Milojicic and Balazs Gerofi and Maria Girone and Viktor Khristenko and Tony Hey and Erza Kissel and Yu Liu and Richard Loft and Pekka Manninen and Sebastian von Alfthan and Takemasa Miyoshi and Bruno Raffin and Olivier Richard and Denis Trystram and Maryam Rahnemoonfar and Robin Murphy and Joel Saltz and Kentaro Sano and Rupak Roy and Kento Sato and Jian Guo and Jen s Domke and Weikuan Yu and Takaki Hatsui and Yasumasa Joti and Alex Szalay and William M. Tang and Michael R. Wyatt II and Michela Taufer and Todd Gamblin and Stephen Herbein and Adam Moody and Dong H. Ahn and Rich Wolski and Chandra Krintz and Fatih Bakir and Wei-tsung Lin and Gareth George}
}
@article {1238,
	title = {Computing Dense Tensor Decompositions with Optimal Dimension Trees},
	journal = {Algorithmica},
	volume = {81},
	year = {2019},
	month = {2019-05},
	pages = {2092{\textendash}2121},
	keywords = {CP decomposition, Dimension tree, Tensor computations, Tucker decomposition},
	issn = {1432-0541},
	doi = {https://doi.org/10.1007/s00453-018-0525-3},
	author = {Oguz Kaya and Yves Robert}
}
@conference {1439,
	title = {Data Logistics: Toolkit and Applications},
	booktitle = {5th EAI International Conference on Smart Objects and Technologies for Social Good},
	year = {2019},
	month = {2019-09},
	address = {Valencia, Spain},
	author = {Micah Beck and Terry Moore and Nancy French and Erza Kissel and Martin Swany}
}
@techreport {1396,
	title = {An Empirical View of SLATE Algorithms on Scalable Hybrid System},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-19-08},
	year = {2019},
	month = {2019-09},
	publisher = {University of Tennessee, Knoxville},
	author = {Asim YarKhan and Jakub Kurzak and Ahmad Abdelfattah and Jack Dongarra}
}
@inproceedings {1404,
	title = {Least Squares Solvers for Distributed-Memory Machines with GPU Accelerators},
	journal = {ACM International Conference on Supercomputing (ICS {\textquoteright}19)},
	year = {2019},
	month = {2019-06},
	pages = {117{\textendash}126},
	publisher = {ACM},
	address = {Phoenix, Arizona},
	isbn = {9781450360791},
	doi = {https://dl.acm.org/doi/abs/10.1145/3330345.3330356},
	author = {Jakub Kurzak and Mark Gates and Ali Charara and Asim YarKhan and Jack Dongarra}
}
@inproceedings {1405,
	title = {Linear Systems Solvers for Distributed-Memory Machines with GPU Accelerators},
	journal = {Euro-Par 2019: Parallel Processing},
	volume = {11725},
	year = {2019},
	month = {2019-08},
	pages = {495{\textendash}506},
	publisher = {Springer},
	isbn = {978-3-030-29399-4},
	doi = {https://doi.org/10.1007/978-3-030-29400-7_35},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-29400-7_35},
	author = {Kurzak, Jakub and Mark Gates and Charara, Ali and Asim YarKhan and Yamazaki, Ichitaro and Jack Dongarra},
	editor = {Yahyapour, Ramin}
}
@conference {1373,
	title = {Massively Parallel Automated Software Tuning},
	booktitle = {48th International Conference on Parallel Processing (ICPP 2019)},
	year = {2019},
	month = {2019-08},
	publisher = {ACM Press},
	organization = {ACM Press},
	address = {Kyoto, Japan},
	abstract = {This article presents an implementation of a distributed autotuning engine developed as part of the Bench-testing OpenN Software Autotuning Infrastructure project. The system is geared towards performance optimization of computational kernels for graphics processing units, and allows for the deployment of vast autotuning sweeps to massively parallel machines. The software implements dynamic work scheduling to distributed-memory resources and takes advantage of multithreading for parallel compilation and dispatches kernel launches to multiple accelerators. This paper lays out the main design principles of the system and discusses the basic mechanics of the initial implementation. Preliminary performance results are presented, encountered challenges are discussed, and the future directions are outlined.},
	doi = {https://doi.org/10.1145/3337821.3337908},
	author = {Jakub Kurzak and Yaohung Tsai and Mark Gates and Ahmad Abdelfattah and Jack Dongarra}
}
@conference {1452,
	title = {Performance Analysis of Tile Low-Rank Cholesky Factorization Using PaRSEC Instrumentation Tools},
	booktitle = {Workshop on Programming and Performance Visualization Tools (ProTools 19) at SC19},
	year = {2019},
	month = {2019-11},
	publisher = {ACM},
	organization = {ACM},
	address = {Denver, CO},
	author = {Qinglei Cao and Yu Pei and Thomas Herault and Kadir Akbudak and Aleksandr Mikhalev and George Bosilca and Hatem Ltaief and David Keyes and Jack Dongarra}
}
@article {1270,
	title = {PLASMA: Parallel Linear Algebra Software for Multicore Using OpenMP},
	journal = {ACM Transactions on Mathematical Software},
	volume = {45},
	year = {2019},
	month = {2019-06},
	doi = {https://doi.org/10.1145/3264491},
	author = {Jack Dongarra and Mark Gates and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Panruo Wu and Ichitaro Yamazaki and Asim YarKhan and Maksims Abalenkovs and Negin Bagherpour and Sven Hammarling and Jakub Sistek}
}
@article {1440,
	title = {Race to Exascale},
	journal = {Computing in Science and Engineering},
	volume = {21},
	year = {2019},
	month = {2019-03},
	pages = {4-5},
	abstract = {Whether called leadership computing, flagship computing, or just plain exascale, over the next few years, governments around the world are planning to spend over 10 billion dollars on a handful of new computer systems that will strive to reach an exascale level of performance. These systems and projects reflect the widespread and expanding recognition that almost all science and engineering endeavors now are intrinsically reliant on computing power not just for modeling and simulation but for data analysis, big data, and machine learning. Scientists and engineers consider computers as {\textquotedblleft}universal instruments{\textquotedblright} of insight.},
	issn = {1558-366X},
	doi = {https://doi.org/10.1109/MCSE.2018.2882574},
	author = {Jack Dongarra and Steven Gottlieb and William T. Kramer}
}
@article {1463,
	title = {SLATE: Design of a Modern Distributed and Accelerated Linear Algebra Library},
	year = {2019},
	month = {2019-11},
	publisher = {International Conference for High Performance Computing, Networking, Storage and Analysis (SC19)},
	address = {Denver, CO},
	author = {Mark Gates and Jakub Kurzak and Ali Charara and Asim YarKhan and Jack Dongarra}
}
@conference {1450,
	title = {SLATE: Design of a Modern Distributed and Accelerated Linear Algebra Library},
	booktitle = {International Conference for High Performance Computing, Networking, Storage and Analysis (SC19)},
	year = {2019},
	month = {2019-11},
	publisher = {ACM},
	organization = {ACM},
	address = {Denver, CO},
	abstract = {The SLATE (Software for Linear Algebra Targeting Exascale) library is being developed to provide fundamental dense linear algebra capabilities for current and upcoming distributed high-performance systems, both accelerated CPU-GPU based and CPU based. SLATE will provide coverage of existing ScaLAPACK functionality, including the parallel BLAS; linear systems using LU and Cholesky; least squares problems using QR; and eigenvalue and singular value problems. In this respect, it will serve as a replacement for ScaLAPACK, which after two decades of operation, cannot adequately be retrofitted for modern accelerated architectures. SLATE uses modern techniques such as communication-avoiding algorithms, lookahead panels to overlap communication and computation, and task-based scheduling, along with a modern C++ framework. Here we present the design of SLATE and initial reports of several of its components.},
	doi = {https://doi.org/10.1145/3295500.3356223},
	author = {Mark Gates and Jakub Kurzak and Ali Charara and Asim YarKhan and Jack Dongarra}
}
@techreport {1279,
	title = {SLATE Developers{\textquoteright} Guide},
	journal = {SLATE Working Notes},
	number = {11, ICL-UT-19-02},
	year = {2019},
	month = {2019-12},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	type = {SLATE Working Notes},
	author = {Ali Charara and Mark Gates and Jakub Kurzak and Asim YarKhan and Jack Dongarra}
}
@techreport {1304,
	title = {SLATE Mixed Precision Performance Report},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-19-03},
	year = {2019},
	month = {2019-04},
	publisher = {University of Tennessee},
	author = {Ali Charara and Jack Dongarra and Mark Gates and Jakub Kurzak and Asim YarKhan}
}
@techreport {1321,
	title = {SLATE Working Note 12: Implementing Matrix Inversions},
	journal = {SLATE Working Notes},
	number = {12, ICL-UT-19-04},
	year = {2019},
	month = {2019-06},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	author = {Jakub Kurzak and Mark Gates and Ali Charara and Asim YarKhan and Jack Dongarra}
}
@techreport {1394,
	title = {SLATE Working Note 13: Implementing Singular Value and Symmetric/Hermitian Eigenvalue Solvers},
	journal = {SLATE Working Notes},
	number = {13, ICL-UT-19-07},
	year = {2019},
	note = {revision 06-2023},
	month = {2019-09},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	type = {SLATE Working Notes},
	author = {Mark Gates and Mohammed Al Farhan and Ali Charara and Jakub Kurzak and Dalal Sukkari and Asim YarKhan and Jack Dongarra}
}
@article {1438,
	title = {Towards a New Peer Review Concept for Scientific Computing ensuring Technical Quality, Software Sustainability, and Result Reproducibility},
	journal = {Proceedings in Applied Mathematics and Mechanics},
	volume = {19},
	year = {2019},
	month = {2019-11},
	abstract = {In this position paper we argue for implementing an alternative peer review process for scientific computing contributions that promotes high quality scientific software developments as fully-recognized conference submission. The idea is based on leveraging the code reviewers{\textquoteright} feedback on scientific software contributions to community software developments as a third-party review involvement. Providing open access to this technical review would complement the scientific review of the contribution, efficiently reduce the workload of the undisclosed reviewers, improve the algorithm implementation quality and software sustainability, and ensure full reproducibility of the reported results. Using this process creates incentives to publish scientific algorithms in open source software {\textendash} instead of designing prototype algorithms with the unique purpose of publishing a paper. In addition, the comments and suggestions of the community being archived in the versioning control systems ensure that also community reviewers are receiving credit for the review contributions {\textendash} unlike reviewers in the traditional peer review process. Finally, it reflects the particularity of the scientific computing community using conferences rather than journals as the main publication venue.},
	issn = {1617-7061},
	doi = {https://doi.org/10.1002/pamm.201900490},
	author = {Hartwig Anzt and Terry Cojean and Eileen Kuhn}
}
@article {1271,
	title = {Autotuning Numerical Dense Linear Algebra for Batched Computation With GPU Hardware Accelerators},
	journal = {Proceedings of the IEEE},
	volume = {106},
	year = {2018},
	month = {2018-11},
	pages = {2040{\textendash}2055},
	abstract = {Computational problems in engineering and scientific disciplines often rely on the solution of many instances of small systems of linear equations, which are called batched solves. In this paper, we focus on the important variants of both batch Cholesky factorization and subsequent substitution. The former requires the linear system matrices to be symmetric positive definite (SPD). We describe the implementation and automated performance engineering of these kernels that implement the factorization and the two substitutions. Our target platforms are graphics processing units (GPUs), which over the past decade have become an attractive high-performance computing (HPC) target for solvers of linear systems of equations. Due to their throughput-oriented design, GPUs exhibit the highest processing rates among the available processors. However, without careful design and coding, this speed is mostly restricted to large matrix sizes. We show an automated exploration of the implementation space as well as a new data layout for the batched class of SPD solvers. Our tests involve the solution of many thousands of linear SPD systems of exactly the same size. The primary focus of our techniques is on the individual matrices in the batch that have dimensions ranging from 5-by-5 up to 100-by-100. We compare our autotuned solvers against the state-of-the-art solvers such as those provided through NVIDIA channels and publicly available in the optimized MAGMA library. The observed performance is competitive and many times superior for many practical cases. The advantage of the presented methodology lies in achieving these results in a portable manner across matrix storage formats and GPU hardware architecture platforms.},
	keywords = {Dense numerical linear algebra, performance autotuning},
	doi = {10.1109/JPROC.2018.2868961},
	author = {Jack Dongarra and Mark Gates and Jakub Kurzak and Piotr Luszczek and Yaohung Tsai}
}
@article {1277,
	title = {Autotuning Techniques for Performance-Portable Point Set Registration in 3D},
	journal = {Supercomputing Frontiers and Innovations},
	volume = {5},
	number = {4},
	year = {2018},
	month = {2018-12},
	chapter = {42},
	abstract = {We present an autotuning approach applied to exhaustive performance engineering of the EM-ICP algorithm for the point set registration problem with a known reference. We were able to achieve progressively higher performance levels through a variety of code transformations and an automated procedure of generating a large number of implementation variants. Furthermore, we managed to exploit code patterns that are not common when only attempting manual optimization but which yielded in our tests better performance for the chosen registration algorithm. Finally, we also show how we maintained high levels of the performance rate in a portable fashion across a wide range of hardware platforms including multicore, manycore coprocessors, and accelerators. Each of these hardware classes is much different from the others and, consequently, cannot reliably be mastered by a single developer in a short time required to deliver a close-to-optimal implementation. We assert in our concluding remarks that our methodology as well as the presented tools provide a valid automation system for software optimization tasks on modern HPC hardware.},
	doi = {10.14529/jsfi180404},
	author = {Piotr Luszczek and Jakub Kurzak and Ichitaro Yamazaki and David Keffer and Vasileios Maroulas and Jack Dongarra}
}
@article {1300,
	title = {Batched BLAS (Basic Linear Algebra Subprograms) 2018 Specification},
	year = {2018},
	month = {2018-07},
	abstract = {This document describes an API for Batch Basic Linear Algebra Subprograms (Batched BLAS or BBLAS). We focus on many independent BLAS operations on small matrices that are grouped together and processed by a single routine, called a Batched BLAS routine. The extensions beyond the original BLAS standard are considered that specify a programming interface not only for routines with uniformly-sized matrices and/or vectors but also for the situation where the sizes vary. The aim is to provide more efficient, but portable, implementations of algorithms on high-performance manycore platforms. These include multicore and many-core CPU processors; GPUs and coprocessors; as well as other hardware accelerators with floating-point compute facility.},
	author = {Jack Dongarra and Iain Duff and Mark Gates and Azzam Haidar and Sven Hammarling and Nicholas J. Higham and Jonathan Hogg and Pedro Valero Lara and Piotr Luszczek and Mawussi Zounon and Samuel D. Relton and Stanimire Tomov and Timothy Costa and Sarah Knepper}
}
@article {1211,
	title = {Big Data and Extreme-Scale Computing: Pathways to Convergence - Toward a Shaping Strategy for a Future Software and Data Ecosystem for Scientific Inquiry},
	journal = {The International Journal of High Performance Computing Applications},
	volume = {32},
	year = {2018},
	month = {2018-07},
	pages = {435{\textendash}479},
	abstract = {Over the past four years, the Big Data and Exascale Computing (BDEC) project organized a series of five international workshops that aimed to explore the ways in which the new forms of data-centric discovery introduced by the ongoing revolution in high-end data analysis (HDA) might be integrated with the established, simulation-centric paradigm of the high-performance computing (HPC) community. Based on those meetings, we argue that the rapid proliferation of digital data generators, the unprecedented growth in the volume and diversity of the data they generate, and the intense evolution of the methods for analyzing and using that data are radically reshaping the landscape of scientific computing. The most critical problems involve the logistics of wide-area, multistage workflows that will move back and forth across the computing continuum, between the multitude of distributed sensors, instruments and other devices at the networks edge, and the centralized resources of commercial clouds and HPC centers. We suggest that the prospects for the future integration of technological infrastructures and research ecosystems need to be considered at three different levels. First, we discuss the convergence of research applications and workflows that establish a research paradigm that combines both HPC and HDA, where ongoing progress is already motivating efforts at the other two levels. Second, we offer an account of some of the problems involved with creating a converged infrastructure for peripheral environments, that is, a shared infrastructure that can be deployed throughout the network in a scalable manner to meet the highly diverse requirements for processing, communication, and buffering/storage of massive data workflows of many different scientific domains. Third, we focus on some opportunities for software ecosystem convergence in big, logically centralized facilities that execute large-scale simulations and models and/or perform large-scale data analytics. We close by offering some conclusions and recommendations for future investment and policy review.},
	doi = {https://doi.org/10.1177/1094342018778123},
	author = {Mark Asch and Terry Moore and Rosa M. Badia and Micah Beck and Pete Beckman and Thierry Bidot and Fran{\c c}ois Bodin and Franck Cappello and Alok Choudhary and Bronis R. de Supinski and Ewa Deelman and Jack Dongarra and Anshu Dubey and Geoffrey Fox and Haohuan Fu and Sergi Girona and Michael Heroux and Yutaka Ishikawa and Kate Keahey and David Keyes and William T. Kramer and Jean-Fran{\c c}ois Lavignon and Yutong Lu and Satoshi Matsuoka and Bernd Mohr and St{\'e}phane Requena and Joel Saltz and Thomas Schulthess and Rick Stevens and Martin Swany and Alexander Szalay and William Tang and Ga{\"e}l Varoquaux and Jean-Pierre Vilotte and Robert W. Wisniewski and Zhiwei Xu and Igor Zacharov}
}
@techreport {1397,
	title = {A Collection of White Papers from the BDEC2 Workshop in Bloomington, IN},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-18-15},
	year = {2018},
	month = {2018-11},
	publisher = {University of Tennessee, Knoxville},
	author = {James Ahrens and Christopher M. Biwer and Alexandru Costan and Gabriel Antoniu and Maria S. P{\'e}rez and Nenad Stojanovic and Rosa Badia and Oliver Beckstein and Geoffrey Fox and Shantenu Jha and Micah Beck and Terry Moore and Sunita Chandrasekaran and Carlos Costa and Thierry Deutsch and Luigi Genovese and Tarek El-Ghazawi and Ian Foster and Dennis Gannon and Toshihiro Hanawa and Tevfik Kosar and William Kramer and Madhav V. Marathe and Christopher L. Barrett and Takemasa Miyoshi and Alex Pothen and Ariful Azad and Judy Qiu and Bo Peng and Ravi Teja and Sahil Tyagi and Chathura Widanage and Jon Koskey and Maryam Rahnemoonfar and Umakishore Ramachandran and Miles Deegan and William Tang and Osamu Tatebe and Michela Taufer and Michel Cuende and Ewa Deelman and Trilce Estrada and Rafael Ferreira Da Silva and Harrel Weinstein and Rodrigo Vargas and Miwako Tsuji and Kevin G. Yager and Wanling Gao and Jianfeng Zhan and Lei Wang and Chunjie Luo and Daoyi Zheng and Xu Wen and Rui Ren and Chen Zheng and Xiwen He and Hainan Ye and Haoning Tang and Zheng Cao and Shujie Zhang and Jiahui Dai}
}
@techreport {1203,
	title = {Implementation of the C++ API for Batch BLAS},
	journal = {SLATE Working Notes},
	number = {07, ICL-UT-18-04},
	year = {2018},
	month = {2018-06},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	author = {Ahmad Abdelfattah and Mark Gates and Jakub Kurzak and Piotr Luszczek and Jack Dongarra}
}
@techreport {1273,
	title = {Least Squares Performance Report},
	journal = {SLATE Working Notes},
	number = {09, ICL-UT-18-10},
	year = {2018},
	month = {2018-12},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	type = {SLATE Working Notes},
	author = {Mark Gates and Ali Charara and Jakub Kurzak and Asim YarKhan and Ichitaro Yamazaki and Jack Dongarra}
}
@techreport {1228,
	title = {Linear Systems Performance Report},
	journal = {SLATE Working Notes},
	number = {08, ICL-UT-18-08},
	year = {2018},
	month = {2018-09},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	type = {SLATE Working Notes},
	author = {Jakub Kurzak and Mark Gates and Ichitaro Yamazaki and Ali Charara and Asim YarKhan and Jamie Finney and Gerald Ragghianti and Piotr Luszczek and Jack Dongarra}
}
@article {1219,
	title = {Optimization and Performance Evaluation of the IDR Iterative Krylov Solver on GPUs},
	journal = {The International Journal of High Performance Computing Applications},
	volume = {32},
	number = {2},
	year = {2018},
	month = {2018-03},
	pages = {220{\textendash}230},
	abstract = {In this paper, we present an optimized GPU implementation for the induced dimension reduction algorithm. We improve data locality, combine it with an efficient sparse matrix vector kernel, and investigate the potential of overlapping computation with communication as well as the possibility of concurrent kernel execution. A comprehensive performance evaluation is conducted using a suitable performance model. The analysis reveals efficiency of up to 90\%, which indicates that the implementation achieves performance close to the theoretically attainable bound.},
	keywords = {co-design, gpu, Induced dimension reduction (IDR), kernel fusion, kernel overlap, roofline performance model},
	doi = {https://doi.org/10.1177/1094342016646844},
	author = {Hartwig Anzt and Moritz Kreutzer and Eduardo Ponce and Gregory D. Peterson and Gerhard Wellein and Jack Dongarra}
}
@techreport {1191,
	title = {Parallel BLAS Performance Report},
	journal = {SLATE Working Notes},
	number = {05, ICL-UT-18-01},
	year = {2018},
	month = {2018-04},
	publisher = {University of Tennessee},
	author = {Jakub Kurzak and Mark Gates and Asim YarKhan and Ichitaro Yamazaki and Panruo Wu and Piotr Luszczek and Jamie Finney and Jack Dongarra}
}
@techreport {1206,
	title = {Parallel Norms Performance Report},
	journal = {SLATE Working Notes},
	number = {06, ICL-UT-18-06},
	year = {2018},
	month = {2018-06},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	author = {Jakub Kurzak and Mark Gates and Asim YarKhan and Ichitaro Yamazaki and Piotr Luszczek and Jamie Finney and Jack Dongarra}
}
@article {1258,
	title = {The Singular Value Decomposition: Anatomy of Optimizing an Algorithm for Extreme Scale},
	journal = {SIAM Review},
	volume = {60},
	year = {2018},
	month = {2018-11},
	pages = {808{\textendash}865},
	abstract = {The computation of the singular value decomposition, or SVD, has a long history with many improvements over the years, both in its implementations and algorithmically. Here, we survey the evolution of SVD algorithms for dense matrices, discussing the motivation and performance impacts of changes. There are two main branches of dense SVD methods: bidiagonalization and Jacobi. Bidiagonalization methods started with the implementation by Golub and Reinsch in Algol60, which was subsequently ported to Fortran in the EISPACK library, and was later more efficiently implemented in the LINPACK library, targeting contemporary vector machines. To address cache-based memory hierarchies, the SVD algorithm was reformulated to use Level 3 BLAS in the LAPACK library. To address new architectures, ScaLAPACK was introduced to take advantage of distributed computing, and MAGMA was developed for accelerators such as GPUs. Algorithmically, the divide and conquer and MRRR algorithms were developed to reduce the number of operations. Still, these methods remained memory bound, so two-stage algorithms were developed to reduce memory operations and increase the computational intensity, with efficient implementations in PLASMA, DPLASMA, and MAGMA. Jacobi methods started with the two-sided method of Kogbetliantz and the one-sided method of Hestenes. They have likewise had many developments, including parallel and block versions and preconditioning to improve convergence. In this paper, we investigate the impact of these changes by testing various historical and current implementations on a common, modern multicore machine and a distributed computing platform. We show that algorithmic and implementation improvements have increased the speed of the SVD by several orders of magnitude, while using up to 40 times less energy.},
	keywords = {bidiagonal matrix, bisection, Divide and conquer, Hestenes method, Jacobi method, Kogbetliantz method, MRRR, QR iteration, Singular value decomposition, SVD},
	issn = {0036-1445},
	doi = {10.1137/17M1117732},
	url = {https://epubs.siam.org/doi/10.1137/17M1117732},
	author = {Jack Dongarra and Mark Gates and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov and Ichitaro Yamazaki}
}
@article {1189,
	title = {Symmetric Indefinite Linear Solver using OpenMP Task on Multicore Architectures},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	volume = {29},
	year = {2018},
	month = {2018-08},
	pages = {1879{\textendash}1892},
	abstract = {Recently, the Open Multi-Processing (OpenMP) standard has incorporated task-based programming, where a function call with input and output data is treated as a task. At run time, OpenMP{\textquoteright}s superscalar scheduler tracks the data dependencies among the tasks and executes the tasks as their dependencies are resolved. On a shared-memory architecture with multiple cores, the independent tasks are executed on different cores in parallel, thereby enabling parallel execution of a seemingly sequential code. With the emergence of many-core architectures, this type of programming paradigm is gaining attention-not only because of its simplicity, but also because it breaks the artificial synchronization points of the program and improves its thread-level parallelization. In this paper, we use these new OpenMP features to develop a portable high-performance implementation of a dense symmetric indefinite linear solver. Obtaining high performance from this kind of solver is a challenge because the symmetric pivoting, which is required to maintain numerical stability, leads to data dependencies that prevent us from using some common performance-improving techniques. To fully utilize a large number of cores through tasking, while conforming to the OpenMP standard, we describe several techniques. Our performance results on current many-core architectures-including Intel{\textquoteright}s Broadwell, Intel{\textquoteright}s Knights Landing, IBM{\textquoteright}s Power8, and Arm{\textquoteright}s ARMv8-demonstrate the portable and superior performance of our implementation compared with the Linear Algebra PACKage (LAPACK). The resulting solver is now available as a part of the PLASMA software package.},
	keywords = {linear algebra, multithreading, runtime, symmetric indefinite matrices},
	doi = {10.1109/TPDS.2018.2808964},
	author = {Ichitaro Yamazaki and Jakub Kurzak and Panruo Wu and Mawussi Zounon and Jack Dongarra}
}
@article {1448,
	title = {Task Based Cholesky Decomposition on Xeon Phi Architectures using OpenMP},
	journal = {International Journal of Computational Science and Engineering (IJCSE)},
	volume = {17},
	number = {3},
	year = {2018},
	month = {2018-10},
	abstract = {The increasing number of computational cores in modern many-core processors, as represented by the Intel Xeon Phi architectures, has created the need for an open-source, high performance and scalable task-based dense linear algebra package that can efficiently use this type of many-core hardware. In this paper, we examined the design modifications necessary when porting PLASMA, a task-based dense linear algebra library, run effectively on two generations of Intel{\textquoteright}s Xeon Phi architecture, known as knights corner (KNC) and knights landing (KNL). First, we modified PLASMA{\textquoteright}s tiled Cholesky decomposition to use OpenMP tasks for its scheduling mechanism to enable Xeon Phi compatibility. We then compared the performance of our modified code to that of the original dynamic scheduler running on an Intel Xeon Sandy Bridge CPU. Finally, we looked at the performance of the OpenMP tiled Cholesky decomposition on knights corner and knights landing processors. We detail the optimisations required to obtain performance on these platforms and compare with the highly tuned Intel MKL math library.},
	doi = {http://dx.doi.org/10.1504/IJCSE.2018.095851},
	author = {Joseph Dorris and Asim YarKhan and Jakub Kurzak and Piotr Luszczek and Jack Dongarra}
}
@article {1341,
	title = {Accelerating Tensor Contractions in High-Order FEM with MAGMA Batched},
	year = {2017},
	month = {2017-03},
	publisher = {SIAM Conference on Computer Science and Engineering (SIAM CSE17), Presentation},
	address = {Atlanta, GA},
	author = {Ahmad Abdelfattah and Marc Baboulin and Veselin Dobrev and Jack Dongarra and Christopher Earl and Jo{\"e}l Falcou and Azzam Haidar and Ian Karlin and Tzanio Kolev and Ian Masliah and Stanimire Tomov}
}
@article {1176,
	title = {Argobots: A Lightweight Low-Level Threading and Tasking Framework},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	year = {2017},
	month = {2017-10},
	abstract = {In the past few decades, a number of user-level threading and tasking models have been proposed in the literature to address the shortcomings of OS-level threads, primarily with respect to cost and flexibility. Current state-of-the-art user-level threading and tasking models, however, are either too specific to applications or architectures or are not as powerful or flexible. In this paper, we present Argobots, a lightweight, low-level threading and tasking framework that is designed as a portable and performant substrate for high-level programming models or runtime systems. Argobots offers a carefully designed execution model that balances generality of functionality with providing a rich set of controls to allow specialization by the user or high-level programming model. We describe the design, implementation, and optimization of Argobots and present integrations with three example high-level models: OpenMP, MPI, and co-located I/O service. Evaluations show that (1) Argobots outperforms existing generic threading runtimes; (2) our OpenMP runtime offers more efficient interoperability capabilities than production OpenMP runtimes do; (3) when MPI interoperates with Argobots instead of Pthreads, it enjoys reduced synchronization costs and better latency hiding capabilities; and (4) I/O service with Argobots reduces interference with co-located applications, achieving performance competitive with that of the Pthreads version.},
	keywords = {Argobots, context switch, I/O, interoperability, lightweight, MPI, OpenMP, stackable scheduler, tasklet, user-level thread},
	doi = {10.1109/TPDS.2017.2766062},
	url = {http://ieeexplore.ieee.org/document/8082139/},
	author = {Sangmin Seo and Abdelhalim Amer and Pavan Balaji and Cyril Bordage and George Bosilca and Alex Brooks and Philip Carns and Adrian Castello and Damien Genet and Thomas Herault and Shintaro Iwasaki and Prateek Jindal and Sanjay Kale and Sriram Krishnamoorthy and Jonathan Lifflander and Huiwei Lu and Esteban Meneses and Mar Snir and Yanhua Sun and Kenjiro Taura and Pete Beckman}
}
@conference {1169,
	title = {Autotuning Batch Cholesky Factorization in CUDA with Interleaved Layout of Matrices},
	booktitle = {Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
	year = {2017},
	month = {2017-06},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Orlando, FL},
	abstract = {Batch matrix operations address the case of solving the same linear algebra problem for a very large number of very small matrices. In this paper, we focus on implementing the batch Cholesky factorization in CUDA, in single precision arithmetic, for NVIDIA GPUs. Specifically, we look into the benefits of using noncanonical data layouts, where consecutive memory locations store elements with the same row and column index in a set of consecutive matrices. We discuss a number of different implementation options and tuning parameters. We demonstrate superior performance to traditional implementations for the case of very small matrices.},
	keywords = {batch computation, Cholesky Factorization, data layout, GPU computing, numerical linear algebra},
	doi = {10.1109/IPDPSW.2017.18},
	author = {Mark Gates and Jakub Kurzak and Piotr Luszczek and Yu Pei and Jack Dongarra}
}
@inbook {1167,
	title = {Bringing High Performance Computing to Big Data Algorithms},
	booktitle = {Handbook of Big Data Technologies},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	isbn = {978-3-319-49339-8},
	doi = {10.1007/978-3-319-49340-4},
	author = {Hartwig Anzt and Jack Dongarra and Mark Gates and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov and Ichitaro Yamazaki}
}
@techreport {1175,
	title = {C++ API for Batch BLAS},
	journal = {SLATE Working Notes},
	number = {04, ICL-UT-17-12},
	year = {2017},
	month = {2017-12},
	publisher = {University of Tennessee},
	author = {Ahmad Abdelfattah and Konstantin Arturov and Cris Cecka and Jack Dongarra and Chip Freitag and Mark Gates and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov and Panruo Wu}
}
@techreport {1081,
	title = {C++ API for BLAS and LAPACK},
	journal = {SLATE Working Notes},
	number = {02, ICL-UT-17-03},
	year = {2017},
	note = {Revision 02-21-2018},
	month = {2017-06},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	author = {Mark Gates and Piotr Luszczek and Ahmad Abdelfattah and Jakub Kurzak and Jack Dongarra and Konstantin Arturov and Cris Cecka and Chip Freitag}
}
@techreport {1138,
	title = {The Case for Directive Programming for Accelerator Autotuner Optimization},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-17-07},
	year = {2017},
	month = {2017-10},
	publisher = {University of Tennessee},
	abstract = {In this work, we present the use of compiler pragma directives for parallelizing autotuning of specialized compute kernels for hardware accelerators. A set of constructs, that include prallelizing a source code that prune a generated search space with a large number of constraints for an autotunning infrastructure. For a better performance we studied optimization aimed at minimization of the run time.We also studied the behavior of the parallel load balance and the speedup on four different machines: x86, Xeon Phi, ARMv8, and POWER8.},
	author = {Diana Fayad and Jakub Kurzak and Piotr Luszczek and Panruo Wu and Jack Dongarra}
}
@article {1165,
	title = {Design and Implementation of the PULSAR Programming System for Large Scale Computing},
	journal = {Supercomputing Frontiers and Innovations},
	volume = {4},
	year = {2017},
	abstract = {The objective of the PULSAR project was to design a programming model suitable for large scale machines with complex memory hierarchies, and to deliver a prototype implementation of a runtime system supporting that model. PULSAR tackled the challenge by proposing a programming model based on systolic processing and virtualization. The PULSAR programming model is quite simple, with point-to-point channels as the main communication abstraction. The runtime implementation is very lightweight and fully distributed, and provides multithreading, message-passing and multi-GPU offload capabilities. Performance evaluation shows good scalability up to one thousand nodes with one thousand GPU accelerators.},
	doi = {10.14529/jsfi170101},
	url = {http://superfri.org/superfri/article/view/121/210},
	author = {Jakub Kurzak and Piotr Luszczek and Ichitaro Yamazaki and Yves Robert and Jack Dongarra}
}
@techreport {1133,
	title = {Designing SLATE: Software for Linear Algebra Targeting Exascale},
	journal = {SLATE Working Notes},
	number = {03, ICL-UT-17-06},
	year = {2017},
	month = {2017-10},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	type = {SLATE Working Notes},
	author = {Jakub Kurzak and Panruo Wu and Mark Gates and Ichitaro Yamazaki and Piotr Luszczek and Gerald Ragghianti and Jack Dongarra}
}
@article {1083,
	title = {A Framework for Out of Memory SVD Algorithms},
	journal = {ISC High Performance 2017},
	year = {2017},
	month = {2017-06},
	pages = {158{\textendash}178},
	abstract = {Many important applications {\textendash} from big data analytics to information retrieval, gene expression analysis, and numerical weather prediction {\textendash} require the solution of large dense singular value decompositions (SVD). In many cases the problems are too large to fit into the computer{\textquoteright}s main memory, and thus require specialized out-of-core algorithms that use disk storage. In this paper, we analyze the SVD communications, as related to hierarchical memories, and design a class of algorithms that minimizes them. This class includes out-of-core SVDs but can also be applied between other consecutive levels of the memory hierarchy, e.g., GPU SVD using the CPU memory for large problems. We call these out-of-memory (OOM) algorithms. To design OOM SVDs, we first study the communications for both classical one-stage blocked SVD and two-stage tiled SVD. We present the theoretical analysis and strategies to design, as well as implement, these communication avoiding OOM SVD algorithms. We show performance results for multicore architecture that illustrate our theoretical findings and match our performance models.},
	doi = {https://doi.org/10.1007/978-3-319-58667-0_9},
	author = {Khairul Kabir and Azzam Haidar and Stanimire Tomov and Aurelien Bouteiller and Jack Dongarra}
}
@article {1166,
	title = {A Look Back on 30 Years of the Gordon Bell Prize},
	journal = {International Journal of High Performance Computing and Networking},
	volume = {31},
	year = {2017},
	pages = {469{\textendash}484},
	abstract = {The Gordon Bell Prize is awarded each year by the Association for Computing Machinery to recognize outstanding achievement in high-performance computing (HPC). The purpose of the award is to track the progress of parallel computing with particular emphasis on rewarding innovation in applying HPC to applications in science, engineering, and large-scale data analytics. Prizes may be awarded for peak performance or special achievements in scalability and time-to-solution on important science and engineering problems. Financial support for the US$10,000 award is provided through an endowment by Gordon Bell, a pioneer in high-performance and parallel computing. This article examines the evolution of the Gordon Bell Prize and the impact it has had on the field.},
	keywords = {benchmarks, Computational Science, Gordon Bell Prize, High Performance Computing, HPC Cost-Performance, HPC Progress, HPC Recognition, HPC special hardware, HPPC Award. HPC Prize, Technical Computing},
	url = {http://journals.sagepub.com/doi/10.1177/1094342017738610},
	author = {Gordon Bell and David Bailey and Alan H. Karp and Jack Dongarra and Kevin Walsh}
}
@techreport {1130,
	title = {MAGMA-sparse Interface Design Whitepaper},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-17-05},
	year = {2017},
	month = {2017-09},
	type = {Technical Report},
	abstract = {In this report we describe the logic and interface we develop for the MAGMA-sparse library
to allow for easy integration as third-party library into a top-level software ecosystem. The
design choices are based on extensive consultation with other software library developers, in
particular the Trilinos software development team. The interface documentation is at this point
not exhaustive, but a first proposal for setting a standard. Although the interface description
targets the MAGMA-sparse software module, we hope that the design choices carry beyond this
specific library, and are attractive for adoption in other packages.
This report is not intended as static document, but will be updated over time to reflect the agile
software development in the ECP 1.3.3.11 STMS11-PEEKS project.},
	author = {Hartwig Anzt and Erik Boman and Jack Dongarra and Goran Flegar and Mark Gates and Mike Heroux and Mark Hoemmen and Jakub Kurzak and Piotr Luszczek and Sivasankaran Rajamanickam and Stanimire Tomov and Stephen Wood and Ichitaro Yamazaki}
}
@conference {1141,
	title = {Out of Memory SVD Solver for Big Data},
	booktitle = {2017 IEEE High Performance Extreme Computing Conference (HPEC{\textquoteright}17)},
	year = {2017},
	month = {2017-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Waltham, MA},
	abstract = {Many applications {\textendash} from data compression to numerical weather prediction and information retrieval {\textendash} need to compute large dense singular value decompositions (SVD). When the problems are too large to fit into the computer{\textquoteright}s main memory, specialized out-of-core algorithms that use disk storage are required. A typical example is when trying to analyze a large data set through tools like MATLAB or Octave, but the data is just too large to be loaded. To overcome this, we designed a class of out-of-memory (OOM) algorithms to reduce, as well as overlap communication with computation. Of particular interest is OOM algorithms for matrices of size m{\texttimes}n, where m >> n or m << n, e.g., corresponding to cases of too many variables, or too many observations. To design OOM SVDs, we first study the communications cost for the SVD techniques as well as for the QR/LQ factorization followed by SVD. We present the theoretical analysis about the data movement cost and strategies to design OOM SVD algorithms. We show performance results for multicore architecture that illustrate our theoretical findings and match our performance models. Moreover, our experimental results show the feasibility and superiority of the OOM SVD.},
	author = {Azzam Haidar and Khairul Kabir and Diana Fayad and Stanimire Tomov and Jack Dongarra}
}
@inbook {1384,
	title = {Performance Analysis and Debugging Tools at Scale},
	booktitle = {Exascale Scientific Applications: Scalability and Performance Portability},
	year = {2017},
	month = {2017-11},
	pages = {17-50},
	publisher = {Chapman \& Hall / CRC Press},
	organization = {Chapman \& Hall / CRC Press},
	chapter = {2},
	abstract = {This chapter explores present-day challenges and those likely to arise as new hardware and software technologies are introduced on the path to exascale. It covers some of the underlying hardware, software, and techniques that enable tools and debuggers. Performance tools and debuggers are critical components that enable computational scientists to fully exploit the computing power of While high-performance computing systems. Instrumentation is the insertion of code to perform measurement in a program. It is vital step in performance analysis, especially for parallel programs. The essence of a debugging tool is enabling observation, exploration, and control of program state, such that a developer can, for example, verify that what is currently occurring correlates to what is intended. The increased complexity and volume of performance and debugging data likely to be seen on exascale systems risks overwhelming tool users. Tools and debuggers may need to develop advanced techniques such as automated filtering and analysis to reduce the complexity seen by the user.},
	isbn = {9781315277400},
	doi = {https://doi.org/10.1201/b21930},
	author = {Scott Parker and John Mellor-Crummey and Dong H. Ahn and Heike Jagode and Holger Brunst and Sameer Shende and Allen D. Malony and David DelSignore and Ronny Tschuter and Ralph Castain and Kevin Harms and Philip Carns and Ray Loy and Kalyan Kumaran}
}
@techreport {1173,
	title = {PLASMA 17 Performance Report},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-17-11},
	year = {2017},
	month = {2017-06},
	publisher = {University of Tennessee},
	abstract = {PLASMA (Parallel Linear Algebra for Multicore Architectures) is a dense linear algebra package at the forefront of multicore computing. PLASMA is designed to deliver the highest possible performance from a system with multiple sockets of multicore processors. PLASMA achieves this objective by combining state of the art solutions in parallel algorithms, scheduling, and software engineering. PLASMA currently offers a collection of routines for solving linear systems of equations and least square problems.},
	author = {Maksims Abalenkovs and Negin Bagherpour and Jack Dongarra and Mark Gates and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Samuel Relton and Jakub Sistek and David Stevens and Panruo Wu and Ichitaro Yamazaki and Asim YarKhan and Mawussi Zounon}
}
@techreport {1172,
	title = {PLASMA 17.1 Functionality Report},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-17-10},
	year = {2017},
	month = {2017-06},
	publisher = {University of Tennessee},
	abstract = {PLASMA (Parallel Linear Algebra for Multicore Architectures) is a dense linear algebra package at the forefront of multicore computing. PLASMA is designed to deliver the highest possible performance from a system with multiple sockets of multicore processors. PLASMA achieves this objective by combining state of the art solutions in parallel algorithms, scheduling, and software engineering. PLASMA currently offers a collection of routines for solving linear systems of equations and least square problems.},
	author = {Maksims Abalenkovs and Negin Bagherpour and Jack Dongarra and Mark Gates and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Samuel Relton and Jakub Sistek and David Stevens and Panruo Wu and Ichitaro Yamazaki and Asim YarKhan and Mawussi Zounon}
}
@article {1067,
	title = {Preconditioned Krylov Solvers on GPUs},
	journal = {Parallel Computing},
	year = {2017},
	month = {2017-06},
	abstract = {In this paper, we study the effect of enhancing GPU-accelerated Krylov solvers with preconditioners. We consider the BiCGSTAB, CGS, QMR, and IDR(s) Krylov solvers. For a large set of test matrices, we assess the impact of Jacobi and incomplete factorization preconditioning on the solvers{\textquoteright} numerical stability and time-to-solution performance. We also analyze how the use of a preconditioner impacts the choice of the fastest solver.},
	keywords = {gpu, ILU, Jacobi, Krylov solvers, Preconditioning},
	issn = {01678191},
	doi = {10.1016/j.parco.2017.05.006},
	url = {http://www.sciencedirect.com/science/article/pii/S0167819117300777},
	author = {Hartwig Anzt and Mark Gates and Jack Dongarra and Moritz Kreutzer and Gerhard Wellein and Martin Kohler}
}
@techreport {1080,
	title = {Roadmap for the Development of a Linear Algebra Library for Exascale Computing: SLATE: Software for Linear Algebra Targeting Exascale},
	journal = {SLATE Working Notes},
	number = {01, ICL-UT-17-02},
	year = {2017},
	month = {2017-06},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	type = {SLATE Working Notes},
	author = {Ahmad Abdelfattah and Hartwig Anzt and Aurelien Bouteiller and Anthony Danalis and Jack Dongarra and Mark Gates and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov and Stephen Wood and Panruo Wu and Ichitaro Yamazaki and Asim YarKhan}
}
@conference {,
	title = {Scaling Point Set Registration in 3D Across Thread Counts on Multicore and Hardware Accelerator Platforms through Autotuning for Large Scale Analysis of Scientific Point Clouds},
	booktitle = {IEEE International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD 2017)},
	year = {2017},
	month = {2017-12},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Boston, MA},
	abstract = {In this article, we present an autotuning approach applied to systematic performance engineering of the EM-ICP (Expectation-Maximization Iterative Closest Point) algorithm for the point set registration problem. We show how we were able to exceed the performance achieved by the reference code through multiple dependence transformations and automated procedure of generating and evaluating numerous implementation variants. Furthermore, we also managed to exploit code transformations that are not that common during manual optimization but yielded better performance in our tests for the EM-ICP algorithm. Finally, we maintained high levels of performance rate in a portable fashion across a wide range of HPC hardware platforms including multicore, many-core, and GPU-based accelerators. More importantly, the results indicate consistently high performance level and ability to move the task of data analysis through point-set registration to any modern compute platform without the concern of inferior asymptotic efficiency.},
	doi = {https://doi.org/10.1109/BigData.2017.8258258},
	author = {Piotr Luszczek and Jakub Kurzak and Ichitaro Yamazaki and David Keffer and Jack Dongarra}
}
@techreport {1082,
	title = {Small Tensor Operations on Advanced Architectures for High-Order Applications},
	journal = {University of Tennessee Computer Science Technical Report},
	number = {UT-EECS-17-749},
	year = {2017},
	month = {2017-04},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	author = {Ahmad Abdelfattah and Marc Baboulin and Veselin Dobrev and Jack Dongarra and Azzam Haidar and Ian Karlin and Tzanio Kolev and Ian Masliah and Stanimire Tomov}
}
@conference {,
	title = {Towards Numerical Benchmark for Half-Precision Floating Point Arithmetic},
	booktitle = {2017 IEEE High Performance Extreme Computing Conference (HPEC)},
	year = {2017},
	month = {2017-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Waltham, MA},
	abstract = {With NVIDA Tegra Jetson X1 and Pascal P100 GPUs, NVIDIA introduced hardware-based computation on FP16 numbers also called half-precision arithmetic. In this talk, we will introduce the steps required to build a viable benchmark for this new arithmetic format. This will include the connections to established IEEE floating point standards and existing HPC benchmarks. The discussion will focus on performance and numerical stability issues that are important for this kind of benchmarking and how they relate to NVIDIA platforms.},
	doi = {https://doi.org/10.1109/HPEC.2017.8091031},
	author = {Piotr Luszczek and Jakub Kurzak and Ichitaro Yamazaki and Jack Dongarra}
}
@article {,
	title = {With Extreme Computing, the Rules Have Changed},
	journal = {Computing in Science \& Engineering},
	volume = {19},
	year = {2017},
	month = {2017-05},
	pages = {52-62},
	abstract = {On the eve of exascale computing, traditional wisdom no longer applies. High-performance computing is gone as we know it. This article discusses a range of new algorithmic techniques emerging in the context of exascale computing, many of which defy the common wisdom of high-performance computing and are considered unorthodox, but could turn out to be a necessity in near future.},
	doi = {https://doi.org/10.1109/MCSE.2017.48},
	author = {Jack Dongarra and Stanimire Tomov and Piotr Luszczek and Jakub Kurzak and Mark Gates and Ichitaro Yamazaki and Hartwig Anzt and Azzam Haidar and Ahmad Abdelfattah}
}
@article {1342,
	title = {Accelerating Tensor Contractions for High-Order FEM on CPUs, GPUs, and KNLs},
	year = {2016},
	month = {2016-09},
	publisher = {moky Mountains Computational Sciences and Engineering Conference (SMC16), Poster},
	address = {Gatlinburg, TN},
	author = {Azzam Haidar and Ahmad Abdelfattah and Veselin Dobrev and Ian Karlin and Tzanio Kolev and Stanimire Tomov and Jack Dongarra}
}
@article {994,
	title = {Accelerating the Conjugate Gradient Algorithm with GPU in CFD Simulations},
	journal = {VECPAR},
	year = {2016},
	abstract = {This paper illustrates how GPU computing can be used to accelerate computational fluid dynamics (CFD) simulations. For sparse linear systems arising from finite volume discretization, we evaluate and optimize the performance of Conjugate Gradient (CG) routines designed for manycore accelerators and compare against an industrial CPU-based implementation. We also investigate how the recent advances in preconditioning, such as iterative Incomplete Cholesky (IC, as symmetric case of ILU) preconditioning, match the requirements for solving real world problems.},
	url = {http://hgpu.org/?p=16264},
	author = {Hartwig Anzt and Marc Baboulin and Jack Dongarra and Yvan Fournier and Frank Hulsemann and Amal Khabou and Yushan Wang}
}
@inbook {883,
	title = {Dense Symmetric Indefinite Factorization on GPU Accelerated Architectures},
	booktitle = {Lecture Notes in Computer Science},
	series = {11th International Conference, PPAM 2015, Krakow, Poland, September 6-9, 2015. Revised Selected Papers, Part I},
	volume = {9573},
	year = {2016},
	month = {2015-09},
	pages = {86-95},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	chapter = {Parallel Processing and Applied Mathematics},
	abstract = {We study the performance of dense symmetric indefinite factorizations (Bunch-Kaufman and Aasen{\textquoteright}s algorithms) on multicore CPUs with a Graphics Processing Unit (GPU). Though such algorithms are needed in many scientific and engineering simulations, obtaining high performance of the factorization on the GPU is difficult because the pivoting that is required to ensure the numerical stability of the factorization leads to frequent synchronizations and irregular data accesses. As a result, until recently, there has not been any implementation of these algorithms on hybrid CPU/GPU architectures. To improve their performance on the hybrid architecture, we explore different techniques to reduce the expensive communication and synchronization between the CPU and GPU, or on the GPU. We also study the performance of an LDL^T factorization with no pivoting combined with the preprocessing technique based on Random Butterfly Transformations. Though such transformations only have probabilistic results on the numerical stability, they avoid the pivoting and obtain a great performance on the GPU.
},
	keywords = {Communication-avoiding, Dense symmetric indefinite factorization, gpu computation, randomization},
	isbn = {978-3-319-32149-3},
	doi = {10.1007/978-3-319-32149-3_9},
	author = {Marc Baboulin and Jack Dongarra and Adrien Remy and Stanimire Tomov and Ichitaro Yamazaki},
	editor = {Roman Wyrzykowski and Ewa Deelman and Konrad Karczewski and Jacek Kitowski and Kazimierz Wiatr}
}
@inproceedings {992,
	title = {Efficiency of General Krylov Methods on GPUs {\textendash} An Experimental Study},
	journal = {2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
	year = {2016},
	month = {2016-05},
	pages = {683-691},
	abstract = {This paper compares different Krylov methods based on short recurrences with respect to their efficiency whenimplemented on GPUs. The comparison includes BiCGSTAB, CGS, QMR, and IDR using different shadow space dimensions. These methods are known for their good convergencecharacteristics. For a large set of test matrices taken from theUniversity of Florida Matrix Collection, we evaluate the methods{\textquoteright}performance against different target metrics: convergence, number of sparse matrix-vector multiplications, and executiontime. We also analyze whether the methods are "orthogonal"in terms of problem suitability. We propose best practicesfor choosing methods in a "black box" scenario, where noinformation about the optimal solver is available.},
	keywords = {algorithmic bombardment, BiCGSTAB, CGS, Convergence, Electric breakdown, gpu, graphics processing units, Hardware, IDR(s), Krylov solver, Libraries, linear systems, QMR, Sparse matrices},
	doi = {10.1109/IPDPSW.2016.45},
	author = {Hartwig Anzt and Jack Dongarra and Moritz Kreutzer and Gerhard Wellein and Martin Kohler}
}
@conference {937,
	title = {Efficiency of General Krylov Methods on GPUs {\textendash} An Experimental Study},
	booktitle = {The Sixth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES)},
	year = {2016},
	month = {2016-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Chicago, IL},
	abstract = {This paper compares different Krylov methods based on short recurrences with respect to their efficiency when implemented on GPUs. The comparison includes BiCGSTAB, CGS, QMR, and IDR using different shadow space dimensions. These methods are known for their good convergence characteristics. For a large set of test matrices taken from the University of Florida Matrix Collection, we evaluate the methods{\textquoteright}
performance against different target metrics: convergence, number of sparse matrix-vector multiplications, and execution time. We also analyze whether the methods are {\textquotedblleft}orthogonal{\textquotedblright} in terms of problem suitability. We propose best practices for choosing methods in a {\textquotedblleft}black box{\textquotedblright} scenario, where no information about the optimal solver is available.},
	keywords = {algorithmic bombardment, BiCGSTAB, CGS, gpu, IDR(s), Krylov solver, QMR},
	doi = {10.1109/IPDPSW.2016.45},
	author = {Hartwig Anzt and Jack Dongarra and Moritz Kreutzer and Gerhard Wellein and Martin Kohler}
}
@conference {939,
	title = {Heterogeneous Streaming},
	booktitle = {The Sixth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), IPDPS 2016},
	year = {2016},
	month = {2016-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Chicago, IL},
	abstract = {This paper introduces a new heterogeneous streaming library called hetero Streams (hStreams). We show how a simple FIFO streaming model can be applied to heterogeneous systems that include manycore coprocessors and multicore CPUs. This model supports concurrency across nodes, among tasks within a node, and between data transfers and computation. We give examples for different approaches, show how the implementation can be layered, analyze overheads among layers, and apply those models to parallelize applications using simple, intuitive interfaces. We compare the features and versatility of hStreams, OpenMP, CUDA Streams1 and OmpSs. We show how the use of hStreams makes it easier for scientists to identify tasks and easily expose concurrency among them, and how it enables tuning experts and runtime systems to tailor execution for different heterogeneous targets. Practical application examples are taken from the field of numerical linear algebra, commercial structural simulation software, and a seismic processing application.},
	keywords = {plasma},
	author = {Chris J. Newburn and Gaurav Bansal and Michael Wood and Luis Crivelli and Judit Planas and Alejandro Duran and Paulo Souza and Leonardo Borges and Piotr Luszczek and Stanimire Tomov and Jack Dongarra and Hartwig Anzt and Mark Gates and Azzam Haidar and Yulu Jia and Khairul Kabir and Ichitaro Yamazaki and Jesus Labarta}
}
@techreport {929,
	title = {High-Performance Tensor Contractions for GPUs},
	journal = {University of Tennessee Computer Science Technical Report},
	number = {UT-EECS-16-738},
	year = {2016},
	month = {2016-01},
	publisher = {University of Tennessee},
	abstract = {We present a computational framework for high-performance tensor contractions on GPUs. High-performance is difficult to obtain using existing libraries, especially for many  independent contractions where each contraction is very small, e.g., sub-vector/warp in size. However, using our framework to batch contractions plus application-specifics, we demonstrate close to peak performance results. In particular, to accelerate large scale tensor-formulated high-order finite element method (FEM) simulations, which is the main focus and motivation for this work, we represent contractions as tensor index reordering plus matrix-matrix multiplications (GEMMs). This is a key factor to achieve algorithmically many-fold acceleration (vs. not using it) due to possible reuse of data loaded in fast memory. In addition to using this context knowledge, we design tensor data-structures, tensor algebra interfaces, and new tensor contraction algorithms and implementations to achieve 90+\% of a theoretically derived peak on GPUs. On a K40c GPU for contractions resulting in GEMMs on square matrices of size 8 for example, we are 2.8{\texttimes} faster than CUBLAS, and 8.5{\texttimes} faster than MKL on 16 cores of Intel Xeon ES-2670 (Sandy Bridge) 2.60GHz CPUs. Finally, we apply autotuning and code generation techniques to simplify tuning and provide an architecture-aware, user-friendly interface.},
	author = {Ahmad Abdelfattah and Marc Baboulin and Veselin Dobrev and Jack Dongarra and Christopher Earl and Jo{\"e}l Falcou and Azzam Haidar and Ian Karlin and Tzanio Kolev and Ian Masliah and Stanimire Tomov}
}
@conference {942,
	title = {High-Performance Tensor Contractions for GPUs},
	booktitle = {International Conference on Computational Science (ICCS{\textquoteright}16)},
	year = {2016},
	month = {2016-06},
	address = {San Diego, CA},
	abstract = {We present a computational framework for high-performance tensor contractions on GPUs. High-performance is difficult to obtain using existing libraries, especially for many independent contractions where each contraction is very small, e.g., sub-vector/warp in size. However, using our framework to batch contractions plus application-specifics, we demonstrate close to peak performance results. In particular, to accelerate large scale tensor-formulated high-order finite element method (FEM) simulations, which is the main focus and motivation for this work, we represent contractions as tensor index reordering plus matrix-matrix multiplications (GEMMs). This is a key factor to achieve algorithmically many-fold acceleration (vs. not using it) due to possible reuse of data loaded in fast memory. In addition to using this context knowledge, we design tensor data-structures, tensor algebra interfaces, and new tensor contraction algorithms and implementations to achieve 90+\% of a theoretically derived peak on GPUs. On a K40c GPU for contractions resulting in GEMMs on square matrices of size 8 for example, we are 2.8{\texttimes} faster than CUBLAS, and 8.5{\texttimes} faster than MKL on 16 cores of Intel Xeon E5-2670 (Sandy Bridge) 2.60GHz CPUs. Finally, we apply autotuning and code generation techniques to simplify tuning and provide an architecture-aware, user-friendly interface.},
	keywords = {Applications, Batched linear algebra, FEM, gpu, Tensor contractions, Tensor HPC},
	author = {Ahmad Abdelfattah and Marc Baboulin and Veselin Dobrev and Jack Dongarra and Christopher Earl and Jo{\"e}l Falcou and Azzam Haidar and Ian Karlin and Tzanio Kolev and Ian Masliah and Stanimire Tomov}
}
@article {1472,
	title = {Linear Algebra Software for Large-Scale Accelerated Multicore Computing},
	journal = {Acta Numerica},
	volume = {25},
	year = {2016},
	month = {2016-05},
	pages = {1-160},
	abstract = {Many crucial scientific computing applications, ranging from national security to medical advances, rely on high-performance linear algebra algorithms and technologies, underscoring their importance and broad impact. Here we present the state-of-the-art design and implementation practices for the acceleration of the predominant linear algebra algorithms on large-scale accelerated multicore systems. Examples are given with fundamental dense linear algebra algorithms {\textendash} from the LU, QR, Cholesky, and LDLT factorizations needed for solving linear systems of equations, to eigenvalue and singular value decomposition (SVD) problems. The implementations presented are readily available via the open-source PLASMA and MAGMA libraries, which represent the next generation modernization of the popular LAPACK library for accelerated multicore systems. To generate the extreme level of parallelism needed for the efficient use of these systems, algorithms of interest are redesigned and then split into well-chosen computational tasks. The task execution is scheduled over the computational components of a hybrid system of multicore CPUs with GPU accelerators and/or Xeon Phi coprocessors, using either static scheduling or light-weight runtime systems. The use of light-weight runtime systems keeps scheduling overheads low, similar to static scheduling, while enabling the expression of parallelism through sequential-like code. This simplifies the development effort and allows exploration of the unique strengths of the various hardware components. Finally, we emphasize the development of innovative linear algebra algorithms using three technologies {\textendash} mixed precision arithmetic, batched operations, and asynchronous iterations {\textendash} that are currently of high interest for accelerated multicore systems.},
	doi = {10.1017/S0962492916000015},
	author = {Ahmad Abdelfattah and Hartwig Anzt and Jack Dongarra and Mark Gates and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov and undefined and Asim YarKhan}
}
@inbook {997,
	title = {Performance, Design, and Autotuning of Batched GEMM for GPUs},
	booktitle = {High Performance Computing: 31st International Conference, ISC High Performance 2016, Frankfurt, Germany, June 19-23, 2016, Proceedings},
	number = {9697},
	year = {2016},
	pages = {21{\textendash}38},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {The general matrix-matrix multiplication (GEMM) is the most important numerical kernel in dense linear algebra, and is the key component for obtaining high performance in most LAPACK routines. As batched computations on relatively small problems continue to gain interest in many scientific applications, a need arises for a high performance GEMM kernel for batches of small matrices. Such a kernel should be well designed and tuned to handle small sizes, and to maintain high performance for realistic test cases found in the higher level LAPACK routines, and scientific computing applications in general.

This paper presents a high performance batched GEMM kernel on Graphics Processing Units (GPUs). We address batched problems with both fixed and variable sizes, and show that specialized GEMM designs and a comprehensive autotuning process are needed to handle problems of small sizes. For most performance tests reported in this paper, the proposed kernels outperform state-of-the-art approaches using a K40c GPU.},
	isbn = {978-3-319-41321-1},
	doi = {10.1007/978-3-319-41321-1_2},
	url = {http://dx.doi.org/10.1007/978-3-319-41321-1_2},
	author = {Ahmad Abdelfattah and Azzam Haidar and Stanimire Tomov and Jack Dongarra},
	editor = {Julian M. Kunkel and Pavan Balaji and Jack Dongarra}
}
@article {985,
	title = {Performance optimization of Sparse Matrix-Vector Multiplication for multi-component PDE-based applications using GPUs},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {28},
	year = {2016},
	month = {2016-05},
	pages = {3447 - 3465},
	abstract = {Simulations of many multi-component PDE-based applications, such as petroleum reservoirs or reacting flows, are dominated by the solution, on each time step and within each Newton step, of large sparse linear systems. The standard solver is a preconditioned Krylov method. Along with application of the preconditioner, memory-bound Sparse Matrix-Vector Multiplication (SpMV) is the most time-consuming operation in such solvers. Multi-species models produce Jacobians with a dense block structure, where the block size can be as large as a few dozen. Failing to exploit this dense block structure vastly underutilizes hardware capable of delivering high performance on dense BLAS operations. This paper presents a GPU-accelerated SpMV kernel for block-sparse matrices. Dense matrix-vector multiplications within the sparse-block structure leverage optimization techniques from the KBLAS library, a high performance library for dense BLAS kernels. The design ideas of KBLAS can be applied to block-sparse matrices. Furthermore, a technique is proposed to balance the workload among thread blocks when there are large variations in the lengths of nonzero rows. Multi-GPU performance is highlighted. The proposed SpMV kernel outperforms existing state-of-the-art implementations using matrices with real structures from different applications.},
	doi = {10.1002/cpe.v28.1210.1002/cpe.3874},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/cpe.3874/full},
	author = {Ahmad Abdelfattah and Hatem Ltaeif and David Keyes and Jack Dongarra}
}
@article {986,
	title = {Porting the PLASMA Numerical Library to the OpenMP Standard},
	journal = {International Journal of Parallel Programming},
	year = {2016},
	month = {2016-06},
	abstract = {PLASMA is a numerical library intended as a successor to LAPACK for solving problems in dense linear algebra on multicore processors. PLASMA relies on the QUARK scheduler for efficient multithreading of algorithms expressed in a serial fashion. QUARK is a superscalar scheduler and implements automatic parallelization by tracking data dependencies and resolving data hazards at runtime. Recently, this type of scheduling has been incorporated in the OpenMP standard, which allows to transition PLASMA from the proprietary solution offered by QUARK to the standard solution offered by OpenMP. This article studies the feasibility of such transition.},
	issn = {0885-7458},
	doi = {10.1007/s10766-016-0441-6},
	url = {http://link.springer.com/10.1007/s10766-016-0441-6http://link.springer.com/content/pdf/10.1007/s10766-016-0441-6http://link.springer.com/content/pdf/10.1007/s10766-016-0441-6.pdfhttp://link.springer.com/article/10.1007/s10766-016-0441-6/fulltext.html},
	author = {Asim YarKhan and Jakub Kurzak and Piotr Luszczek and Jack Dongarra}
}
@conference {962,
	title = {Search Space Generation and Pruning System for Autotuners},
	booktitle = {30th IEEE International Parallel \& Distributed Processing Symposium (IPDPS)},
	year = {2016},
	month = {2016-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Chicago, IL},
	abstract = {This work tackles two simultaneous challenges faced by autotuners: the ease of describing a complex, multidimensional search space, and the speed of evaluating that space, while applying a multitude of pruning constraints. This article presents a declarative notation for describing a search space and a translation system for conversion to a standard C code for fast and multithreaded, as necessary, evaluation. The notation is Python-based and thus simple in syntax and easy to assimilate by the user interested in tuning rather than learning a new programming language. A large number of dimensions and a large number of pruning constraints may be expressed with little effort. The system is discussed in the context of autotuning the canonical matrix multiplication kernel for NVIDIA GPUs, where the search space has 15 dimensions and involves application of 10 complex pruning constrains. The speed of evaluation is compared against generators created using imperative programming style in various scripting and compiled languages.},
	author = {Piotr Luszczek and Mark Gates and Jakub Kurzak and Anthony Danalis and Jack Dongarra}
}
@conference {880,
	title = {Accelerating Collaborative Filtering for Implicit Feedback Datasets using GPUs},
	booktitle = {2015 IEEE International Conference on Big Data (IEEE BigData 2015)},
	year = {2015},
	month = {2015-11},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Santa Clara, CA},
	abstract = {In this paper we accelerate the Alternating Least Squares (ALS) algorithm used for generating product recommendations on the basis of implicit feedback datasets. We approach the algorithm with concepts proven to be successful in High Performance Computing. This includes the formulation of the algorithm as a mix of cache-optimized algorithm-specific kernels and standard BLAS routines, acceleration via graphics processing units (GPUs), use of parallel batched kernels, and autotuning to identify performance winners. For benchmark datasets, the multi-threaded CPU implementation we propose achieves more than a 10 times speedup over the implementations available in the GraphLab and Spark MLlib software packages. For the GPU implementation, the parameters of an algorithm-specific kernel were optimized using a comprehensive autotuning sweep. This results in an additional 2 times speedup over our CPU implementation.},
	author = {Mark Gates and Hartwig Anzt and Jakub Kurzak and Jack Dongarra}
}
@conference {842,
	title = { A Data Flow Divide and Conquer Algorithm for Multicore Architecture},
	booktitle = {29th IEEE International Parallel \& Distributed Processing Symposium (IPDPS)},
	year = {2015},
	month = {2015-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Hyderabad, India},
	abstract = {Computing eigenpairs of a symmetric matrix is a problem arising in many industrial applications, including quantum physics and finite-elements computation for automobiles. A classical approach is to reduce the matrix to tridiagonal form before computing eigenpairs of the tridiagonal matrix. Then, a back-transformation allows one to obtain the final solution. Parallelism issues of the reduction stage have already been tackled in different shared-memory libraries. In this article, we focus on solving the tridiagonal eigenproblem, and we describe a novel implementation of the Divide and Conquer algorithm. The algorithm is expressed as a sequential task-flow, scheduled in an out-of-order fashion by a dynamic runtime which allows the programmer to play with tasks granularity. The resulting implementation is between two and five times faster than the equivalent routine from the Intel MKL library, and outperforms the best MRRR implementation for many matrices.},
	keywords = {Eigensolver, lapack, Multicore, plasma, task-based programming},
	author = {Azzam Haidar and Jakub Kurzak and Gregoire Pichon and Mathieu Faverge}
}
@conference {862,
	title = {On the Design, Development, and Analysis of Optimized Matrix-Vector Multiplication Routines for Coprocessors},
	booktitle = {ISC High Performance 2015},
	year = {2015},
	month = {2015-07},
	address = {Frankfurt, Germany},
	abstract = {The dramatic change in computer architecture due to the manycore paradigm shift, made the development of numerical routines that are optimal extremely challenging. In this work, we target the development of numerical algorithms and implementations for Xeon Phi coprocessor architecture designs. In particular, we examine and optimize the general and symmetric matrix-vector multiplication routines (gemv/symv), which are some of the most heavily used linear algebra kernels in many important engineering and physics applications. We describe a successful approach on how to address the challenges for this problem, starting from our algorithm design, performance analysis and programing model, to kernel optimization. Our goal, by targeting low-level, easy to understand fundamental kernels, is to develop new optimization strategies that can be effective elsewhere for the use on manycore coprocessors, and to show significant performance improvements compared to the existing state-of-the-art implementations. Therefore, in addition to the new optimization strategies, analysis, and optimal performance results, we finally present the significance of using these routines/strategies to accelerate higher-level numerical algorithms for the eigenvalue problem (EVP) and the singular value decomposition (SVD) that by themselves are foundational for many important applications.},
	author = {Khairul Kabir and Azzam Haidar and Stanimire Tomov and Jack Dongarra}
}
@conference {886,
	title = {Efficient Implementation Of Quantum Materials Simulations On Distributed CPU-GPU Systems},
	booktitle = {The International Conference for High Performance Computing, Networking, Storage and Analysis (SC15)},
	year = {2015},
	month = {2015-11},
	publisher = {ACM},
	organization = {ACM},
	address = {Austin, TX},
	abstract = {We present a scalable implementation of the Linearized Augmented Plane Wave method for distributed memory systems, which relies on an efficient distributed, block-cyclic setup of the Hamiltonian and overlap matrices and allows us to turn around highly accurate 1000+ atom all-electron quantum materials simulations on clusters with a few hundred nodes. The implementation runs efficiently on standard multicore CPU nodes, as well as hybrid CPU-GPU nodes. The key for the latter is a novel algorithm to solve the generalized eigenvalue problem for dense, complex Hermitian matrices on distributed hybrid CPU-GPU systems. Performance tests for Li-intercalated CoO2 supercells containing 1501 atoms demonstrate that high-accuracy, transferable quantum simulations can now be used in throughput materials search problems. While our application can benefit and get scalable performance through CPU-only libraries like ScaLAPACK or ELPA2, our new hybrid solver enables the efficient use of GPUs and shows that a hybrid CPU-GPU architecture scales to a desired performance using substantially fewer cluster nodes, and notably, is considerably more energy efficient than the traditional multicore CPU only systems for such complex applications.},
	author = {Raffaele Solc{\`a} and Anton Kozhevnikov and Azzam Haidar and Stanimire Tomov and Thomas C. Schulthess and Jack Dongarra}
}
@article {873,
	title = {Experiences in autotuning matrix multiplication for energy minimization on GPUs},
	journal = {Concurrency in Computation: Practice and Experience},
	volume = {27},
	year = {2015},
	month = {2015-12},
	pages = {5096-5113},
	doi = {10.1002/cpe.3516},
	author = {Hartwig Anzt and Blake Haugen and Jakub Kurzak and Piotr Luszczek and Jack Dongarra}
}
@article {982,
	title = {Experiences in Autotuning Matrix Multiplication for Energy Minimization on GPUs},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {27},
	year = {2015},
	month = {12-Oct},
	pages = {5096 - 5113},
	abstract = {In this paper, we report extensive results and analysis of autotuning the computationally intensive graphics processing units kernel for dense matrix{\textendash}matrix multiplication in double precision. In contrast to traditional autotuning and/or optimization for runtime performance only, we also take the energy efficiency into account. For kernels achieving equal performance, we show significant differences in their energy balance. We also identify the memory throughput as the most influential metric that trades off performance and energy efficiency. As a result, the performance optimal case ends up not being the most efficient kernel in overall resource use.},
	keywords = {Autotuning, energy efficiency, hardware accelerators, matrix multiplication, power},
	doi = {10.1002/cpe.3516},
	url = {http://doi.wiley.com/10.1002/cpe.3516https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002\%2Fcpe.3516},
	author = {Hartwig Anzt and Blake Haugen and Jakub Kurzak and Piotr Luszczek and Jack Dongarra}
}
@article {829,
	title = {HPC Programming on Intel Many-Integrated-Core Hardware with MAGMA Port to Xeon Phi},
	journal = {Scientific Programming},
	volume = {23},
	year = {2015},
	month = {2015-01},
	abstract = {This paper presents the design and implementation of several fundamental dense linear algebra (DLA) algorithms for multicore with Intel Xeon Phi Coprocessors. In particular, we consider algorithms for solving linear systems. Further, we give an overview of the MAGMA MIC library, an open source, high performance library that incorporates the developments presented, and in general provides to heterogeneous architectures of multicore with coprocessors the DLA functionality of the popular LAPACK library. The LAPACK-compliance simplifies the use of the MAGMA MIC library in applications, while providing them with portably performant DLA. High performance is obtained through use of the high-performance BLAS, hardware-specific tuning, and a hybridization methodology where we split the algorithm into computational tasks of various granularities. Execution of those tasks is properly scheduled over the heterogeneous hardware components by minimizing data movements and mapping algorithmic requirements to the architectural strengths of the various heterogeneous hardware components. Our methodology and programming techniques are incorporated into the MAGMA MIC API, which abstracts the application developer from the specifics of the Xeon Phi architecture and is therefore applicable to algorithms beyond the scope of DLA.},
	keywords = {communication and computation overlap, dynamic runtime scheduling using dataflow dependences, hardware accelerators and coprocessors, Intel Xeon Phi processor, Many Integrated Cores, numerical linear algebra},
	issn = {1058-9244},
	doi = {10.3233/SPR-140404},
	author = {Azzam Haidar and Jack Dongarra and Khairul Kabir and Mark Gates and Piotr Luszczek and Stanimire Tomov and Yulu Jia}
}
@article {881,
	title = {Implementation and Tuning of Batched Cholesky Factorization and Solve for NVIDIA GPUs},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	number = {1045-9219},
	year = {2015},
	month = {2015-11},
	author = {Jakub Kurzak and Hartwig Anzt and Mark Gates and Jack Dongarra}
}
@article {1347,
	title = {MAGMA MIC: Optimizing Linear Algebra for Intel Xeon Phi},
	year = {2015},
	month = {2015-06},
	publisher = {ISC High Performance (ISC15), Intel Booth Presentation},
	address = {Frankfurt, Germany},
	author = {Hartwig Anzt and Jack Dongarra and Mark Gates and Azzam Haidar and Khairul Kabir and Piotr Luszczek and Stanimire Tomov and Ichitaro Yamazaki}
}
@conference {891,
	title = {Mixed-precision Block Gram Schmidt Orthogonalization},
	booktitle = {6th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems},
	year = {2015},
	month = {2015-11},
	publisher = {ACM},
	organization = {ACM},
	address = {Austin, TX},
	abstract = {The mixed-precision Cholesky QR (CholQR) can orthogonalize the columns of a dense matrix with the minimum communication cost. Moreover, its orthogonality error depends only linearly to the condition number of the input matrix. However, when the desired higher-precision is not supported by the hardware, the software-emulated arithmetics are needed, which could significantly increase its computational cost. When there are a large number of columns to be orthogonalized, this computational overhead can have a significant
impact on the orthogonalization time, and the mixed-precision CholQR can be much slower than the standard CholQR. In this paper, we examine several block variants of the algorithm, which reduce the computational overhead associated with the software-emulated arithmetics, while maintaining the same orthogonality error bound as the mixed-precision CholQR. Our numerical and performance results on multicore CPUs with a GPU, as well as a hybrid CPU/GPU cluster, demonstrate that compared to the mixed-precision CholQR, such a block variant can obtain speedups of up to 7:1 while maintaining about the same order of the numerical errors.},
	author = {Ichitaro Yamazaki and Stanimire Tomov and Jakub Kurzak and Jack Dongarra and Jesse Barlow}
}
@conference {897,
	title = {Mixed-precision orthogonalization process Performance on multicore CPUs with GPUs},
	booktitle = {2015 SIAM Conference on Applied Linear Algebra},
	year = {2015},
	month = {2015-10},
	publisher = {SIAM},
	organization = {SIAM},
	address = {Atlanta, GA},
	abstract = {Orthogonalizing a set of dense vectors is an important computational kernel in subspace projection methods for solving large-scale problems. In this talk, we discuss our efforts to improve the performance of the kernel, while maintaining its numerical accuracy. Our experimental results demonstrate the effectiveness of our approaches.},
	author = {Ichitaro Yamazaki and Jesse Barlow and Stanimire Tomov and Jakub Kurzak and Jack Dongarra}
}
@article {936,
	title = {Parallel Programming Models for Dense Linear Algebra on Heterogeneous Systems},
	journal = {Supercomputing Frontiers and Innovations},
	volume = {2},
	number = {4},
	year = {2015},
	month = {2015-10},
	abstract = {We present a review of the current best practices in parallel programming models for dense linear algebra (DLA) on heterogeneous architectures. We consider multicore CPUs, stand alone manycore coprocessors, GPUs, and combinations of these. Of interest is the evolution of the programming models for DLA libraries {\textendash} in particular, the evolution from the popular LAPACK and ScaLAPACK libraries to their modernized counterparts PLASMA (for multicore CPUs) and MAGMA (for heterogeneous architectures), as well as other programming models and libraries. Besides providing insights into the programming techniques of the libraries considered, we outline our view of the current strengths and weaknesses of their programming models {\textendash} especially in regards to hardware trends and ease of programming high-performance numerical software that current applications need {\textendash} in order to motivate work and future directions for the next generation of parallel programming models for high-performance linear algebra libraries on heterogeneous systems.},
	keywords = {dense linear algebra, gpu, HPC, Multicore, plasma, Programming models, runtime},
	doi = {10.14529/jsfi1504},
	author = {Maksims Abalenkovs and Ahmad Abdelfattah and Jack Dongarra and Mark Gates and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov and Ichitaro Yamazaki and Asim YarKhan}
}
@conference {860,
	title = {Performance Analysis and Design of a Hessenberg Reduction using Stabilized Blocked Elementary Transformations for New Architectures},
	booktitle = {The Spring Simulation Multi-Conference 2015 (SpringSim{\textquoteright}15), Best Paper Award},
	year = {2015},
	month = {2015-04},
	address = {Alexandria, VA},
	abstract = {The solution of nonsymmetric eigenvalue problems, Ax = λx, can be accelerated substantially by first reducing A to an upper Hessenberg matrix H that has the same eigenvalues as A. This can be done using Householder orthogonal transformations, which is a well established standard, or stabilized elementary transformations. The latter approach, although having half the flops of the former, has been used less in practice, e.g., on computer architectures with well developed hierarchical memories, because of its memory-bound operations and the complexity in stabilizing it. In this paper we revisit the stabilized elementary transformations approach in the context of new architectures {\textendash} both multicore CPUs and Xeon Phi coprocessors. We derive for a first time a blocking version of the algorithm. The blocked version reduces the memory-bound operations and we analyze its performance. A performance model is developed that shows the limitations of both approaches. The competitiveness of using stabilized elementary transformations has been quantified, highlighting that it can be 20 to 30\% faster on current high-end multicore CPUs and Xeon Phi coprocessors.},
	keywords = {Eigenvalues problem, Hessenberg reduction, Multi/Many-core, Stabilized Elementary Transformations},
	author = {Khairul Kabir and Azzam Haidar and Stanimire Tomov and Jack Dongarra}
}
@conference {861,
	title = {Performance Analysis and Optimization of Two-Sided Factorization Algorithms for Heterogeneous Platform},
	booktitle = {International Conference on Computational Science (ICCS 2015)},
	year = {2015},
	month = {2015-06},
	address = {Reykjav{\'\i}k, Iceland},
	author = {Khairul Kabir and Azzam Haidar and Stanimire Tomov and Jack Dongarra}
}
@conference {884,
	title = {Performance of Random Sampling for Computing Low-rank Approximations of a Dense Matrix on GPUs},
	booktitle = {The International Conference for High Performance Computing, Networking, Storage and Analysis (SC15)},
	year = {2015},
	month = {2015-11},
	publisher = {ACM},
	organization = {ACM},
	address = {Austin, TX},
	author = {Theo Mary and Ichitaro Yamazaki and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov and Jack Dongarra}
}
@conference {885,
	title = {Randomized Algorithms to Update Partial Singular Value Decomposition on a Hybrid CPU/GPU Cluster},
	booktitle = {The International Conference for High Performance Computing, Networking, Storage and Analysis (SC15)},
	year = {2015},
	month = {2015-11},
	publisher = {ACM},
	organization = {ACM},
	address = {Austin, TX},
	author = {Ichitaro Yamazaki and Jakub Kurzak and Piotr Luszczek and Jack Dongarra}
}
@article {699,
	title = {A Survey of Recent Developments in Parallel Implementations of Gaussian Elimination},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {27},
	year = {2015},
	month = {2015-04},
	pages = {1292-1309},
	abstract = {Gaussian elimination is a canonical linear algebra procedure for solving linear systems of equations. In the last few years, the algorithm has received a lot of attention in an attempt to improve its parallel performance. This article surveys recent developments in parallel implementations of Gaussian elimination for shared memory architecture. Five different flavors are investigated. Three of them are based on different strategies for pivoting: partial pivoting, incremental pivoting, and tournament pivoting. The fourth one replaces pivoting with the Partial Random Butterfly Transformation, and finally, an implementation without pivoting is used as a performance baseline. The technique of iterative refinement is applied to recover numerical accuracy when necessary. All parallel implementations are produced using dynamic, superscalar, runtime scheduling and tile matrix layout. Results on two multisocket multicore systems are presented. Performance and numerical accuracy is analyzed.},
	keywords = {Gaussian elimination, lu factorization, Multicore, parallel, plasma, shared memory},
	doi = {10.1002/cpe.3306},
	author = {Simplice Donfack and Jack Dongarra and Mathieu Faverge and Mark Gates and Jakub Kurzak and Piotr Luszczek and Ichitaro Yamazaki}
}
@article {1346,
	title = {Towards a High-Performance Tensor Algebra Package for Accelerators},
	year = {2015},
	month = {2015-09},
	publisher = {moky Mountains Computational Sciences and Engineering Conference (SMC15)},
	address = {Gatlinburg, TN},
	author = {Marc Baboulin and Veselin Dobrev and Jack Dongarra and Christopher Earl and Jo{\"e}l Falcou and Azzam Haidar and Ian Karlin and Tzanio Kolev and Ian Masliah and Stanimire Tomov}
}
@inproceedings {1309,
	title = {UCX: An Open Source Framework for HPC Network APIs and Beyond},
	journal = {2015 IEEE 23rd Annual Symposium on High-Performance Interconnects},
	year = {2015},
	month = {Aug},
	pages = {40-43},
	publisher = {IEEE},
	address = {Santa Clara, CA, USA},
	abstract = {This paper presents Unified Communication X (UCX), a set of network APIs and their implementations for high throughput computing. UCX comes from the combined effort of national laboratories, industry, and academia to design and implement a high-performing and highly-scalable network stack for next generation applications and systems. UCX design provides the ability to tailor its APIs and network functionality to suit a wide variety of application domains and hardware. We envision these APIs to satisfy the networking needs of many programming models such as Message Passing Interface (MPI), OpenSHMEM, Partitioned Global Address Space (PGAS) languages, task-based paradigms and I/O bound applications. To evaluate the design we implement the APIs and protocols, and measure the performance of overhead-critical network primitives fundamental for implementing many parallel programming models and system libraries. Our results show that the latency, bandwidth, and message rate achieved by the portable UCX prototype is very close to that of the underlying driver. With UCX, we achieved a message exchange latency of 0.89 us, a bandwidth of 6138.5 MB/s, and a message rate of 14 million messages per second. As far as we know, this is the highest bandwidth and message rate achieved by any network stack (publicly known) on this hardware.},
	keywords = {application program interfaces, Bandwidth, Electronics packaging, Hardware, high throughput computing, highly-scalable network stack, HPC, HPC network APIs, I/O bound applications, Infiniband, input-output programs, Libraries, Memory management, message passing, message passing interface, Middleware, MPI, open source framework, OpenSHMEM, parallel programming, parallel programming models, partitioned global address space languages, PGAS, PGAS languages, Programming, protocols, public domain software, RDMA, system libraries, task-based paradigms, UCX, Unified Communication X},
	isbn = {978-1-4673-9160-3},
	doi = {10.1109/HOTI.2015.13},
	author = {P. Shamis and Manjunath Gorentla Venkata and M. Graham Lopez and M. B. Baker and O. Hernandez and Y. Itigin and M. Dubman and G. Shainer and R. L. Graham and L. Liss and Y. Shahar and S. Potluri and D. Rossetti and D. Becker and D. Poole and C. Lamb and S. Kumar and C. Stunkel and George Bosilca and Aurelien Bouteiller}
}
@conference {892,
	title = {Visualizing Execution Traces with Task Dependencies},
	booktitle = {2nd Workshop on Visual Performance Analysis (VPA {\textquoteright}15)},
	year = {2015},
	month = {2015-11},
	publisher = {ACM},
	organization = {ACM},
	address = {Austin, TX},
	abstract = {Task-based scheduling has emerged as one method to reduce the complexity of parallel computing. When using task-based schedulers, developers must frame their computation as a series of tasks with various data dependencies. The scheduler can take these tasks, along with their input and output dependencies, and schedule the task in parallel across a node or cluster. While these schedulers simplify the process of parallel software development, they can obfuscate the performance characteristics of the execution of an algorithm. The execution trace has been used for many years to give developers a visual representation of how their computations are performed. These methods can be employed to visualize when and where each of the tasks in a task-based algorithm is scheduled. In addition, the task dependencies can be used to create a directed acyclic graph (DAG) that can also be visualized to demonstrate the dependencies of the various tasks that make up a workload. The work presented here aims to combine these two data sets and extend execution trace visualization to better suit task-based workloads. This paper presents a brief description of task-based schedulers and the performance data they produce. It will then describe an interactive extension to the current trace visualization methods that combines the trace and DAG data sets. This new tool allows users to gain a greater understanding of how their tasks are scheduled. It also provides a simplified way for developers to evaluate and debug the performance of their scheduler. },
	author = {Blake Haugen and Stephen Richmond and Jakub Kurzak and Chad A. Steed and Jack Dongarra}
}
@inbook {780,
	title = {Accelerating Numerical Dense Linear Algebra Calculations with GPUs},
	booktitle = {Numerical Computations with GPUs},
	year = {2014},
	pages = {3-28},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	chapter = {1},
	isbn = {978-3-319-06547-2},
	doi = {10.1007/978-3-319-06548-9_1},
	author = {Jack Dongarra and Mark Gates and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov and Ichitaro Yamazaki}
}
@conference {818,
	title = {Access-averse Framework for Computing Low-rank Matrix Approximations},
	booktitle = {First International Workshop on High Performance Big Graph Data Management, Analysis, and Mining},
	year = {2014},
	month = {2014-10},
	address = {Washington, DC},
	author = {Ichitaro Yamazaki and Theo Mary and Jakub Kurzak and Stanimire Tomov and Jack Dongarra}
}
@conference {710,
	title = {Design and Implementation of a Large Scale Tree-Based QR Decomposition Using a 3D Virtual Systolic Array and a Lightweight Runtime},
	booktitle = {Workshop on Large-Scale Parallel Processing, IPDPS 2014},
	year = {2014},
	month = {2014-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Phoenix, AZ},
	abstract = {A systolic array provides an alternative computing paradigm to the von Neuman architecture. Though its hardware implementation has failed as a paradigm to design integrated circuits in the past, we are now discovering that the systolic array as a software virtualization layer can lead to an extremely scalable execution paradigm. To demonstrate this scalability, in this paper, we design and implement a 3D virtual systolic array to compute a tile QR decomposition of a tall-and-skinny dense matrix. Our implementation is based on a state-of-the-art algorithm that factorizes a panel based on a tree-reduction. Using a runtime developed as a part of the Parallel Ultra Light Systolic Array Runtime (PULSAR) project, we demonstrate on a Cray-XT5 machine how our virtual systolic array can be mapped to a large-scale machine and obtain excellent parallel performance. This is an important contribution since such a QR decomposition is used, for example, to compute a least squares solution of an overdetermined system, which arises in many scientific and engineering problems.},
	keywords = {dataflow, message-passing, multithreading, QR decomposition, runtime, systolic array},
	author = {Ichitaro Yamazaki and Jakub Kurzak and Piotr Luszczek and Jack Dongarra}
}
@article {760,
	title = {Looking Back at Dense Linear Algebra Software},
	journal = {Journal of Parallel and Distributed Computing},
	volume = {74},
	year = {2014},
	month = {2014-07},
	pages = {2548{\textendash}2560},
	chapter = {2548},
	abstract = {Over the years, computational physics and chemistry served as an ongoing source of problems that demanded the ever increasing performance from hardware as well as the software that ran on top of it. Most of these problems could be translated into solutions for systems of linear equations: the very topic of numerical linear algebra. Seemingly then, a set of efficient linear solvers could be solving important scientific problems for years to come. We argue that dramatic changes in hardware designs precipitated by the shifting nature of the marketplace of computer hardware had a continuous effect on the software for numerical linear algebra. The extraction of high percentages of peak performance continues to require adaptation of software. If the past history of this adaptive nature of linear algebra software is any guide then the future theme will feature changes as well{\textendash}changes aimed at harnessing the incredible advances of the evolving hardware infrastructure.},
	keywords = {decompositional approach, dense linear algebra, parallel algorithms},
	doi = {10.1016/j.jpdc.2013.10.005},
	author = {Piotr Luszczek and Jakub Kurzak and Jack Dongarra}
}
@article {831,
	title = {Model-Driven One-Sided Factorizations on Multicore, Accelerated Systems},
	journal = {Supercomputing Frontiers and Innovations},
	volume = {1},
	year = {2014},
	abstract = {Hardware heterogeneity of the HPC platforms is no longer considered unusual but instead have become the most viable way forward towards Exascale.  In fact, the multitude of the heterogeneous resources available to modern computers are designed for different workloads and their efficient use is closely aligned with the specialized role envisaged by their design.  Commonly in order to efficiently use such GPU resources, the workload in question must have a much greater degree of parallelism than workloads often associated with multicore processors (CPUs).  Available GPU variants differ in their internal architecture and, as a result, are capable of handling workloads of varying degrees of complexity and a range of computational patterns.  This vast array of applicable workloads will likely lead to an ever accelerated mixing of multicore-CPUs and GPUs in multi-user environments with the ultimate goal of offering adequate computing facilities for a wide range of scientific and technical workloads.  In the following paper, we present a research prototype that uses a lightweight runtime environment to manage the resource-specific workloads, and to control the dataflow and parallel execution in hybrid systems.  Our lightweight runtime environment uses task superscalar concepts to enable the developer to write serial code while providing parallel execution.  This concept is reminiscent of dataflow and systolic architectures in its conceptualization of a workload as a set of side-effect-free tasks that pass data items whenever the associated work assignment have been completed.  Additionally, our task abstractions and their parametrization enable uniformity in the algorithmic development across all the heterogeneous resources without sacrificing precious compute cycles.  We include performance results for dense linear algebra functions which demonstrate the practicality and effectiveness of our approach that is aptly capable of full utilization of a wide range of accelerator hardware.},
	keywords = {dense linear algebra, hardware accelerators, task superscalar scheduling},
	doi = {http://dx.doi.org/10.14529/jsfi1401},
	author = {Jack Dongarra and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov and Asim YarKhan}
}
@techreport {827,
	title = {PULSAR Users{\textquoteright} Guide, Parallel Ultra-Light Systolic Array Runtime},
	journal = {University of Tennessee EECS Technical Report},
	number = {UT-EECS-14-733},
	year = {2014},
	month = {2014-11},
	publisher = {University of Tennessee},
	abstract = {PULSAR version 2.0, released in November 2014, is a complete programming platform for large-scale distributed memory systems with multicore processors and hardware accelerators. PULSAR provides a simple abstraction layer over multithreading, message passing, and multi-GPU, multi-stream programming. PULSAR offers a general-purpose programming model, suitable for a wide range of scientific and engineering applications. PULSAR was inspired by systolic arrays, popularized by Hsiang-Tsung Kung and Charles E. Leiserson.},
	author = {Jack Dongarra and Jakub Kurzak and Piotr Luszczek and Ichitaro Yamazaki}
}
@conference {879,
	title = {Search Space Pruning Constraints Visualization},
	booktitle = {VISSOFT{\textquoteright}14: 2nd IEEE Working Conference on Software Visualization},
	year = {2014},
	month = {2014-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Victoria, BC, Canada},
	abstract = {The field of software optimization, among others, is interested in finding an optimal solution in a large search space. These search spaces are often large, complex, non-linear and even non-continuous at times. The size of the search space makes a brute force solution intractable. As a result, one or more search space pruning constraints are often used to reduce the number of candidate configurations that must be evaluated in order to solve the optimization problem.

If more than one pruning constraint is employed, it can be challenging to understand how the pruning constraints interact and overlap. This work presents a visualization technique based on a radial, space-filling technique that allows the user to gain a better understanding of how the pruning constraints remove candidates from the search space. The technique is then demonstrated using a search space pruning data set derived from the optimization of a matrix multiplication code for NVIDIA CUDA accelerators.},
	author = {Blake Haugen and Jakub Kurzak}
}
@conference {767,
	title = {A Step towards Energy Efficient Computing: Redesigning A Hydrodynamic Application on CPU-GPU},
	booktitle = {IPDPS 2014},
	year = {2014},
	month = {2014-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Phoenix, AZ},
	abstract = {Power and energy consumption are becoming an increasing concern in high performance computing. Compared to multi-core CPUs, GPUs have a much better performance per watt. In this paper we discuss efforts to redesign the most computation intensive parts of BLAST, an application that solves the equations for compressible hydrodynamics with high order finite elements, using GPUs [10, 1]. In order to exploit the hardware parallelism of GPUs and achieve high performance, we implemented custom linear algebra kernels. We intensively optimized our CUDA kernels by exploiting the memory hierarchy, which exceed the vendor{\textquoteright}s library routines substantially in performance. We proposed an autotuning technique to adapt our CUDA kernels to the orders of the finite element method. Compared to a previous base implementation, our redesign and optimization lowered the energy consumption of the GPU in two aspects: 60\% less time to solution and 10\% less power required. Compared to the CPU-only solution, our GPU accelerated BLAST obtained a 2:5x overall speedup and 1:42x energy efficiency (greenup) using 4th order (Q4) finite elements, and a 1:9x speedup and 1:27x greenup using 2nd order (Q2) finite elements.},
	keywords = {Computer science, CUDA, FEM, Finite element method, linear algebra, nVidia, Tesla K20},
	author = {Tingxing Dong and Veselin Dobrev and Tzanio Kolev and Robert Rieben and Stanimire Tomov and Jack Dongarra}
}
@conference {768,
	title = {Utilizing Dataflow-based Execution for Coupled Cluster Methods},
	booktitle = {2014 IEEE International Conference on Cluster Computing},
	number = {ICL-UT-14-02},
	year = {2014},
	month = {2014-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Madrid, Spain},
	abstract = {Computational chemistry comprises one of the driving forces of High Performance Computing. In particular, many-body methods, such as Coupled Cluster (CC) methods of the quantum chemistry package NWCHEM, are of particular interest for the applied chemistry community.

Harnessing large fractions of the processing power of modern large scale computing platforms has become increasingly difficult. With the increase in scale, complexity, and heterogeneity of modern platforms, traditional programming models fail to deliver the expected performance scalability. On our way to Exascale and with these extremely hybrid platforms, dataflow-based programming models may be the only viable way for achieving and maintaining computation at scale.

In this paper, we discuss a dataflow-based programming model and its applicability to NWCHEM{\textquoteright}s CC methods. Our dataflow version of the CC kernels breaks down the algorithm into fine-grained tasks with explicitly defined data dependencies. As a result, many of the traditional synchronization points can be eliminated, allowing for a dynamic reshaping of the execution based on the ongoing availability of computational resources. We build this experiment using PARSEC {\textendash} a task-based dataflow-driven execution engine {\textendash} that enables efficient task scheduling on distributed systems, providing a desirable portability layer for application developers.},
	author = {Heike McCraw and Anthony Danalis and George Bosilca and Jack Dongarra and Karol Kowalski and Theresa Windus}
}
@article {icl:698,
	title = {Dense Linear Algebra on Distributed Heterogeneous Hardware with a Symbolic DAG Approach},
	journal = {Scalable Computing and Communications: Theory and Practice},
	year = {2013},
	month = {2013-03},
	pages = {699-735},
	publisher = {John Wiley \& Sons},
	author = {George Bosilca and Aurelien Bouteiller and Anthony Danalis and Thomas Herault and Piotr Luszczek and Jack Dongarra},
	editor = {Samee Khan and Lin-Wang Wang and Albert Zomaya}
}
@techreport {690,
	title = {Hydrodynamic Computation with Hybrid Programming on CPU-GPU Clusters},
	journal = {University of Tennessee Computer Science Technical Report},
	number = {ut-cs-13-714},
	year = {2013},
	month = {2013-07},
	abstract = {The explosion of parallelism and heterogeneity in today{\textquoteright}s computer architectures has created opportunities as well as challenges for redesigning legacy numerical software to harness the power of new hardware. In this paper we address the main challenges in redesigning BLAST { a numerical library that solves the equations of compressible hydrodynamics using high order nite element methods (FEM) in a moving Lagrangian frame { to support CPU-GPU clusters. We use a hybrid MPI + OpenMP + CUDA programming model that includes two layers: domain decomposed MPI parallelization and OpenMP + CUDA acceleration in a given domain. To optimize the code, we implemented custom linear algebra kernels and introduced an auto-tuning technique to deal with heterogeneity and load balancing at runtime. Our tests show that 12 Intel Xeon cores and two M2050 GPUs deliver a 24x speedup compared to a single core, and a 2.5x speedup compared to 12 MPI tasks in one node. Further, we achieve perfect weak scaling, demonstrated on a cluster with up to 64 GPUs in 32 nodes. Our choice of programming model and proposed solutions, as related to parallelism and load balancing, specifically targets high order FEM discretizations, and can be used equally successfully for applications beyond hydrodynamics. A major accomplishment is that we further establish the appeal of high order FEMs, which despite their better approximation properties, are often avoided due to their high computational cost. GPUs, as we show, have the potential to make them the method of choice, as the increased computational cost is also localized, e.g., cast as Level 3 BLAS, and thus can be done very efficiently (close to \free" relative to the usual overheads inherent in sparse computations).},
	author = {Tingxing Dong and Veselin Dobrev and Tzanio Kolev and Robert Rieben and Stanimire Tomov and Jack Dongarra}
}
@techreport {682,
	title = {Implementing a systolic algorithm for QR factorization on multicore clusters with PaRSEC},
	journal = {Lawn 277},
	number = {UT-CS-13-709},
	year = {2013},
	month = {2013-05},
	abstract = {This article introduces a new systolic algorithm for QR factorization, and its implementation on a supercomputing cluster of multicore nodes. The algorithm targets a virtual 3D-array and requires only local communications. The implementation of the algorithm uses threads at the node level, and MPI for inter-node communications. The complexity of the implementation is addressed with the PaRSEC software, which takes as input a parametrized dependence graph, which is derived from the algorithm, and only requires the user to decide, at the high-level, the allocation of tasks to nodes. We show that the new algorithm exhibits competitive performance with state-of-the-art QR routines on a supercomputer called Kraken, which shows that high-level programming environments, such as PaRSEC, provide a viable alternative to enhance the production of quality software on complex and hierarchical architectures},
	author = {Guillaume Aupy and Mathieu Faverge and Yves Robert and Jakub Kurzak and Piotr Luszczek and Jack Dongarra}
}
@techreport {704,
	title = {An Improved Parallel Singular Value Algorithm and Its Implementation for Multicore Hardware},
	journal = {University of Tennessee Computer Science Technical Report (also LAWN 283)},
	number = {ut-eecs-13-720},
	year = {2013},
	month = {2013-10},
	publisher = {University of Tennessee},
	keywords = {lapack, plasma, scalapack},
	author = {Azzam Haidar and Piotr Luszczek and Jakub Kurzak and Jack Dongarra}
}
@conference {696,
	title = {An Improved Parallel Singular Value Algorithm and Its Implementation for Multicore Hardware},
	booktitle = {Supercomputing 2013},
	year = {2013},
	month = {2013-11},
	address = {Denver, CO},
	author = {Azzam Haidar and Piotr Luszczek and Jakub Kurzak and Jack Dongarra}
}
@article {icl:693,
	title = {LU Factorization with Partial Pivoting for a Multicore System with Accelerators},
	journal = {IEEE Transactions on Parallel and Distributed Computing},
	volume = {24},
	year = {2013},
	month = {2013-08},
	pages = {1613-1621},
	chapter = {1613},
	abstract = {LU factorization with partial pivoting is a canonical numerical procedure and the main component of the high performance LINPACK benchmark. This paper presents an implementation of the algorithm for a hybrid, shared memory, system with standard CPU cores and GPU accelerators. The difficulty of implementing the algorithm for such a system lies in the disproportion between the computational power of the CPUs, compared to the GPUs, and in the meager bandwidth of the communication link between their memory systems. An additional challenge comes from the complexity of the memory-bound and synchronization-rich nature of the panel factorization component of the block LU algorithm, imposed by the use of partial pivoting. The challenges are tackled with the use of a data layout geared toward complex memory hierarchies, autotuning of GPU kernels, fine-grain parallelization of memory-bound CPU operations and dynamic scheduling of tasks to different devices. Performance in excess of one TeraFLOPS is achieved using four AMD Magny Cours CPUs and four NVIDIA Fermi GPUs.},
	keywords = {accelerator, Gaussian elimination, gpu, lu factorization, manycore, Multicore, partial pivoting, plasma},
	doi = {http://doi.ieeecomputersociety.org/10.1109/TPDS.2012.242},
	author = {Jakub Kurzak and Piotr Luszczek and Jack Dongarra}
}
@article {icl:704,
	title = {Multithreading in the PLASMA Library},
	journal = {Multi and Many-Core Processing: Architecture, Programming, Algorithms, \& Applications},
	year = {2013},
	month = {2013-00},
	publisher = {Taylor \& Francis},
	keywords = {plasma},
	author = {Jakub Kurzak and Piotr Luszczek and Asim YarKhan and Mathieu Faverge and Julien Langou and Henricus Bouwmeester and Jack Dongarra},
	editor = {Mohamed Ahmed and Reda Ammar and Sanguthevar Rajasekaran}
}
@article {1382,
	title = {PAPI 5: Measuring Power, Energy, and the Cloud},
	year = {2013},
	month = {2013-04},
	publisher = {2013 IEEE International Symposium on Performance Analysis of Systems and Software},
	address = {Austin, TX},
	author = {Vincent Weaver and Dan Terpstra and Heike McCraw and Matt Johnson and Kiran Kasichayanula and James Ralph and John Nelson and Phil Mucci and Tushar Mohan and Shirley Moore}
}
@conference {753,
	title = {Portable HPC Programming on Intel Many-Integrated-Core Hardware with MAGMA Port to Xeon Phi},
	booktitle = {PPAM 2013},
	year = {2013},
	month = {2013-09},
	address = {Warsaw, Poland},
	abstract = {This paper presents the design and implementation of several fundamental dense linear algebra (DLA) algorithms for multicore with Intel Xeon Phi Coprocessors. In particular, we consider algorithms for solving linear systems. Further, we give an overview of the MAGMA MIC library, an open source, high performance library that incorporates the developments presented, and in general provides to heterogeneous architectures of multicore with coprocessors the DLA functionality of the popular LAPACK library. The LAPACK-compliance simplifies the use of the MAGMA MIC library in applications, while providing them with portably performant DLA. High performance is obtained through use of the high-performance BLAS, hardware-specific tuning, and a hybridization methodology where we split the algorithm into computational tasks of various granularities. Execution of those tasks is properly scheduled over the heterogeneous hardware components by minimizing data movements and mapping algorithmic requirements to the architectural strengths of the various heterogeneous hardware components. Our methodology and programming techniques are incorporated into the MAGMA MIC API, which abstracts the application developer from the specifics of the Xeon Phi architecture and is therefore applicable to algorithms beyond the scope of DLA.},
	keywords = {magma, mic, xeon phi},
	author = {Jack Dongarra and Mark Gates and Azzam Haidar and Yulu Jia and Khairul Kabir and Piotr Luszczek and Stanimire Tomov}
}
@inbook {695,
	title = {Scalable Dense Linear Algebra on Heterogeneous Hardware},
	booktitle = {HPC: Transition Towards Exascale Processing, in the series Advances in Parallel Computing},
	year = {2013},
	abstract = {Abstract. Design of systems exceeding 1 Pflop/s and the push toward 1 Eflop/s, forced a dramatic shift in hardware design. Various physical and engineering constraints resulted in introduction of massive parallelism and functional hybridization with the use of accelerator units. This paradigm change brings about a serious challenge for application developers, as the management of multicore proliferation and heterogeneity rests on software. And it is reasonable to expect, that this situation will not change in the foreseeable future. This chapter presents a methodology of dealing with this issue in three common scenarios. In the context of shared-memory multicore installations, we show how high performance and scalability go hand in hand, when the well-known linear algebra algorithms are recast in terms of Direct Acyclic Graphs (DAGs), which are then transparently scheduled at runtime inside the Parallel Linear Algebra Software for Multicore Architectures (PLASMA) project. Similarly, Matrix Algebra on GPU and Multicore Architectures (MAGMA) schedules DAG-driven computations on multicore processors and accelerators. Finally, Distributed PLASMA (DPLASMA), takes the approach to distributed-memory machines with the use of automatic dependence analysis and the Direct Acyclic Graph Engine (DAGuE) to deliver high performance at the scale of many thousands of cores.},
	author = {George Bosilca and Aurelien Bouteiller and Anthony Danalis and Thomas Herault and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov and Jack Dongarra}
}
@conference {761,
	title = {Standards for Graph Algorithm Primitives},
	booktitle = {17th IEEE High Performance Extreme Computing Conference (HPEC {\textquoteright}13)},
	year = {2013},
	month = {2013-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Waltham, MA},
	abstract = {It is our view that the state of the art in constructing a large collection of graph algorithms in terms of linear algebraic operations is mature enough to support the emergence of a standard set of primitive building blocks. This paper is a position paper defining the problem and announcing our intention to launch an open effort to define this standard.},
	keywords = {algorithms, graphs, linear algebra, software standards},
	doi = {10.1109/HPEC.2013.6670338},
	author = {Tim Mattson and David Bader and Jon Berry and Aydin Buluc and Jack Dongarra and Christos Faloutsos and John Feo and John Gilbert and Joseph Gonzalez and Bruce Hendrickson and Jeremy Kepner and Charles Lieserson and Andrew Lumsdaine and David Padua and Steve W. Poole and Steve Reinhardt and Mike Stonebraker and Steve Wallach and Andrew Yoo}
}
@conference {icl:692,
	title = {Virtual Systolic Array for QR Decomposition},
	booktitle = {15th Workshop on Advances in Parallel and Distributed Computational Models, IEEE International Parallel \& Distributed Processing Symposium (IPDPS 2013)},
	year = {2013},
	month = {2013-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Boston, MA},
	abstract = {Systolic arrays offer a very attractive, data-centric, execution model as an alternative to the von Neumann architecture. Hardware implementations of systolic arrays turned out not to be viable solutions in the past. This article shows how the systolic design principles can be applied to a software solution to deliver an algorithm with unprecedented strong scaling capabilities. Systolic array for the QR decomposition is developed and a virtualization layer is used for mapping of the algorithm to a large distributed memory system. Strong scaling properties are discovered, superior to existing solutions.},
	keywords = {dataflow programming, message passing, multi-core, QR decomposition, roofline model, systolic array},
	doi = {10.1109/IPDPS.2013.119},
	author = {Jakub Kurzak and Piotr Luszczek and Mark Gates and Ichitaro Yamazaki and Jack Dongarra}
}
@article {icl:731,
	title = {Acceleration of the BLAST Hydro Code on GPU},
	journal = {Supercomputing {\textquoteright}12 (poster)},
	year = {2012},
	month = {2012-11},
	publisher = {SC12},
	address = {Salt Lake City, Utah},
	author = {Tingxing Dong and Tzanio Kolev and Robert Rieben and Veselin Dobrev and Stanimire Tomov and Jack Dongarra}
}
@techreport {688,
	title = {On Algorithmic Variants of Parallel Gaussian Elimination: Comparison of Implementations in Terms of Performance and Numerical Properties},
	journal = {University of Tennessee Computer Science Technical Report},
	number = {UT-CS-13-715},
	year = {2012},
	month = {2013-07},
	abstract = {Gaussian elimination is a canonical linear algebra procedure for solving linear systems of equations. In the last few years, the algorithm received a lot of attention in an attempt to improve its parallel performance. This article surveys recent developments in parallel implementations of the Gaussian elimination. Five different flavors are investigated. Three of them are based on different strategies for pivoting: partial pivoting, incremental pivoting, and tournament pivoting. The fourth one replaces pivoting with the Random Butterfly Transformation, and finally, an implementation without pivoting is used as a performance baseline. The technique of iterative refinement is applied to recover numerical accuracy when necessary. All parallel implementations are produced using dynamic, superscalar, runtime scheduling and tile matrix layout. Results on two multi-socket multicore systems are presented. Performance and numerical accuracy is analyzed.},
	author = {Simplice Donfack and Jack Dongarra and Mathieu Faverge and Mark Gates and Jakub Kurzak and Piotr Luszczek and Ichitaro Yamazaki}
}
@article {,
	title = {Autotuning GEMM Kernels for the Fermi GPU},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	volume = {23},
	number = {11},
	year = {2012},
	month = {2012-11},
	abstract = {Abstract{\textemdash}In recent years, the use of graphics chips has been recognized as a viable way of accelerating scientific and engineering applications, even more so since the introduction of the Fermi architecture by NVIDIA, with features essential to numerical computing, such as fast double precision arithmetic and memory protected with error correction codes. Being the crucial component of numerical software packages, such as LAPACK and ScaLAPACK, the general dense matrix multiplication routine is one of the more important workloads to be implemented on these devices. This paper presents a methodology for producing matrix multiplication kernels tuned for a specific architecture, through a canonical process of heuristic autotuning, based on generation of multiple code variants and selecting the fastest ones through benchmarking. The key contribution of this work is in the method for generating the search space; specifically, pruning it to a manageable size. Performance numbers match or exceed other available implementations.},
	doi = {https://doi.org/10.1109/TPDS.2011.311},
	author = {Jakub Kurzak and Stanimire Tomov and Jack Dongarra}
}
@inproceedings {icl:679,
	title = {A Checkpoint-on-Failure Protocol for Algorithm-Based Recovery in Standard MPI},
	journal = {18th International European Conference on Parallel and Distributed Computing (Euro-Par 2012) (Best Paper Award)},
	year = {2012},
	month = {2012-08},
	publisher = {Springer-Verlag},
	address = {Rhodes, Greece},
	author = {Wesley Bland and Peng Du and Aurelien Bouteiller and Thomas Herault and George Bosilca and Jack Dongarra},
	editor = {Christos Kaklamanis and Theodore Papatheodorou and Paul Spirakis}
}
@article {icl:703,
	title = {Dense Linear Algebra on Accelerated Multicore Hardware},
	journal = {High Performance Scientific Computing: Algorithms and Applications},
	year = {2012},
	month = {2012-00},
	publisher = {Springer-Verlag},
	address = {London, UK},
	author = {Jack Dongarra and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov},
	editor = {Michael Berry and et al.,}
}
@techreport {icl:717,
	title = {How LAPACK library enables Microsoft Visual Studio support with CMake and LAPACKE},
	journal = {University of Tennessee Computer Science Technical Report (also LAWN 270)},
	number = {UT-CS-12-698},
	year = {2012},
	month = {2012-07},
	author = {Julien Langou and Bill Hoffman and Brad King}
}
@article {icl:690,
	title = {An Implementation of the Tile QR Factorization for a GPU and Multiple CPUs},
	journal = {Applied Parallel and Scientific Computing},
	volume = {7133},
	year = {2012},
	month = {2012-00},
	pages = {248-257},
	author = {Jakub Kurzak and Rajib Nath and Peng Du and Jack Dongarra},
	editor = {Kristj{\'a}n J{\'o}nasson}
}
@article {icl:705,
	title = {Looking Back at Dense Linear Algebra Software},
	journal = {Perspectives on Parallel and Distributed Processing: Looking Back and What{\textquoteright}s Ahead (to appear)},
	year = {2012},
	month = {2012-00},
	author = {Piotr Luszczek and Jakub Kurzak and Jack Dongarra},
	editor = {Viktor K. Prasanna and Yves Robert and Per Stenstr{\"o}m}
}
@article {1354,
	title = {MAGMA MIC: Linear Algebra Library for Intel Xeon Phi Coprocessors},
	year = {2012},
	month = {2012-11},
	publisher = {The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC12)},
	address = {Salt Lake City, UT},
	author = {Jack Dongarra and Mark Gates and Yulu Jia and Khairul Kabir and Piotr Luszczek and Stanimire Tomov}
}
@inproceedings {icl:689,
	title = {Measuring Energy and Power with PAPI},
	journal = {International Workshop on Power-Aware Systems and Architectures},
	year = {2012},
	month = {2012-09},
	address = {Pittsburgh, PA},
	abstract = {Energy and power consumption are becoming critical metrics in the design and usage of high performance systems. We have extended the Performance API (PAPI) analysis library to measure and report energy and power values. These values are reported using the existing PAPI API, allowing code previously instrumented for performance counters to also measure power and energy. Higher level tools that build on PAPI will automatically gain support for power and energy readings when used with the newest version of PAPI. We describe in detail the types of energy and power readings available through PAPI. We support external power meters, as well as values provided internally by recent CPUs and GPUs. Measurements are provided directly to the instrumented process, allowing immediate code analysis in real time. We provide examples showing results that can be obtained with our infrastructure.},
	keywords = {papi},
	doi = {10.1109/ICPPW.2012.39},
	author = {Vincent M Weaver and Matt Johnson and Kiran Kasichayanula and James Ralph and Piotr Luszczek and Dan Terpstra and Shirley Moore}
}
@article {icl:709,
	title = {Optimizing Memory-Bound Numerical Kernels on GPU Hardware Accelerators},
	journal = {VECPAR 2012},
	year = {2012},
	month = {2012-07},
	address = {Kobe, Japan},
	author = {Ahmad Abdelfattah and Jack Dongarra and David Keyes and Hatem Ltaeif}
}
@article {icl:727,
	title = {Parallel Processing and Applied Mathematics, 9th International Conference, PPAM 2011},
	journal = {Lecture Notes in Computer Science},
	volume = {7203},
	year = {2012},
	month = {2012-00},
	address = {Torun, Poland},
	editor = {Roman Wyrzykowski and Jack Dongarra and Konrad Karczewski and Jerzy Wasniewski}
}
@article {icl:686,
	title = {Power Aware Computing on GPUs},
	journal = {SAAHPC {\textquoteright}12 (Best Paper Award)},
	year = {2012},
	month = {2012-07},
	address = {Argonne, IL},
	keywords = {magma},
	author = {Kiran Kasichayanula and Dan Terpstra and Piotr Luszczek and Stanimire Tomov and Shirley Moore and Gregory D. Peterson}
}
@article {icl:718,
	title = {Preliminary Results of Autotuning GEMM Kernels for the NVIDIA Kepler Architecture},
	journal = {LAWN 267},
	year = {2012},
	month = {2012-00},
	author = {Jakub Kurzak and Piotr Luszczek and Stanimire Tomov and Jack Dongarra}
}
@inproceedings {icl:665,
	title = {Programming the LU Factorization for a Multicore System with Accelerators},
	journal = {Proceedings of VECPAR{\textquoteright}12},
	year = {2012},
	month = {2012-04},
	address = {Kobe, Japan},
	keywords = {plasma, quark},
	author = {Jakub Kurzak and Piotr Luszczek and Mathieu Faverge and Jack Dongarra}
}
@article {icl:722,
	title = {Reducing the Amount of Pivoting in Symmetric Indefinite Systems},
	journal = {Parallel Processing and Applied Mathematics, Lecture Notes in Computer Science (PPAM 2011)},
	volume = {7203},
	year = {2012},
	month = {2012-00},
	pages = {133-142},
	publisher = {Springer-Verlag Berlin Heidelberg},
	author = {Dulceneia Becker and Marc Baboulin and Jack Dongarra},
	editor = {Roman Wyrzykowski and Jack Dongarra and Konrad Karczewski and Jerzy Wasniewski}
}
@inproceedings {icl:736,
	title = {User Level Failure Mitigation in MPI},
	journal = {Euro-Par 2012: Parallel Processing Workshops},
	volume = {7640},
	year = {2012},
	month = {2012-08},
	pages = {499-504},
	publisher = {Springer Berlin Heidelberg},
	address = {Rhodes Island, Greece},
	keywords = {ftmpi},
	author = {Wesley Bland},
	editor = {Ioannis Caragiannis and Michael Alexander and Rosa M. Badia and Mario Cannataro and Alexandru Costan and Marco Danelutto and Frederic Desprez and Bettina Krammer and Sahuquillo, J. and Stephen L. Scott and J. Weidendorfer}
}
@techreport {icl:630,
	title = {Autotuning GEMMs for Fermi},
	journal = {University of Tennessee Computer Science Technical Report, UT-CS-11-671, (also Lawn 245)},
	year = {2011},
	month = {2011-04},
	keywords = {magma},
	author = {Jakub Kurzak and Stanimire Tomov and Jack Dongarra}
}
@article {icl:587,
	title = {Changes in Dense Linear Algebra Kernels - Decades Long Perspective},
	journal = {in Solving the Schrodinger Equation: Has everything been tried? (to appear)},
	year = {2011},
	month = {2011-00},
	publisher = {Imperial College Press},
	author = {Piotr Luszczek and Jakub Kurzak and Jack Dongarra},
	editor = {P. Popular}
}
@inproceedings {icl:676,
	title = {Flexible Development of Dense Linear Algebra Algorithms on Massively Parallel Architectures with DPLASMA},
	journal = {Proceedings of the Workshops of the 25th IEEE International Symposium on Parallel and Distributed Processing (IPDPS 2011 Workshops)},
	year = {2011},
	month = {2011-05},
	pages = {1432-1441},
	publisher = {IEEE},
	address = {Anchorage, Alaska, USA},
	keywords = {dague, dplasma, parsec},
	author = {George Bosilca and Aurelien Bouteiller and Anthony Danalis and Mathieu Faverge and Azzam Haidar and Thomas Herault and Jakub Kurzak and Julien Langou and Pierre Lemariner and Hatem Ltaeif and Piotr Luszczek and Asim YarKhan and Jack Dongarra}
}
@article {icl:643,
	title = {The International Exascale Software Project Roadmap},
	journal = {International Journal of High Performance Computing},
	volume = {25},
	number = {1},
	year = {2011},
	month = {2011-01},
	pages = {3-60},
	abstract = {Over the last 20 years, the open-source community has provided more and more software on which the world{\textquoteright}s high-performance computing systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. However, although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual petascale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/ exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and graphics processing units. This report describes the work of the community to prepare for the challenges of exascale computing, ultimately combing their efforts in a coordinated International Exascale Software Project.},
	doi = {https://doi.org/10.1177/1094342010391989},
	author = {Jack Dongarra and Pete Beckman and Terry Moore and Patrick Aerts and Giovanni Aloisio and Jean-Claude Andre and David Barkai and Jean-Yves Berthou and Taisuke Boku and Bertrand Braunschweig and Franck Cappello and Barbara Chapman and Xuebin Chi and Alok Choudhary and Sudip Dosanjh and Thom Dunning and Sandro Fiore and Al Geist and Bill Gropp and Robert Harrison and Mark Hereld and Michael Heroux and Adolfy Hoisie and Koh Hotta and Zhong Jin and Yutaka Ishikawa and Fred Johnson and Sanjay Kale and Richard Kenway and David Keyes and Bill Kramer and Jesus Labarta and Alain Lichnewsky and Thomas Lippert and Bob Lucas and Barney MacCabe and Satoshi Matsuoka and Paul Messina and Peter Michielse and Bernd Mohr and Matthias S. Mueller and Wolfgang E. Nagel and Hiroshi Nakashima and Michael E. Papka and Dan Reed and Mitsuhisa Sato and Ed Seidel and John Shalf and David Skinner and Marc Snir and Thomas Sterling and Rick Stevens and Fred Streitz and Bob Sugar and Shinji Sumimoto and William Tang and John Taylor and Rajeev Thakur and Anne Trefethen and Mateo Valero and Aad van der Steen and Jeffrey Vetter and Peg Williams and Robert Wisniewski and Kathy Yelick}
}
@article {icl:647,
	title = {OMPIO: A Modular Software Architecture for MPI I/O},
	journal = {18th EuroMPI},
	year = {2011},
	month = {2011-09},
	pages = {81-89},
	publisher = {Springer},
	address = {Santorini, Greece},
	author = {Mohamad Chaarawi and Edgar Gabriel and Rainer Keller and Richard L. Graham and George Bosilca and Jack Dongarra},
	editor = {Yiannis Cotronis and Anthony Danalis and Dimitrios S. Nikolopoulos and Jack Dongarra}
}
@article {1361,
	title = {Power-aware Computing on GPGPUs},
	year = {2011},
	month = {2011-09},
	publisher = {Fall Creek Falls Conference, Poster},
	address = {Gatlinburg, TN},
	author = {Kiran Kasichayanula and Haihang You and Shirley Moore and Stanimire Tomov and Heike Jagode and Matt Johnson}
}
@techreport {icl:609,
	title = {QUARK Users{\textquoteright} Guide: QUeueing And Runtime for Kernels},
	journal = {University of Tennessee Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-11-02},
	year = {2011},
	month = {2011-00},
	keywords = {magma, plasma, quark},
	author = {Asim YarKhan and Jakub Kurzak and Jack Dongarra}
}
@inproceedings {icl:580,
	title = {8th International Conference on Parallel Processing and Applied Mathematics, Lecture Notes in Computer Science (LNCS)},
	journal = {PPAM 2009 Proceedings},
	volume = {6067},
	year = {2010},
	month = {2010-09},
	publisher = {Springer},
	address = {Wroclaw, Poland},
	editor = {Roman Wyrzykowski and Jack Dongarra and Konrad Karczewski and Jerzy Wasniewski}
}
@article {icl:572,
	title = {A Class of Parallel Tiled Linear Algebra Algorithms for Multicore Architectures},
	journal = {Parallel Computing (to appear)},
	year = {2010},
	month = {2010-00},
	author = {Alfredo Buttari and Julien Langou and Jakub Kurzak and Jack Dongarra}
}
@techreport {icl:563,
	title = {Distributed Dense Numerical Linear Algebra Algorithms on Massively Parallel Architectures: DPLASMA},
	journal = {University of Tennessee Computer Science Technical Report, UT-CS-10-660},
	year = {2010},
	month = {2010-09},
	keywords = {dague, dplasma, parsec, plasma},
	author = {George Bosilca and Aurelien Bouteiller and Anthony Danalis and Mathieu Faverge and Azzam Haidar and Thomas Herault and Jakub Kurzak and Julien Langou and Pierre Lemariner and Hatem Ltaeif and Piotr Luszczek and Asim YarKhan and Jack Dongarra}
}
@techreport {icl:529,
	title = {Distributed-Memory Task Execution and Dependence Tracking within DAGuE and the DPLASMA Project},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-10-02},
	year = {2010},
	month = {2010-00},
	keywords = {dague, plasma},
	author = {George Bosilca and Aurelien Bouteiller and Anthony Danalis and Mathieu Faverge and Azzam Haidar and Thomas Herault and Jakub Kurzak and Julien Langou and Pierre Lemariner and Hatem Ltaeif and Piotr Luszczek and Asim YarKhan and Jack Dongarra}
}
@inproceedings {icl:534,
	title = {Dodging the Cost of Unavoidable Memory Copies in Message Logging Protocols},
	journal = {Proceedings of EuroMPI 2010},
	year = {2010},
	month = {2010-09},
	publisher = {Springer},
	address = {Stuttgart, Germany},
	keywords = {ftmpi},
	author = {George Bosilca and Aurelien Bouteiller and Thomas Herault and Pierre Lemariner and Jack Dongarra},
	editor = {Jack Dongarra and Michael Resch and Rainer Keller and Edgar Gabriel}
}
@article {icl:569,
	title = {Parallel Band Two-Sided Matrix Bidiagonalization for Multicore Architectures},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	year = {2010},
	month = {2010-04},
	pages = {417-423},
	author = {Hatem Ltaeif and Jakub Kurzak and Jack Dongarra}
}
@article {icl:571,
	title = {QR Factorization for the CELL Processor},
	journal = {Scientific Programming},
	volume = {17},
	number = {1-2},
	year = {2010},
	month = {2010-00},
	pages = {31-42},
	author = {Jakub Kurzak and Jack Dongarra}
}
@inproceedings {icl:581,
	title = {Recent Advances in the Message Passing Interface, Lecture Notes in Computer Science (LNCS)},
	journal = {EuroMPI 2010 Proceedings},
	volume = {6305},
	year = {2010},
	month = {2010-09},
	publisher = {Springer},
	address = {Stuttgart, Germany},
	editor = {Rainer Keller and Edgar Gabriel and Michael Resch and Jack Dongarra}
}
@article {icl:540,
	title = {Scheduling Dense Linear Algebra Operations on Multicore Processors},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {22},
	number = {1},
	year = {2010},
	month = {2010-01},
	pages = {15-44},
	keywords = {gridpac, plasma},
	author = {Jakub Kurzak and Hatem Ltaeif and Jack Dongarra and Rosa M. Badia}
}
@article {icl:473,
	title = {Scheduling Two-sided Transformations using Tile Algorithms on Multicore Architectures},
	journal = {Journal of Scientific Computing},
	volume = {18},
	number = {1},
	year = {2010},
	month = {2010-00},
	pages = {33-50},
	keywords = {plasma},
	author = {Hatem Ltaeif and Jakub Kurzak and Jack Dongarra and Rosa M. Badia}
}
@article {icl:576,
	title = {Trace-based Performance Analysis for the Petascale Simulation Code FLASH},
	journal = {International Journal of High Performance Computing Applications (to appear)},
	year = {2010},
	month = {2010-00},
	author = {Heike Jagode and Andreas Knuepfer and Jack Dongarra and Matthias Jurenz and Matthias S. Mueller and Wolfgang E. Nagel}
}
@article {,
	title = {Accelerating Scientific Computations with Mixed Precision Algorithms},
	journal = {Computer Physics Communications},
	volume = {180},
	year = {2009},
	month = {2009-12},
	pages = {2526-2533},
	abstract = {On modern architectures, the performance of 32-bit operations is often at least twice as fast as the performance of 64-bit operations. By using a combination of 32-bit and 64-bit floating point arithmetic, the performance of many dense and sparse linear algebra algorithms can be significantly enhanced while maintaining the 64-bit accuracy of the resulting solution. The approach presented here can apply not only to conventional processors but also to other technologies such as Field Programmable Gate Arrays (FPGA), Graphical Processing Units (GPU), and the STI Cell BE processor. Results on modern processor architectures and the STI Cell BE are presented.},
	doi = {https://doi.org/10.1016/j.cpc.2008.11.005},
	author = {Marc Baboulin and Alfredo Buttari and Jack Dongarra and Jakub Kurzak and Julie Langou and Julien Langou and Piotr Luszczek and Stanimire Tomov}
}
@article {icl:509,
	title = {A Class of Parallel Tiled Linear Algebra Algorithms for Multicore Architectures},
	journal = {Parallel Computing},
	volume = {35},
	year = {2009},
	month = {2009-00},
	pages = {38-53},
	keywords = {plasma},
	author = {Alfredo Buttari and Julien Langou and Jakub Kurzak and Jack Dongarra}
}
@article {icl:518,
	title = {Dependency-Driven Scheduling of Dense Matrix Factorizations on Shared-Memory Systems},
	journal = {PPAM 2009},
	year = {2009},
	month = {2009-09},
	address = {Poland},
	author = {Jakub Kurzak and Hatem Ltaeif and Jack Dongarra and Rosa M. Badia}
}
@techreport {icl:494,
	title = {Fully Dynamic Scheduler for Numerical Computing on Multicore Processors},
	journal = {University of Tennessee Computer Science Department Technical Report, UT-CS-09-643 (Also LAPACK Working Note 220)},
	year = {2009},
	month = {2009-00},
	author = {Jakub Kurzak and Jack Dongarra}
}
@article {icl:480,
	title = {Impact of Quad-core Cray XT4 System and Software Stack on Scientific Computation},
	journal = {Euro-Par 2009, Lecture Notes in Computer Science},
	volume = {5704/2009},
	year = {2009},
	month = {2009-08},
	pages = {334-344},
	publisher = {Springer Berlin / Heidelberg},
	address = {Delft, The Netherlands},
	keywords = {test},
	author = {Sadaf Alam and Richard F. Barrett and Heike Jagode and J. A. Kuehn and Steve W. Poole and R. Sankaran}
}
@article {icl:497,
	title = {I/O Performance Analysis for the Petascale Simulation Code FLASH},
	journal = {ISC{\textquoteright}09},
	year = {2009},
	month = {2009-06},
	address = {Hamburg, Germany},
	keywords = {test},
	author = {Heike Jagode and Shirley Moore and Dan Terpstra and Jack Dongarra and Andreas Knuepfer and Matthias Jurenz and Matthias S. Mueller and Wolfgang E. Nagel}
}
@inproceedings {icl:602,
	title = {Modeling the Office of Science Ten Year Facilities Plan: The PERI Architecture Tiger Team},
	journal = {SciDAC 2009, Journal of Physics: Conference Series},
	volume = {180(2009)012039},
	year = {2009},
	month = {2009-07},
	publisher = {IOP Publishing},
	address = {San Diego, California},
	keywords = {test},
	author = {Bronis R. de Supinski and Sadaf Alam and David Bailey and Laura Carrington and Chris Daley and Anshu Dubey and Todd Gamblin and Dan Gunter and Paul D. Hovland and Heike Jagode and Karen Karavanic and Gabriel Marin and John Mellor-Crummey and Shirley Moore and Boyana Norris and Leonid Oliker and Catherine Olschanowsky and Philip C. Roth and Martin Schulz and Sameer Shende and Allan Snavely}
}
@article {1352,
	title = {Numerical Linear Algebra on Emerging Architectures: The PLASMA and MAGMA Projects},
	year = {2009},
	month = {2009-11},
	publisher = {The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC09)},
	address = {Portland, OR},
	author = {Emmanuel Agullo and James Demmel and Jack Dongarra and Bilel Hadri and Jakub Kurzak and Julien Langou and Hatem Ltaeif and Piotr Luszczek and Rajib Nath and Stanimire Tomov and Asim YarKhan and Vasily Volkov}
}
@inproceedings {icl:486,
	title = {Numerical Linear Algebra on Emerging Architectures: The PLASMA and MAGMA Projects},
	journal = {Journal of Physics: Conference Series},
	volume = {180},
	year = {2009},
	month = {2009-00},
	keywords = {magma, plasma},
	author = {Emmanuel Agullo and James Demmel and Jack Dongarra and Bilel Hadri and Jakub Kurzak and Julien Langou and Hatem Ltaeif and Piotr Luszczek and Stanimire Tomov}
}
@article {icl:492,
	title = {Optimizing Matrix Multiplication for a Short-Vector SIMD Architecture - CELL Processor},
	journal = {Parallel Computing},
	volume = {35},
	year = {2009},
	month = {2009-00},
	pages = {138-150},
	author = {Wesley Alvaro and Jakub Kurzak and Jack Dongarra}
}
@article {icl:489,
	title = {Parallel Band Two-Sided Matrix Bidiagonalization for Multicore Architectures},
	journal = {IEEE Transactions on Parallel and Distributed Systems (to appear)},
	year = {2009},
	month = {2009-05},
	author = {Hatem Ltaeif and Jakub Kurzak and Jack Dongarra}
}
@article {icl:505,
	title = {Parallel Dense Linear Algebra Software in the Multicore Era},
	journal = {in Cyberinfrastructure Technologies and Applications},
	year = {2009},
	month = {2009-00},
	pages = {9-24},
	publisher = {Nova Science Publishers, Inc.},
	keywords = {plasma},
	author = {Alfredo Buttari and Jack Dongarra and Jakub Kurzak and Julien Langou},
	editor = {Junwei Cao}
}
@article {icl:508,
	title = {QR Factorization for the CELL Processor},
	journal = {Scientific Programming (to appear)},
	year = {2009},
	month = {2009-00},
	keywords = {plasma},
	author = {Jakub Kurzak and Jack Dongarra}
}
@techreport {icl:495,
	title = {Scheduling Linear Algebra Operations on Multicore Processors},
	journal = {University of Tennessee Computer Science Department Technical Report, UT-CS-09-636 (Also LAPACK Working Note 213)},
	year = {2009},
	month = {2009-00},
	author = {Jakub Kurzak and Hatem Ltaeif and Jack Dongarra and Rosa M. Badia}
}
@article {icl:510,
	title = {Scheduling Linear Algebra Operations on Multicore Processors},
	journal = {Concurrency Practice and Experience (to appear)},
	year = {2009},
	month = {2009-00},
	keywords = {plasma},
	author = {Jakub Kurzak and Hatem Ltaeif and Jack Dongarra and Rosa M. Badia}
}
@techreport {icl:475,
	title = {Trace-based Performance Analysis for the Petascale Simulation Code FLASH},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-09-01},
	year = {2009},
	month = {2009-04},
	keywords = {test},
	author = {Heike Jagode and Andreas Knuepfer and Jack Dongarra and Matthias Jurenz and Matthias S. Mueller and Wolfgang E. Nagel}
}
@inproceedings {icl:519,
	title = {VGrADS: Enabling e-Science Workflows on Grids and Clouds with Fault Tolerance},
	journal = {SC{\textquoteright}09 The International Conference for High Performance Computing, Networking, Storage and Analysis (to appear)},
	year = {2009},
	month = {2009-00},
	address = {Portland, OR},
	keywords = {grads},
	author = {Lavanya Ramakrishan and Daniel Nurmi and Anirban Mandal and Charles Koelbel and Dennis Gannon and Mark Huang and Yang-Suk Kee and Graziano Obertelli and Kiran Thyagaraja and Rich Wolski and Asim YarKhan and Dmitrii Zagorodnov}
}
@inproceedings {icl:452,
	journal = {7th International parallel Processing and Applied Mathematics Conference, Lecture Notes in Comptuer Science},
	volume = {4967},
	year = {2008},
	month = {2008-01},
	publisher = {Springer Berlin},
	address = {Gdansk, Poland},
	editor = {Roman Wyrzykowski and Jack Dongarra and Konrad Karczewski and Jerzy Wasniewski}
}
@article {icl:454,
	journal = {15th European PVM/MPI Users{\textquoteright} Group Meeting, Recent Advances in Parallel Virtual Machine and Message Passing Interface, Lecture Notes in Computer Science},
	volume = {5205},
	year = {2008},
	month = {2008-01},
	publisher = {Springer Berlin},
	address = {Dublin Ireland},
	editor = {Alexey Lastovetsky and Tahar Kechadi and Jack Dongarra}
}
@article {icl:451,
	title = {DARPA{\textquoteright}s HPCS Program: History, Models, Tools, Languages},
	journal = {in Advances in Computers},
	volume = {72},
	year = {2008},
	month = {2008-01},
	publisher = {Elsevier},
	author = {Jack Dongarra and Robert Graybill and William Harrod and Robert Lucas and Ewing Lusk and Piotr Luszczek and Janice McMahon and Allan Snavely and Jeffrey Vetter and Katherine Yelick and Sadaf Alam and Roy Campbell and Laura Carrington and Tzu-Yi Chen and Omid Khalili and Jeremy Meredith and Mustafa Tikir},
	editor = {M. Zelkowitz}
}
@article {icl:449,
	title = {Exploiting Mixed Precision Floating Point Hardware in Scientific Computations},
	journal = {in High Performance Computing and Grids in Action},
	year = {2008},
	month = {2008-01},
	publisher = {IOS Press},
	address = {Amsterdam},
	author = {Alfredo Buttari and Jack Dongarra and Jakub Kurzak and Julien Langou and Julien Langou and Piotr Luszczek and Stanimire Tomov},
	editor = {Lucio Grandinetti}
}
@techreport {icl:405,
	title = {Fast and Small Short Vector SIMD Matrix Multiplication Kernels for the CELL Processor},
	journal = {University of Tennessee Computer Science Technical Report},
	number = {UT-CS-08-609, (also LAPACK Working Note 189)},
	year = {2008},
	month = {2008-01},
	keywords = {plasma},
	author = {Wesley Alvaro and Jakub Kurzak and Jack Dongarra}
}
@techreport {icl:431,
	title = {Parallel Block Hessenberg Reduction using Algorithms-By-Tiles for Multicore Architectures Revisited},
	journal = {University of Tennessee Computer Science Technical Report, UT-CS-08-624 (also LAPACK Working Note 208)},
	year = {2008},
	month = {2008-08},
	keywords = {plasma},
	author = {Hatem Ltaeif and Jakub Kurzak and Jack Dongarra}
}
@article {icl:446,
	title = {Parallel Tiled QR Factorization for Multicore Architectures},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {20},
	year = {2008},
	month = {2008-01},
	pages = {1573-1590},
	author = {Alfredo Buttari and Julien Langou and Jakub Kurzak and Jack Dongarra}
}
@article {icl:444,
	title = {The PlayStation 3 for High Performance Scientific Computing},
	journal = {Computing in Science and Engineering},
	year = {2008},
	month = {2008-01},
	pages = {80-83},
	author = {Jakub Kurzak and Alfredo Buttari and Piotr Luszczek and Jack Dongarra}
}
@techreport {icl:406,
	title = {The PlayStation 3 for High Performance Scientific Computing},
	journal = {University of Tennessee Computer Science Technical Report},
	number = {UT-CS-08-608},
	year = {2008},
	month = {2008-01},
	author = {Jakub Kurzak and Alfredo Buttari and Piotr Luszczek and Jack Dongarra}
}
@techreport {icl:421,
	title = {QR Factorization for the CELL Processor},
	journal = {University of Tennessee Computer Science Technical Report, UT-CS-08-616 (also LAPACK Working Note 201)},
	year = {2008},
	month = {2008-05},
	keywords = {plasma},
	author = {Jakub Kurzak and Jack Dongarra}
}
@article {icl:445,
	title = {Solving Systems of Linear Equations on the CELL Processor Using Cholesky Factorization},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	volume = {19},
	number = {9},
	year = {2008},
	month = {2008-01},
	pages = {1-11},
	author = {Jakub Kurzak and Alfredo Buttari and Jack Dongarra}
}
@inproceedings {icl:412,
	title = {Usage of the Scalasca Toolset for Scalable Performance Analysis of Large-scale Parallel Applications},
	journal = {Proceedings of the 2nd International Workshop on Tools for High Performance Computing},
	year = {2008},
	month = {2008-01},
	pages = {157-167},
	publisher = {Springer},
	address = {Stuttgart, Germany},
	keywords = {point},
	author = {Felix Wolf and Brian Wylie and Erika Abraham and Wolfgang Frings and Karl F{\"u}rlinger and Markus Geimer and Marc-Andre Hermanns and Bernd Mohr and Shirley Moore and Matthias Pfeifer},
	editor = {Michael Resch and Rainer Keller and Valentin Himmler and Bettina Krammer and A Schulz}
}
@article {icl:424,
	title = {Using Mixed Precision for Sparse Matrix Computations to Enhance the Performance while Achieving 64-bit Accuracy},
	journal = {ACM Transactions on Mathematical Software},
	volume = {34},
	number = {4},
	year = {2008},
	month = {2008-00},
	pages = {17-22},
	keywords = {plasma},
	author = {Alfredo Buttari and Jack Dongarra and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov}
}
@techreport {icl:375,
	title = {A Class of Parallel Tiled Linear Algebra Algorithms for Multicore Architectures},
	journal = {University of Tennessee Computer Science Technical Report},
	number = {UT-CS-07-600 (also LAPACK Working Note 191)},
	year = {2007},
	month = {2007-01},
	keywords = {plasma},
	author = {Alfredo Buttari and Julien Langou and Jakub Kurzak and Jack Dongarra}
}
@article {icl:392,
	title = {Exploiting Mixed Precision Floating Point Hardware in Scientific Computations},
	journal = {In High Performance Computing and Grids in Action (to appear)},
	year = {2007},
	month = {2007-00},
	publisher = {IOS Press},
	address = {Amsterdam},
	author = {Alfredo Buttari and Jack Dongarra and Jakub Kurzak and Julien Langou and Julie Langou and Piotr Luszczek and Stanimire Tomov},
	editor = {Lucio Grandinetti}
}
@article {,
	title = {The Impact of Multicore on Computational Science Software},
	journal = {CTWatch Quarterly},
	volume = {3},
	year = {2007},
	month = {2007-02},
	author = {Jack Dongarra and Dennis Gannon and Geoffrey Fox and Ken Kennedy}
}
@article {icl:399,
	title = {Implementation of Mixed Precision in Solving Systems of Linear Equations on the Cell Processor},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {19},
	number = {10},
	year = {2007},
	month = {2007-07},
	pages = {1371-1385},
	author = {Jakub Kurzak and Jack Dongarra}
}
@techreport {icl:404,
	title = {Limitations of the Playstation 3 for High Performance Cluster Computing},
	journal = {University of Tennessee Computer Science Technical Report, UT-CS-07-597 (Also LAPACK Working Note 185)},
	year = {2007},
	month = {2007-00},
	author = {Alfredo Buttari and Jack Dongarra and Jakub Kurzak}
}
@article {icl:395,
	title = {Mixed Precision Iterative Refinement Techniques for the Solution of Dense Linear Systems},
	journal = {International Journal of High Performance Computer Applications (to appear)},
	year = {2007},
	month = {2007-08},
	author = {Alfredo Buttari and Jack Dongarra and Julien Langou and Julie Langou and Piotr Luszczek and Jakub Kurzak}
}
@inproceedings {icl:388,
	title = {Multithreading for synchronization tolerance in matrix factorization},
	journal = {Journal of Physics: Conference Series, SciDAC 2007},
	volume = {78},
	number = {2007},
	year = {2007},
	month = {2007-01},
	author = {Alfredo Buttari and Jack Dongarra and Parry Husbands and Jakub Kurzak and Katherine Yelick}
}
@techreport {icl:363,
	title = {Parallel Tiled QR Factorization for Multicore Architectures},
	journal = {University of Tennessee Computer Science Dept. Technical Report, UT-CS-07-598 (also LAPACK Working Note 190)},
	year = {2007},
	month = {2007-00},
	keywords = {plasma},
	author = {Alfredo Buttari and Julien Langou and Jakub Kurzak and Jack Dongarra}
}
@techreport {icl:364,
	title = {SCOP3: A Rough Guide to Scientific Computing On the PlayStation 3},
	journal = {University of Tennessee Computer Science Dept. Technical Report, UT-CS-07-595},
	year = {2007},
	month = {2007-00},
	keywords = {multi-core},
	author = {Alfredo Buttari and Piotr Luszczek and Jakub Kurzak and Jack Dongarra and George Bosilca}
}
@techreport {icl:341,
	title = {Solving Systems of Linear Equations on the CELL Processor Using Cholesky Factorization},
	journal = {UT Computer Science Technical Report (Also LAPACK Working Note 184)},
	number = {UT-CS-07-596},
	year = {2007},
	month = {2007-01},
	keywords = {lapack},
	author = {Jakub Kurzak and Alfredo Buttari and Jack Dongarra}
}
@article {icl:333,
	title = {Application of Machine Learning to the Selection of Sparse Linear Solvers},
	journal = {International Journal of High Performance Computing Applications (submitted)},
	year = {2006},
	month = {2006-00},
	keywords = {salsa, sans},
	author = {Sanjukta Bhowmick and Victor Eijkhout and Yoav Freund and Erika Fuentes and David Keyes}
}
@article {icl:317,
	title = {Exploiting the Performance of 32 bit Floating Point Arithmetic in Obtaining 64 bit Accuracy},
	journal = {University of Tennessee Computer Science Tech Report},
	number = {UT-CS-06-574, LAPACK Working Note $\#$175},
	year = {2006},
	month = {2006-04},
	keywords = {iter-ref},
	author = {Julien Langou and Julien Langou and Piotr Luszczek and Jakub Kurzak and Alfredo Buttari and Jack Dongarra}
}
@inproceedings {icl:337,
	title = {The HPC Challenge (HPCC) Benchmark Suite},
	journal = {SC06 Conference Tutorial},
	year = {2006},
	month = {2006-11},
	publisher = {IEEE},
	address = {Tampa, Florida},
	keywords = {hpcc, hpcchallenge},
	author = {Piotr Luszczek and David Bailey and Jack Dongarra and Jeremy Kepner and Robert Lucas and Rolf Rabenseifner and Daisuke Takahashi}
}
@article {icl:369,
	title = {The Impact of Multicore on Math Software},
	journal = {PARA 2006},
	year = {2006},
	month = {2006-06},
	address = {Umea, Sweden},
	keywords = {plasma},
	author = {Alfredo Buttari and Jack Dongarra and Jakub Kurzak and Julien Langou and Piotr Luszczek and Stanimire Tomov}
}
@article {icl:650,
	title = {Implementation and Usage of the PERUSE-Interface in Open MPI},
	journal = {Euro PVM/MPI 2006},
	year = {2006},
	month = {2006-09},
	address = {Bonn, Germany},
	author = {Rainer Keller and George Bosilca and Graham Fagg and Michael Resch and Jack Dongarra}
}
@article {icl:318,
	title = {Implementation of the Mixed-Precision High Performance LINPACK Benchmark on the CELL Processor},
	journal = {University of Tennessee Computer Science Tech Report},
	number = {UT-CS-06-580, LAPACK Working Note $\#$177},
	year = {2006},
	month = {2006-09},
	keywords = {iter-ref},
	author = {Jakub Kurzak and Jack Dongarra}
}
@article {icl:321,
	title = {Implementing Linear Algebra Routines on Multi-Core Processors with Pipelining and a Look Ahead},
	journal = {University of Tennessee Computer Science Tech Report, UT-CS-06-581, LAPACK Working Note $\#$178},
	year = {2006},
	month = {2006-01},
	author = {Jakub Kurzak and Jack Dongarra}
}
@article {icl:327,
	title = {Predicting the electronic properties of 3D, million-atom semiconductor nanostructure architectures},
	journal = {J. Phys.: Conf. Ser. 46},
	volume = {:101088/1742-6596/46/1/040},
	year = {2006},
	month = {2006-01},
	pages = {292-298},
	keywords = {DOE_NANO},
	author = {Alex Zunger and Alberto Franceschetti and Gabriel Bester and Wesley B. Jones and Kwiseon Kim and Peter A. Graf and Lin-Wang Wang and Andrew Canning and Osni Marques and Christof Voemel and Jack Dongarra and Julien Langou and Stanimire Tomov}
}
@article {icl:370,
	title = {Prospectus for the Next LAPACK and ScaLAPACK Libraries},
	journal = {PARA 2006},
	year = {2006},
	month = {2006-06},
	address = {Umea, Sweden},
	author = {James Demmel and Jack Dongarra and B. Parlett and William Kahan and Ming Gu and David Bindel and Yozo Hida and Xiaoye Li and Osni Marques and Jason E. Riedy and Christof Voemel and Julien Langou and Piotr Luszczek and Jakub Kurzak and Alfredo Buttari and Julien Langou and Stanimire Tomov}
}
@article {icl:304,
	title = {HPC Challenge v1.x Benchmark Suite},
	journal = {SC|05 Tutorial - S13},
	year = {2005},
	month = {2005-01},
	address = {Seattle, Washington},
	keywords = {hpcc},
	author = {Piotr Luszczek and David Koester}
}
@article {icl:282,
	title = {Introduction to the HPC Challenge Benchmark Suite},
	year = {2005},
	month = {2005-03},
	keywords = {hpcc, hpcchallenge},
	author = {Piotr Luszczek and Jack Dongarra and David Koester and Rolf Rabenseifner and Bob Lucas and Jeremy Kepner and John McCalpin and David Bailey and Daisuke Takahashi}
}
@article {,
	title = {NanoPSE: A Nanoscience Problem Solving Environment for Atomistic Electronic Structure of Semiconductor Nanostructures},
	journal = {Journal of Physics: Conference Series},
	year = {2005},
	month = {2005-06},
	pages = {277-282},
	abstract = {Researchers at the National Renewable Energy Laboratory and their collaborators have developed over the past ~10 years a set of algorithms for an atomistic description of the electronic structure of nanostructures, based on plane-wave pseudopotentials and configuration interaction. The present contribution describes the first step in assembling these various codes into a single, portable, integrated set of software packages. This package is part of an ongoing research project in the development stage. Components of NanoPSE include codes for atomistic nanostructure generation and passivation, valence force field model for atomic relaxation, code for potential field generation, empirical pseudopotential method solver, strained linear combination of bulk bands method solver, configuration interaction solver for excited states, selection of linear algebra methods, and several inverse band structure solvers. Although not available for general distribution at this time as it is being developed and tested, the design goal of the NanoPSE software is to provide a software context for collaboration. The software package is enabled by fcdev, an integrated collection of best practice GNU software for open source development and distribution augmented to better support FORTRAN.},
	doi = {https://doi.org/10.1088/1742-6596/16/1/038},
	url = {https://iopscience.iop.org/article/10.1088/1742-6596/16/1/038/meta},
	author = {Wesley B. Jones and Gabriel Bester and Andrew Canning and Alberto Franceschetti and Peter A. Graf and Kwiseon Kim and Julien Langou and Lin-Wang Wang and Jack Dongarra and Alex Zunger}
}
@article {icl:278,
	title = {New Grid Scheduling and Rescheduling Methods in the GrADS Project},
	journal = {International Journal of Parallel Programming},
	volume = {33},
	number = {2},
	year = {2005},
	month = {2005-06},
	pages = {209-229},
	publisher = {Springer},
	keywords = {grads},
	author = {Francine Berman and Henri Casanova and Andrew Chien and Keith Cooper and Holly Dail and Anshuman Dasgupta and Wei Deng and Jack Dongarra and Lennart Johnsson and Ken Kennedy and Charles Koelbel and Bo Liu and Xu Liu and Anirban Mandal and Gabriel Marin and Mark Mazina and John Mellor-Crummey and Celso Mendes and A. Olugbile and Jignesh M. Patel and Dan Reed and Zhiao Shi and Otto Sievert and H. Xia and Asim YarKhan}
}
@inproceedings {icl:287,
	title = {Performance Analysis of GYRO: A Tool Evaluation},
	journal = {In Proceedings of the 2005 SciDAC Conference},
	year = {2005},
	month = {2005-06},
	address = {San Francisco, CA},
	keywords = {kojak},
	author = {Patrick H. Worley and Jeff Candy and Laura Carrington and Kevin Huck and Timothy Kaiser and Kumar Mahinthakumar and Allen D. Malony and Shirley Moore and Dan Reed and Philip C. Roth and H. Shan and Sameer Shende and Allan Snavely and S. Sreepathi and Felix Wolf and Y. Zhang}
}
@inproceedings {icl:295,
	title = {Performance Analysis of One-sided Communication Mechanisms},
	journal = {Mini-Symposium "Tools Support for Parallel Programming", Proceedings of Parallel Computing (ParCo)},
	number = {ICL-UT-06-07},
	year = {2005},
	month = {2005-09},
	address = {Malaga, Spain},
	keywords = {kojak},
	author = {Bernd Mohr and Andrej K{\"u}hnal and Marc-Andre Hermanns and Felix Wolf}
}
@conference {icl:235,
	title = {Automatic Blocking of QR and LU Factorizations for Locality},
	booktitle = {2nd ACM SIGPLAN Workshop on Memory System Performance (MSP 2004)},
	year = {2004},
	month = {2004-06},
	publisher = {ACM},
	organization = {ACM},
	address = {Washington, DC},
	abstract = {QR and LU factorizations for dense matrices are important linear algebra computations that are widely used in scientific applications. To efficiently perform these computations on modern computers, the factorization algorithms need to be blocked when operating on large matrices to effectively exploit the deep cache hierarchy prevalent in today{\textquoteright}s computer memory systems. Because both QR (based on Householder transformations) and LU factorization algorithms contain complex loop structures, few compilers can fully automate the blocking of these algorithms. Though linear algebra libraries such as LAPACK provides manually blocked implementations of these algorithms, by automatically generating blocked versions of the computations, more benefit can be gained such as automatic adaptation of different blocking strategies. This paper demonstrates how to apply an aggressive loop transformation technique, dependence hoisting, to produce efficient blockings for both QR and LU with partial pivoting. We present different blocking strategies that can be generated by our optimizer and compare the performance of auto-blocked versions with manually tuned versions in LAPACK, both using reference BLAS, ATLAS BLAS and native BLAS specially tuned for the underlying machine architectures.},
	keywords = {gco, papi, sans},
	doi = {10.1145/1065895.1065898},
	author = {Qing Yi and Ken Kennedy and Haihang You and Keith Seymour and Jack Dongarra}
}
@conference {icl:239,
	title = {Automating the Large-Scale Collection and Analysis of Performance},
	booktitle = {5th LCI International Conference on Linux Clusters: The HPC Revolution},
	year = {2004},
	month = {2004-05},
	address = {Austin, Texas},
	keywords = {kojak, papi},
	author = {Phil Mucci and Jack Dongarra and Rick Kufrin and Shirley Moore and Fengguang Song and Felix Wolf}
}
@article {icl:236,
	title = {Cray X1 Evaluation Status Report},
	journal = {Oak Ridge National Laboratory Report},
	volume = {/-2004/13},
	year = {2004},
	month = {2004-01},
	author = {Pratul Agarwal and R. A. Alexander and E. Apra and Satish Balay and Arthur S. Bland and James Colgan and Eduardo D{\textquoteright}Azevedo and Jack Dongarra and Tom Dunigan and Mark Fahey and Al Geist and M. Gordon and Robert Harrison and Dinesh Kaushik and M. Krishnakumar and Piotr Luszczek and Tony Mezzacapa and Jeff Nichols and Jarek Nieplocha and Leonid Oliker and T. Packwood and M. Pindzola and Thomas C. Schulthess and Jeffrey Vetter and James B White and T. Windus and Patrick H. Worley and Thomas Zacharia}
}
@inproceedings {icl:172,
	title = {GrADSolve - RPC for High Performance Computing on the Grid},
	journal = {Lecture Notes in Computer Science, Proceedings of the 9th International Euro-Par Conference},
	volume = {2790},
	year = {2003},
	month = {2003-01},
	pages = {394-403},
	publisher = {Springer-Verlag, Berlin},
	address = {Klagenfurt, Austria},
	keywords = {netsolve},
	doi = {10.1007/978-3-540-45209-6_58},
	author = {Sathish Vadhiyar and Jack Dongarra and Asim YarKhan},
	editor = {Harald Kosch and Laszlo Boszormenyi and Hermann Hellwagner}
}
@inproceedings {icl:129,
	title = {A Simple Installation and Administration Tool for Large-scaled PC Cluster System},
	journal = {ClusterWorld Conference and Expo},
	year = {2003},
	month = {2003-03},
	address = {San Jose, CA},
	author = {Tomoyuki Hiroyasu and Mitsunori Miki and Kenzo Kodama and Junichi Uekawa and Jack Dongarra}
}
@article {icl:119,
	title = {Adaptive Scheduling for Task Farming with Grid Middleware},
	journal = {International Journal of Supercomputer Applications and High-Performance Computing},
	volume = {13},
	number = {3},
	year = {2002},
	month = {2002-10},
	pages = {231-240},
	author = {Henri Casanova and Myung Ho Kim and James Plank and Jack Dongarra}
}
@techreport {icl:91,
	title = {Development of the PICMSS NetSolve Service},
	journal = {ICL Technical Report},
	number = {ICL-UT-02-04},
	year = {2002},
	month = {2002-04},
	keywords = {netsolve},
	author = {Matthew Kelleher Jr.}
}
@inproceedings {icl:79,
	title = {Toward a Framework for Preparing and Executing Adaptive Grid Programs},
	journal = {International Parallel and Distributed Processing Symposium: IPDPS 2002 Workshops},
	year = {2002},
	month = {2002-04},
	pages = {0171},
	address = {Fort Lauderdale, FL},
	keywords = {grads},
	author = {Ken Kennedy and John Mellor-Crummey and Keith Cooper and Linda Torczon and Francine Berman and Andrew Chien and Dave Angulo and Ian Foster and Dennis Gannon and Lennart Johnsson and Carl Kesselman and Jack Dongarra and Sathish Vadhiyar}
}
@article {icl:125,
	title = {An Updated Set of Basic Linear Algebra Subprograms (BLAS)},
	journal = {ACM Transactions on Mathematical Software},
	volume = {28},
	number = {2},
	year = {2002},
	month = {2002-12},
	pages = {135-151},
	doi = {10.1145/567806.567807},
	author = {Susan Blackford and James Demmel and Jack Dongarra and Iain Duff and Sven Hammarling and Greg Henry and Michael Heroux and Linda Kaufman and Andrew Lumsdaine and Antoine Petitet and Roldan Pozo and Karin Remington and Clint Whaley}
}
@article {icl:6,
	title = {Basic Linear Algebra Subprograms (BLAS)},
	journal = {(an update), submitted to ACM TOMS},
	year = {2001},
	month = {2001-02},
	author = {Susan Blackford and James Demmel and Jack Dongarra and Iain Duff and Sven Hammarling and Greg Henry and Michael Heroux and Linda Kaufman and Andrew Lumsdaine and Antoine Petitet and Roldan Pozo and Karin Remington and Clint Whaley}
}
@article {icl:90,
	title = {The GrADS Project: Software Support for High-Level Grid Application Development},
	journal = {International Journal of High Performance Applications and Supercomputing},
	volume = {15},
	number = {4},
	year = {2001},
	month = {2001-01},
	pages = {327-344},
	keywords = {grads},
	author = {Francine Berman and Andrew Chien and Keith Cooper and Jack Dongarra and Ian Foster and Dennis Gannon and Lennart Johnsson and Ken Kennedy and Carl Kesselman and John Mellor-Crummey and Dan Reed and Linda Torczon and Rich Wolski}
}
@article {icl:82,
	title = {Telescoping Languages: A Strategy for Automatic Generation of Scientific Problem-Solving Systems from Annotated Libraries},
	journal = {Journal of Parallel and Distributed Computing},
	volume = {61},
	number = {12},
	year = {2001},
	month = {2001-12},
	pages = {1803-1826},
	author = {Ken Kennedy and Bradley Broom and Keith Cooper and Jack Dongarra and Rob Fowler and Dennis Gannon and Lennart Johnsson and John Mellor-Crummey and Linda Torczon}
}
@techreport {icl:30,
	title = {The GrADS Project: Software Support for High-Level Grid Application Development},
	journal = {Technical Report},
	year = {2000},
	month = {2000-02},
	keywords = {grads},
	author = {Francine Berman and Andrew Chien and Keith Cooper and Jack Dongarra and Ian Foster and Dennis Gannon and Lennart Johnsson and Ken Kennedy and Carl Kesselman and Dan Reed and Linda Torczon and Rich Wolski}
}
@inproceedings {icl:39,
	title = {Recent Advances in Parallel Virtual Machine and Message Passing Interface},
	journal = {Lecture Notes in Computer Science: Proceedings of 7th European PVM/MPI Users{\textquoteright} Group Meeting 2000},
	year = {2000},
	month = {2000-01},
	pages = {V1908},
	address = {(Hungary: Springer Verlag)},
	keywords = {ftmpi},
	author = {Jack Dongarra and Peter Kacsuk and N. Podhorszki}
}
@article {icl:55,
	title = {HARNESS: A Next Generation Distributed Virtual Machine},
	journal = {International Journal on Future Generation Computer Systems},
	volume = {15},
	number = {5-6},
	year = {1999},
	month = {1999-01},
	pages = {571-582},
	keywords = {harness},
	author = {Micah Beck and Jack Dongarra and Graham Fagg and Al Geist and Paul Gray and James Kohl and Mauro Migliardi and Keith Moore and Terry Moore and Philip Papadopoulous and Stephen L. Scott and Vaidy Sunderam}
}
@article {icl:63,
	title = {Stochastic Performance Prediction for Iterative Algorithms in Distributed Environments},
	journal = {Journal of Parallel and Distributed Computing},
	volume = {98},
	number = {1},
	year = {1999},
	month = {1999-01},
	pages = {68-91},
	author = {Henri Casanova and Myung Ho Kim and James Plank and Jack Dongarra}
}
