@article {,
	title = {Economical Quasi-Newton Unitary Optimization of Electronic Orbitals},
	journal = {Physical Chemistry Chemical Physics},
	year = {2024},
	month = {2023-12},
	abstract = {We present an efficient quasi-Newton orbital solver optimized to reduce the number of gradient evaluations and other computational steps of comparable cost. The solver optimizes orthogonal orbitals by sequences of unitary rotations generated by the (preconditioned) limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm equipped with trust-region step restriction. The low-rank structure of the L-BFGS inverse Hessian is exploited when solving the trust-region problem. The efficiency of the proposed {\textquoteleft}{\textquoteleft}Quasi-Newton Unitary Optimization with Trust-Region{\textquoteright}{\textquoteright} (QUOTR) solver is compared to that of the standard Roothaan-Hall approach accelerated by the Direct Inversion of Iterative Subspace (DIIS), and other exact and approximate Newton solvers for mean-field (Hartree-Fock and Kohn-Sham) problems.},
	issn = {1463-9076},
	doi = {10.1039/D3CP05557D},
	url = {http://pubs.rsc.org/en/Content/ArticleLanding/2024/CP/D3CP05557},
	author = {Slattery, Samuel A and Surjuse, Kshitijkumar A and Peterson, Charles and Penchoff, Deborah A and Valeev, Edward}
}
@booklet {,
	title = {XaaS: Acceleration as a Service to Enable Productive High-Performance Cloud Computing},
	year = {2024},
	month = {2024-01},
	publisher = {arXiv},
	abstract = {HPC and Cloud have evolved independently, specializing their innovations into performance or productivity. Acceleration as a Service (XaaS) is a recipe to empower both fields with a shared execution platform that provides transparent access to computing resources, regardless of the underlying cloud or HPC service provider. Bridging HPC and cloud advancements, XaaS presents a unified architecture built on performance-portable containers. Our converged model concentrates on low-overhead, high-performance communication and computing, targeting resource-intensive workloads from climate simulations to machine learning. XaaS lifts the restricted allocation model of Function-as-a-Service (FaaS), allowing users to benefit from the flexibility and efficient resource utilization of serverless while supporting long-running and performance-sensitive workloads from HPC.},
	url = {https://arxiv.org/abs/2401.04552},
	author = {Torsten Hoefler and Marcin Copik and Pete Beckman and Andrew Jones and Ian Foster and Manish Parashar and Daniel Reed and Matthias Troyer and Thomas Schulthess and Dan Ernst and Jack Dongarra}
}
@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}
}
@article {,
	title = {Combining multitask and transfer learning with deep Gaussian processes for autotuning-based performance engineering},
	journal = {The International Journal of High Performance Computing Applications},
	year = {2023},
	month = {2023-03},
	abstract = {We combine deep Gaussian processes (DGPs) with multitask and transfer learning for the performance modeling and optimization of HPC applications. Deep Gaussian processes merge the uncertainty quantification advantage of Gaussian processes (GPs) with the predictive power of deep learning. Multitask and transfer learning allow for improved learning efficiency when several similar tasks are to be learned simultaneously and when previous learned models are sought to help in the learning of new tasks, respectively. A comparison with state-of-the-art autotuners shows the advantage of our approach on two application problems. In this article, we combine DGPs with multitask and transfer learning to allow for both an improved tuning of an application parameters on problems of interest but also the prediction of parameters on any potential problem the application might encounter.},
	issn = {1094-3420},
	doi = {10.1177/10943420231166365},
	url = {http://journals.sagepub.com/doi/10.1177/10943420231166365},
	author = {Luszczek, Piotr and Sid-Lakhdar, Wissam M and Dongarra, Jack}
}
@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 = {Improving the Scaling of an Asynchronous Many-Task Runtime with a Lightweight Communication Engine},
	booktitle = {52nd International Conference on Parallel Processing (ICPP 2023)},
	year = {2023},
	month = {2023-09},
	publisher = {ACM},
	organization = {ACM},
	address = {Salt Lake City, Utah},
	abstract = {There is a growing interest in Asynchronous Many-Task (AMT) runtimes as an efficient way to map irregular and dynamic parallel applications onto heterogeneous computing resources. In this work, we show that AMTs nonetheless struggle with communication bottlenecks when scaling computations strongly and that the design of commonly-used communication libraries such as MPI contribute to these bottlenecks. We replace MPI with LCI, a Lightweight Communication Interface that is designed for dynamic, asynchronous frameworks, as the communication layer for the PaRSEC runtime. The result is a significant reduction of end-to-end latency in communication microbenchmarks and a reduction of overall time-tosolution by up to 12\% in HiCMA, a tile-based low-rank Cholesky factorization package.},
	keywords = {asynchronous many-task, dynamic runtime, lightweight communication, low-rank Cholesky, message-passing, MPI, strong scaling},
	doi = {10.1145/3605573.3605642},
	url = {http://snir.cs.illinois.edu/listed/icpp2023-69.pdf},
	author = {Omri Mor and George Bosilca and Marc Snir}
}
@conference {,
	title = {MPI Continuations And How To Invoke Them},
	booktitle = {Sustained Simulation Performance 2021},
	year = {2023},
	month = {2023-02},
	pages = {67 - 83},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Asynchronous programming models (APM) are gaining more and more traction, allowing applications to expose the available concurrency to a runtime system tasked with coordinating the execution. While MPI has long provided support for multi-threaded communication and non-blocking operations, it falls short of adequately supporting the asynchrony of separate but dependent parts of an application coupled by the start and completion of a communication operation. Correctly and efficiently handling MPI communication in differentAPMmodels is still a challenge. We have previously proposed an extension to the MPI standard providing operation completion notifications using callbacks, so-called MPI Continuations. This interface is flexible enough to accommodate a wide range of different APMs. In this paper, we discuss different variations of the callback signature and how to best pass data from the code starting the communication operation to the code reacting to its completion. We establish three requirements (efficiency, usability, safety) and evaluate different variations against them. Finally, we find that the current choice is not the best design in terms of both efficiency and safety and propose a simpler, possibly more efficient and safe interface. We also show how the transfer of information into the continuation callback can be largely automated using C++ lambda captures.},
	isbn = {978-3-031-18045-3},
	doi = {10.1007/978-3-031-18046-010.1007/978-3-031-18046-0_5},
	url = {https://link.springer.com/10.1007/978-3-031-18046-0},
	author = {Schuchart, Joseph and George Bosilca}
}
@conference {,
	title = {PAQR: Pivoting Avoiding QR factorization},
	booktitle = {2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
	year = {2023},
	publisher = {IEEE},
	organization = {IEEE},
	address = {St. Petersburg, FL, USA},
	doi = {10.1109/IPDPS54959.2023.00040},
	url = {https://ieeexplore.ieee.org/document/10177407/},
	author = {Sid-Lakhdar, Wissam and Cayrols, Sebastien and Bielich, Daniel and Abdelfattah, Ahmad and Luszczek, Piotr and Gates, Mark and Tomov, Stanimire and Johansen, Hans and Williams-Young, David and Davis, Timothy and Dongarra, Jack and Anzt, Hartwig}
}
@inproceedings {,
	title = {Synchronizing MPI Processes in Space and Time},
	journal = {EUROMPI {\textquoteright}23: 30th European MPI Users{\textquoteright} Group Meeting},
	year = {2023},
	month = {2023-09},
	publisher = {ACM},
	address = {Bristol, United Kingdom},
	abstract = {Performance benchmarks are an integral part of the development and evaluation of parallel algorithms, both in distributed applications as well as MPI implementations themselves. The initial step of the benchmark process is to obtain a common timestamp to mark the start of an operation across all involved processes, and the state-of-the-art in many applications and widely used MPI benchmark suites is the use of MPI barriers. In this paper, we show that the synchronization in space provided by an MPI_Barrier is insufficient for proper benchmark results of parallel distributed algorithms, using MPI collective operations as examples. The resulting lack of a global start timestamp for an operation leads to skewed results, with a significant impact of the used barrier algorithm. In order to mitigate these issues, we propose and discuss the implementation of MPIX_Harmonize, which extends the synchronization in space provided by MPI_Barrier with a time synchronization to guarantee a common starting timestamp across all involved processes. By replacing the use of MPI_Barrier with MPIX_Harmonize, benchmark implementors can eliminate skews resulting from barrier algorithms and achieve stable performance benchmark results. We will show that the proper time synchronization can have significant impact on the benchmark results for various implementations of MPI_Allreduce, MPI_Reduce, and MPI_Bcast.},
	isbn = {9798400709135},
	doi = {10.1145/3615318.3615325},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3615318},
	author = {Schuchart, Joseph and Hunold, Sascha and Bosilca, George}
}
@conference {,
	title = {Task-Based Polar Decomposition Using SLATE on Massively Parallel Systems with Hardware Accelerators},
	booktitle = {SC-W {\textquoteright}23: Proceedings of the SC {\textquoteright}23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis},
	year = {2023},
	month = {2023-11},
	publisher = {ACM},
	organization = {ACM},
	address = {Denver, CO},
	abstract = {We investigate a new task-based implementation of the polar decomposition on massively parallel systems augmented with multiple GPUs using SLATE. We implement the iterative QR Dynamically-Weighted Halley (QDWH) algorithm, whose building blocks mainly consist of compute-bound matrix operations, allowing for high levels of parallelism to be exploited on various hardware architectures, such as NVIDIA, AMD, and Intel GPU-based systems. To achieve both performance and portability, we implement our QDWH-based polar decomposition in the SLATE library, which uses efficient techniques in dense linear algebra, such as 2D block cyclic data distribution and communication-avoiding algorithms, as well as modern parallel programming approaches, such as dynamic scheduling and communication overlapping, and uses OpenMP tasks to track data dependencies.

We report numerical accuracy and performance results. The benchmarking campaign reveals up to an 18-fold performance speedup of the GPU accelerated implementation compared to the existing state-of-the-art implementation for the polar decomposition.},
	isbn = {9798400707858},
	doi = {10.1145/3624062.3624248},
	url = {https://dl.acm.org/doi/proceedings/10.1145/3624062},
	author = {Sukkari, Dalal and Gates, Mark and Al Farhan, Mohammed and Anzt, Hartwig and Dongarra, Jack}
}
@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 = {Approximate Computing for Scientific Applications},
	booktitle = {Approximate Computing Techniques},
	year = {2022},
	month = {2022-01},
	pages = {415 - 465},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	edition = {322},
	abstract = {This chapter reviews the performance benefits that result from applying (software) approximate computing to scientific applications. For this purpose, we target two particular areas, linear algebra and deep learning, with the first one selected for being ubiquitous in scientific problems and the second one for its considerable and growing number of important applications both in industry and science.

The review of linear algebra in scientific computing is focused on the iterative solution of sparse linear systems, exposing the prevalent costs of memory accesses in these methods, and demonstrating how approximate computing can help to reduce these overheads, for example, in the case of stationary solvers themselves or the application of preconditioners for the solution of sparse linear systems via Krylov subspace methods.

The discussion of deep learning is focused on the use of approximate data transfer for cutting costs of host-to-device operations, as well as the use of adaptive precision for accelerating training of classical CNN architectures. Additionally we discuss model optimization and architecture search in presence of constraints for edge devices applications.},
	isbn = {978-3-030-94704-0},
	doi = {10.1007/978-3-030-94705-7_14},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-94705-7_14},
	author = {Anzt, Hartwig and Casas, Marc and Malossi, ~Cristiano I. and Quintana-Ort{\'\i}, Enrique S and Scheidegger, Florian and Zhuang, Sicong},
	editor = {Bosio, Alberto and M{\'e}nard, Daniel and Sentieys, Olivier}
}
@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}
}
@inproceedings {,
	title = {Checkpointing {\`a} la Young/Daly: An Overview},
	journal = {IC3-2022: Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing},
	year = {2022},
	month = {2022-08},
	pages = {701-710},
	publisher = {ACM Press},
	address = {Noida, India},
	abstract = {The Young/Daly formula provides an approximation of the optimal checkpoint period for a parallel application executing on a supercomputing platform. The Young/Daly formula was originally designed for preemptible tightly-coupled applications. We provide some background and survey various application scenarios to assess the usefulness and limitations of the formula.},
	isbn = {9781450396752},
	doi = {10.1145/3549206},
	url = {https://dl.acm.org/doi/fullHtml/10.1145/3549206.3549328},
	author = {Anne Benoit and Yishu Du and Thomas Herault and Loris Marchal and Guillaume Pallez and Lucas Perotin and Yves Robert and Hongyang Sun and Frederic Vivien}
}
@techreport {,
	title = {Communication Avoiding LU with Tournament Pivoting in SLATE},
	journal = {SLATE Working Notes},
	number = {18, ICL-UT-22-01},
	year = {2022},
	month = {2022-01},
	author = {Rabab Alomairy and Mark Gates and Sebastien Cayrols and Dalal Sukkari and Kadir Akbudak and Asim YarKhan and Paul Bagwell and Jack Dongarra}
}
@conference {,
	title = {Composition of Algorithmic Building Blocks in Template Task Graphs},
	booktitle = {2022 IEEE/ACM Parallel Applications Workshop: Alternatives To MPI+X (PAW-ATM)},
	year = {2022},
	month = {2023-01},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Dallas, TX, USA},
	doi = {10.1109/PAW-ATM56565.2022.00008},
	url = {https://ieeexplore.ieee.org/document/10024647/},
	author = {Herault, Thomas and Schuchart, Joseph and Valeev, Edward F. and George Bosilca}
}
@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.}
}
@inproceedings {,
	title = {Deep Gaussian process with multitask and transfer learning for performance optimization},
	journal = {2022 IEEE High Performance Extreme Computing Conference (HPEC)},
	year = {2022},
	month = {2022-09},
	pages = {1-7},
	abstract = {We combine Deep Gaussian Processes with multitask and transfer learning for the performance modeling and optimization of HPC applications. Deep Gaussian processes merge the uncertainty quantification advantage of Gaussian Processes with the predictive power of deep learning. Multitask and transfer learning allow for improved learning efficiency when several similar tasks are to be learned simultaneously and when previous learned models are sought to help in the learning of new tasks, respectively. A comparison with state-of-the-art autotuners shows the advantage of our approach on two application problems.},
	doi = {10.1109/HPEC55821.2022.9926396},
	author = {Sid-Lakhdar, Wissam M. and Aznaveh, Mohsen and Luszczek, Piotr and Dongarra, Jack}
}
@conference {,
	title = {Generalized Flow-Graph Programming Using Template Task-Graphs: Initial Implementation and Assessment},
	booktitle = {2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
	year = {2022},
	month = {2022-07},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Lyon, France},
	abstract = {We present and evaluate TTG, a novel programming model and its C++ implementation that by marrying the ideas of control and data flowgraph programming supports compact specification and efficient distributed execution of dynamic and irregular applications. Programming interfaces that support task-based execution often only support shared memory parallel environments; a few support distributed memory environments, either by discovering the entire DAG of tasks on all processes, or by introducing explicit communications. The first approach limits scalability, while the second increases the complexity of programming. We demonstrate how TTG can address these issues without sacrificing scalability or programmability by providing higher-level abstractions than conventionally provided by task-centric programming systems, without impeding the ability of these runtimes to manage task creation and execution as well as data and resource management efficiently. TTG supports distributed memory execution over 2 different task runtimes, PaRSEC and MADNESS. Performance of four paradigmatic applications (in graph analytics, dense and block-sparse linear algebra, and numerical integrodifferential calculus) with various degrees of irregularity implemented in TTG is illustrated on large distributed-memory platforms and compared to the state-of-the-art implementations.},
	doi = {10.1109/IPDPS53621.2022.00086},
	url = {https://ieeexplore.ieee.org/abstract/document/9820613},
	author = {Schuchart, Joseph and Nookala, Poornima and Javanmard, Mohammad Mahdi and Herault, Thomas and Valeev, Edward F. and George Bosilca and Harrison, Robert J.}
}
@inproceedings {,
	title = {{Integrating process, control-flow, and data resiliency layers using a hybrid Fenix/Kokkos approach}},
	journal = {2022 IEEE International Conference on Cluster Computing (CLUSTER 2022)},
	year = {2022},
	month = {2022-09},
	address = {Heidelberg, Germany},
	keywords = {checkpointing, Fault tolerance, Fenix, HPC, Kokkos, MPI-ULFM, resilience},
	url = {https://hal.archives-ouvertes.fr/hal-03772536},
	author = {Whitlock, Matthew and Morales, Nicolas and George Bosilca and Bouteiller, Aur{\'e}lien and Nicolae, Bogdan and Teranishi, Keita and Giem, Elisabeth and Sarkar, Vivek}
}
@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.}
}
@techreport {,
	title = {PAQR: Pivoting Avoiding QR factorization},
	journal = {ICL Technical Report},
	number = {ICL-UT-22-06},
	year = {2022},
	month = {2022-06},
	abstract = {The solution of linear least-squares problems is at the heart of many scientific and engineering applications. While any method able to minimize the backward error of such problems is considered numerically stable, the theory states that the forward error depends on the condition number of the
matrix in the system of equations. On the one hand, the QR factorization is an efficient method to solve such problems, but the solutions it produces may have large forward errors when the matrix is deficient. On the other hand, QR with column pivoting (QRCP) is able to produce smaller forward errors on deficient matrices, but its cost is prohibitive compared to QR. The aim of this paper is to propose PAQR, an alternative solution method with the same cost (or smaller) as QR and as accurate as QRCP in practical cases, for the solution of rank-deficient linear least-squares problems. After presenting the algorithm and its implementations on different architectures, we compare its accuracy and performance results on a variety of application problems.
},
	author = {Wissam M. Sid-Lakhdar and Sebastien Cayrols and Daniel Bielich and Ahmad Abdelfattah and Piotr Luszczek and Mark Gates and Stanimire Tomov and Hans Johansen and David Williams-Young and Timothy A. Davis and Jack Dongarra}
}
@conference {,
	title = {Performance Analysis of Parallel FFT on Large Multi-GPU Systems},
	booktitle = {2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
	year = {2022},
	month = {2022-08},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Lyon, France},
	doi = {10.1109/IPDPSW55747.2022.00072},
	url = {https://ieeexplore.ieee.org/document/9835388/},
	author = {Ayala, Alan and Stanimire Tomov and Stoyanov, Miroslav and Haidar, Azzam and Dongarra, Jack}
}
@inproceedings {,
	title = {Porting Sparse Linear Algebra to~Intel GPUs},
	journal = {Euro-Par 2021: Parallel Processing Workshops},
	volume = {13098},
	year = {2022},
	month = {2022-06},
	pages = {57 - 68},
	publisher = {Springer International Publishing},
	address = {Lisbon, Portugal},
	abstract = {With discrete Intel GPUs entering the high performance computing landscape, there is an urgent need for production-ready software stacks for these platforms. In this paper, we report how we prepare the Ginkgo math library for Intel GPUs by developing a kernel backed based on the DPC++ programming environment. We discuss conceptual differences to the CUDA and HIP programming models and describe workflows for simplified code conversion. We benchmark advanced sparse linear algebra routines utilizing the converted kernels to assess the efficiency of the DPC++ backend in the hardware-specific performance bounds. We compare the performance of basic building blocks against routines providing the same functionality that ship with Intel{\textquoteright}s oneMKL vendor library.},
	keywords = {Ginkgo, Intel GPUs, math library, oneAPI, SpMV},
	isbn = {978-3-031-06155-4},
	doi = {10.1007/978-3-031-06156-1_5},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-06156-1_5},
	author = {Tsai, Yuhsiang M. and Cojean, Terry and Anzt, Hartwig},
	editor = {Chaves, Ricardo and B. Heras, Dora and Ilic, Aleksandar and Unat, Didem and Badia, Rosa M. and Bracciali, Andrea and Diehl, Patrick and Dubey, Anshu and Sangyoon, Oh and L. Scott, Stephen and Ricci, Laura}
}
@conference {,
	title = {Pushing the Boundaries of Small Tasks: Scalable Low-Overhead Data-Flow Programming in TTG},
	booktitle = {2022 IEEE International Conference on Cluster Computing (CLUSTER)},
	year = {2022},
	month = {2022-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Heidelberg, Germany},
	abstract = {Shared memory parallel programming models strive to provide low-overhead execution environments. Task-based programming models, in particular, are well-suited to cope with the ubiquitous multi- and many-core systems since they allow applications to express all available concurrency to a scheduler, which is tasked with exploiting the available hardware resources. It is general consensus that atomic operations should be preferred over locks and mutexes to avoid inter-thread serialization and the resulting loss in efficiency. However, even atomic operations may serialize threads if not used judiciously. In this work, we will discuss several optimizations applied to TTG and the underlying PaRSEC runtime system aiming at removing contentious atomic operations to reduce the overhead of task management to a few hundred clock cycles. The result is an optimized data-flow programming system that seamlessly scales from a single node to distributed execution and which is able to compete with OpenMP in shared memory.},
	keywords = {Dataflow graph, Hardware, Instruction sets, Memory management, PaR-SEC, parallel programming, runtime, scalability, Task analysis, task-based programming, Template Task Graph, TTG},
	doi = {10.1109/CLUSTER51413.2022.00026},
	url = {https://ieeexplore.ieee.org/document/9912704/},
	author = {Schuchart, Joseph and Nookala, Poornima and Herault, Thomas and Valeev, Edward F. and George Bosilca}
}
@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 = {Accelerating FFT towards Exascale Computing},
	year = {2021},
	publisher = {NVIDIA GPU Technology Conference (GTC2021)},
	author = {Alan Ayala and Stanimire Tomov and Haidar, Azzam and Stoyanov, M. and Cayrols, Sebastien and Li, Jiali and George Bosilca and Jack Dongarra}
}
@conference {,
	title = {Accelerating Multi - Process Communication for Parallel 3-D FFT},
	booktitle = {2021 Workshop on Exascale MPI (ExaMPI)},
	year = {2021},
	month = {2021-12},
	publisher = {IEEE},
	organization = {IEEE},
	address = {St. Louis, MO, USA},
	abstract = {Today largest and most powerful supercomputers in the world are built on heterogeneous platforms; and using the combined power of multi-core CPUs and GPUs, has had a great impact accelerating large-scale applications. However, on these architectures, parallel algorithms, such as the Fast Fourier Transform (FFT), encounter that inter-processor communication become a bottleneck and limits their scalability. In this paper, we present techniques for speeding up multi-process communication cost during the computation of FFTs, considering hybrid network connections as those expected on upcoming exascale machines. Among our techniques, we present algorithmic tuning, making use of phase diagrams; parametric tuning, using different FFT settings; and MPI distribution tuning based on FFT size and computational resources available. We present several experiments obtained on Summit supercomputer at Oak Ridge National Laboratory, using up to 40,960 IBM Power9 cores and 6,144 NVIDIA V-100 GPUs.},
	doi = {10.1109/ExaMPI54564.2021.00011},
	url = {https://ieeexplore.ieee.org/document/9652837/},
	author = {Ayala, Alan and Tomov, Stan and Stoyanov, Miroslav and Haidar, Azzam and Dongarra, Jack}
}
@article {,
	title = {Callback-based completion notification using MPI Continuations},
	journal = {Parallel Computing},
	volume = {21238566},
	year = {2021},
	month = {Jan-05-2021},
	pages = {102793},
	abstract = {Asynchronous programming models (APM) are gaining more and more traction, allowing applications to expose the available concurrency to a runtime system tasked with coordinating the execution. While MPI has long provided support for multi-threaded communication and nonblocking operations, it falls short of adequately supporting APMs as correctly and efficiently handling MPI communication in different models is still a challenge. We have previously proposed an extension to the MPI standard providing operation completion notifications using callbacks, so-called MPI Continuations. This interface is flexible enough to accommodate a wide range of different APMs.

In this paper, we present an extension to the previously described interface that allows for finer control of the behavior of the MPI Continuations interface. We then present some of our first experiences in using the interface in the context of different applications, including the NAS parallel benchmarks, the PaRSEC task-based runtime system, and a load-balancing scheme within an adaptive mesh refinement solver called ExaHyPE. We show that the interface, implemented inside Open MPI, enables low-latency, high-throughput completion notifications that outperform solutions implemented in the application space.},
	keywords = {MPI, MPI Continuations, OmpSs, OpenMP, parsec, TAMPI, Task-based programming models},
	issn = {01678191},
	doi = {10.1016/j.parco.2021.102793},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0167819121000466?via\%3Dihub},
	author = {Schuchart, Joseph and Samfass, Philipp and Niethammer, Christoph and Gracia, Jos{\'e} and George Bosilca}
}
@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 = {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 = {A More Portable HeFFTe: Implementing a Fallback Algorithm for Scalable Fourier Transforms},
	journal = {ICL Technical Report},
	number = {ICL-UT-21-04},
	year = {2021},
	note = {accepted at HPEC{\textquoteright}21},
	month = {2021-08},
	publisher = {University of Tennessee},
	type = {ICL Tech Report},
	author = {Daniel Sharp and Miroslav Stoyanov and Stanimire Tomov and Jack Dongarra}
}
@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}
}
@conference {,
	title = {Quo Vadis MPI RMA? Towards a More Efficient Use of MPI One-Sided Communication},
	booktitle = {EuroMPI{\textquoteright}21},
	year = {2021},
	address = {Garching, Munich Germany},
	abstract = { The MPI standard has long included one-sided communication abstractions through the MPI Remote Memory Access (RMA) interface. Unfortunately, the MPI RMA chapter in the 4.0 version of the MPI standard still contains both well-known and lesser known short-comings for both implementations and users, which lead to potentially non-optimal usage patterns. In this paper, we identify a set of issues and propose ways for applications to better express anticipated usage of RMA routines, allowing the MPI implementation to better adapt to the application{\textquoteright}s needs. In order to increase the flexibility of the RMA interface, we add the capability to duplicate windows, allowing access to the same resources encapsulated by a window using different configurations. In the same vein, we introduce the concept of MPI memory handles, meant to provide life-time guarantees on memory attached to dynamic windows, removing the overhead currently present in using dynamically exposed memory. We will show that our extensions provide improved accumulate latencies, reduced overheads for multi-threaded flushes, and allow for zero overhead dynamic memory window usage. },
	keywords = {Memory Handles, MPI, MPI-RMA, RDMA},
	url = {https://arxiv.org/abs/2111.08142},
	author = {Schuchart, Joseph and Niethammer, Christoph and Gracia, Jos{\'e} and George Bosilca}
}
@inbook {,
	title = {Rare Earth Elements and Critical Materials: Uses and Availability},
	booktitle = {Rare Earth Elements and Actinides: Progress in Computational Science Applications},
	volume = {1388},
	year = {2021},
	month = {2021-10},
	pages = {63-74},
	publisher = {American Chemical Society},
	organization = {American Chemical Society},
	chapter = {3},
	address = {Washington, DC},
	abstract = {Rare Earth Elements (REEs) are essential elements in critical materials for the economy and national security. This chapter explores REEs{\textquoteright} classifications, importance, abundance, resources and challenges, and the need for solutions and alternatives. REEs{\textquoteright} production, reserves, world markets, and net import reliance is discussed. Applications of REEs to clean energy, electronics, and defense are highlighted.},
	keywords = {critical materials, REE},
	isbn = {ISBN13: 9780841298255 eISBN: 9780841298248},
	doi = {10.1021/bk-2021-1388.ch003},
	url = {https://pubs.acs.org/doi/10.1021/bk-2021-1388.ch003},
	author = {Deborah A. Penchoff and Charles B. Sims and Theresa L. Windus}
}
@article {,
	title = {{Resilient scheduling heuristics for rigid parallel jobs}},
	journal = {Int. J. of Networking and Computing},
	volume = {11},
	number = {1},
	year = {2021},
	pages = {2-26},
	author = {Anne Benoit and Valentin Le F{\`e}vre and Padma Raghavan and Yves Robert and Hongyang Sun}
}
@inproceedings {,
	title = {Scalability Issues in FFT Computation},
	journal = {International Conference on Parallel Computing Technologies},
	year = {2021},
	pages = {279{\textendash}287},
	publisher = {Springer},
	abstract = {The fast Fourier transform (FFT), is one the most important tools in mathematics, and it is widely required by several applications of science and engineering. State-of-the-art parallel implementations of the FFT algorithm, based on Cooley-Tukey developments, are known to be communication-bound, which causes critical issues when scaling the computational and architectural capabilities. In this paper, we study the main performance bottleneck of FFT computations on hybrid CPU and GPU systems at large-scale. We provide numerical simulations and potential acceleration techniques that can be easily integrated into FFT distributed libraries. We present different experiments on performance scalability and runtime analysis on the world{\textquoteright}s most powerful supercomputers today: Summit, using up to 6,144 NVIDIA V100 GPUs, and Fugaku, using more than one million Fujitsu A64FX cores.},
	keywords = {Hybrid systems, Parallel FFT, scalability},
	isbn = {978-3-030-86359-3},
	doi = {10.1007/978-3-030-86359-3_21},
	author = {Alan Ayala and Stanimire Tomov and Stoyanov, Miroslav and Jack Dongarra}
}
@techreport {,
	title = {SLATE Performance Improvements: QR and Eigenvalues},
	journal = {SLATE Working Notes},
	number = {17, ICL-UT-21-02},
	year = {2021},
	month = {2021-04},
	author = {Kadir Akbudak and Paul Bagwell and Sebastien Cayrols and Mark Gates and Dalal Sukkari and Asim YarKhan and Jack Dongarra}
}
@techreport {,
	title = {SLATE Port to AMD and Intel Platforms},
	journal = {SLATE Working Notes},
	number = {16, ICL-UT-21-01},
	year = {2021},
	month = {2021-04},
	author = {Ahmad Abdelfattah and Mohammed Al Farhan and Cade Brown and Mark Gates and Dalal Sukkari and Asim YarKhan and Jack Dongarra}
}
@inproceedings {,
	title = {Task-graph scheduling extensions for efficient synchronization and communication},
	journal = {Proceedings of the ACM International Conference on Supercomputing},
	year = {2021},
	pages = {88{\textendash}101},
	abstract = {Task graphs have been studied for decades as a foundation for scheduling irregular parallel applications and incorporated in many programming models including OpenMP. While many high-performance parallel libraries are based on task graphs, they also have additional scheduling requirements, such as synchronization within inner levels of data parallelism and internal blocking communications. In this paper, we extend task-graph scheduling to support efficient synchronization and communication within tasks. Compared to past work, our scheduler avoids deadlock and oversubscription of worker threads, and refines victim selection to increase the overlap of sibling tasks. To the best of our knowledge, our approach is the first to combine gang-scheduling and work-stealing in a single runtime. Our approach has been evaluated on the SLATE high-performance linear algebra library. Relative to the LLVM OMP runtime, our runtime demonstrates performance improvements of up to 13.82\%, 15.2\%, and 36.94\% for LU, QR, and Cholesky, respectively, evaluated across different configurations related to matrix size, number of nodes, and use of CPUs vs GPUs.},
	keywords = {Compilers, Computing methodologies, Parallel computing methodologies, Parallel programming languages, Runtime environments, Software and its engineering, Software notations and tools},
	doi = {10.1145/3447818.3461616},
	author = {Bak, Seonmyeong and Hernandez, Oscar and Mark Gates and Piotr Luszczek and Sarkar, Vivek}
}
@techreport {,
	title = {ASCR@40: Four Decades of Department of Energy Leadership in Advanced Scientific Computing Research},
	year = {2020},
	month = {2020-08},
	publisher = {Advanced Scientific Computing Advisory Committee (ASCAC), US Department of Energy},
	url = {https://computing.llnl.gov/misc/ASCR@40-Highlights.pdf},
	author = {Bruce Hendrickson and Paul Messina and Buddy Bland and Jackie Chen and Phil Colella and Eli Dart and Jack Dongarra and Thom Dunning and Ian Foster and Richard Gerber and Rachel Harken and Wendy Huntoon and Bill Johnston and John Sarrao and Jeff Vetter}
}
@techreport {,
	title = {ASCR@40: Highlights and Impacts of ASCR{\textquoteright}s Programs},
	year = {2020},
	month = {2020-06},
	publisher = {US Department of Energy{\textquoteright}s Office of Advanced Scientific Computing Research},
	abstract = {The Office of Advanced Scientific Computing Research (ASCR) sits within the Office of Science in the Department of Energy (DOE). Per their web pages, {\textquotedblleft}the mission of the ASCR program is to discover, develop, and deploy computational and networking capabilities to analyze, model, simulate, and predict complex phenomena important to the DOE.{\textquotedblright} This succinct statement encompasses a wide range of responsibilities for computing and networking facilities; for procuring, deploying, and operating high performance computing, networking, and storage resources; for basic research in mathematics and computer science; for developing and sustaining a large body of software; and for partnering with organizations across the Office of Science and beyond. While its mission statement may seem very contemporary, the roots of ASCR are quite deep{\textemdash}long predating the creation of DOE. Applied mathematics and advanced computing were both elements of the Theoretical Division of the Manhattan Project. In the early 1950s, the Manhattan Project scientist and mathematician John von Neumann, then a commissioner for the AEC (Atomic Energy Commission), advocated for the creation of a Mathematics program to support the continued development and applications of digital computing. Los Alamos National Laboratory (LANL) scientist John Pasta created such a program to fund researchers at universities and AEC laboratories. Under several organizational name changes, this program has persisted ever since, and would eventually grow to become ASCR.},
	doi = {https://doi.org/10.2172/1631812},
	url = {https://www.osti.gov/servlets/purl/1631812},
	author = {Bruce Hendrickson and Paul Messina and Buddy Bland and Jackie Chen and Phil Colella and Eli Dart and Jack Dongarra and Thom Dunning and Ian Foster and Richard Gerber and Rachel Harken and Wendy Huntoon and Bill Johnston and John Sarrao and Jeff Vetter}
}
@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 = {Design and Comparison of Resilient Scheduling Heuristics for Parallel Jobs},
	booktitle = {22nd Workshop on Advances in Parallel and Distributed Computational Models (APDCM 2020)},
	year = {2020},
	month = {2020-05},
	publisher = {IEEE Computer Society Press},
	organization = {IEEE Computer Society Press},
	address = {New Orleans, LA},
	author = {Anne Benoit and Valentin Le F{\`e}vre and Padma Raghavan and Yves Robert and Hongyang Sun}
}
@article {,
	title = {MAGMA Templates for Scalable Linear Algebra on Emerging Architectures},
	journal = {The International Journal of High Performance Computing Applications},
	volume = {34},
	year = {2020},
	month = {2020-11},
	pages = {645-658},
	abstract = {With the acquisition and widespread use of more resources that rely on accelerator/wide vector{\textendash}based computing, there has been a strong demand for science and engineering applications to take advantage of these latest assets. This, however, has been extremely challenging due to the diversity of systems to support their extreme concurrency, complex memory hierarchies, costly data movement, and heterogeneous node architectures. To address these challenges, we design a programming model and describe its ease of use in the development of a new MAGMA Templates library that delivers high-performance scalable linear algebra portable on current and emerging architectures. MAGMA Templates derives its performance and portability by (1) building on existing state-of-the-art linear algebra libraries, like MAGMA, SLATE, Trilinos, and vendor-optimized math libraries, and (2) providing access (seamlessly to the users) to the latest algorithms and architecture-specific optimizations through a single, easy-to-use C++-based API.},
	issn = {1094-3420},
	doi = {https://doi.org/10.1177/1094342020938421},
	author = {Mohammed Al Farhan and Ahmad Abdelfattah and Stanimire Tomov and Mark Gates and Dalal Sukkari and Azzam Haidar and Robert Rosenberg and Jack Dongarra}
}
@techreport {1454,
	title = {Performance Tuning SLATE},
	journal = {SLATE Working Notes},
	number = {14,  ICL-UT-20-01},
	year = {2020},
	month = {2020-01},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	author = {Mark Gates and Ali Charara and Asim YarKhan and Dalal Sukkari and Mohammed Al Farhan and Jack Dongarra}
}
@conference {,
	title = {Reservation and Checkpointing Strategies for Stochastic Jobs},
	booktitle = {34th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2020)},
	year = {2020},
	month = {2020-05},
	publisher = {IEEE Computer Society Press},
	organization = {IEEE Computer Society Press},
	address = {New Orleans, LA},
	author = {Ana Gainaru and Brice Goglin and Valentin Honor{\'e} and Padma Raghavan and Guillaume Pallez and Padma Raghavan and Yves Robert and Hongyang Sun}
}
@techreport {,
	title = {SLATE Performance Report: Updates to Cholesky and LU Factorizations},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-20-14},
	year = {2020},
	month = {2020-10},
	publisher = {University of Tennessee},
	author = {Asim YarKhan and Mohammed Al Farhan and Dalal Sukkari and Mark Gates and Jack Dongarra}
}
@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}
}
@techreport {,
	title = {A Survey of Numerical Methods Utilizing Mixed Precision Arithmetic},
	journal = {SLATE Working Notes},
	number = {15, ICL-UT-20-08},
	year = {2020},
	month = {2020-07},
	publisher = {University of Tennessee},
	type = {SLATE Working Notes},
	author = {Ahmad Abdelfattah and Hartwig Anzt and Erik Boman and Erin Carson and Terry Cojean and Jack Dongarra and Mark Gates and Thomas Gruetzmacher and Nicholas J. Higham and Sherry Li and Neil Lindquist and Yang Liu and Jennifer Loe and Piotr Luszczek and Pratik Nayak and Sri Pranesh and Siva Rajamanickam and Tobias Ribizel and Barry Smith and Kasia Swirydowicz and Stephen Thomas and Stanimire Tomov and Yaohung Tsai and Ichitaro Yamazaki and Urike Meier Yang}
}
@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}
}
@conference {1484,
	title = {Using Arm Scalable Vector Extension to Optimize Open MPI},
	booktitle = {20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID 2020)},
	year = {2020},
	month = {2020-05},
	publisher = {IEEE/ACM},
	organization = {IEEE/ACM},
	address = {Melbourne, Australia},
	abstract = {As the scale of high-performance computing (HPC) systems continues to grow, increasing levels of parallelism must be implored to achieve optimal performance. Recently, the processors support wide vector extensions, vectorization becomes much more important to exploit the potential peak performance of target architecture. Novel processor architectures, such as the Armv8-A architecture, introduce Scalable Vector Extension (SVE) - an optional separate architectural extension with a new set of A64 instruction encodings, which enables even greater parallelisms. In this paper, we analyze the usage and performance of the SVE instructions in Arm SVE vector Instruction Set Architecture (ISA); and utilize those instructions to improve the memcpy and various local reduction operations. Furthermore, we propose new strategies to improve the performance of MPI operations including datatype packing/unpacking and MPI reduction. With these optimizations, we not only provide a higher-parallelism for a single node, but also achieve a more efficient communication scheme of message exchanging. The resulting efforts have been implemented in the context of OPEN MPI, providing efficient and scalable capabilities of SVE usage and extending the possible implementations of SVE to a more extensive range of programming and execution paradigms. The evaluation of the resulting software stack under different scenarios with both simulator and Fujitsu{\textquoteright}s A64FX processor demonstrates that the solution is at the same time generic and efficient.},
	keywords = {ARMIE, datatype pack and unpack, local reduction, non-contiguous accesses, SVE, Vector Length Agnostic},
	doi = {https://doi.org/10.1109/CCGrid49817.2020.00-71},
	author = {Dong Zhong and Pavel Shamis and Qinglei Cao and George Bosilca and Jack Dongarra}
}
@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 {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}
}
@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 {1322,
	title = {Design and Implementation for FFT-ECP on Distributed Accelerated Systems},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-19-05},
	year = {2019},
	month = {2019-04},
	publisher = {University of Tennessee},
	type = {ECP WBS 2.3.3.09 Milestone Report},
	author = {Stanimire Tomov and Azzam Haidar and Alan Ayala and Daniel Schultz and Jack Dongarra}
}
@article {1329,
	title = {FFT-ECP Fast Fourier Transform},
	year = {2019},
	month = {2019-01},
	publisher = {2019 ECP Annual Meeting (Research Poster)},
	address = {Houston, TX},
	author = {Stanimire Tomov and Azzam Haidar and Alan Ayala and Daniel Schultz and Jack Dongarra}
}
@techreport {1401,
	title = {FFT-ECP Implementation Optimizations and Features Phase},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-19-12},
	year = {2019},
	month = {2019-10},
	publisher = {University of Tennessee},
	author = {Stanimire Tomov and Azzam Haidar and Alan Ayala and Hejer Shaiek and Jack Dongarra}
}
@article {1385,
	title = {GPUDirect MPI Communications and Optimizations to Accelerate FFTs on Exascale Systems},
	journal = {EuroMPI{\textquoteright}19 Posters, Zurich, Switzerland},
	number = {icl-ut-19-06},
	year = {2019},
	month = {2019-09},
	publisher = {ICL},
	type = {Extended Abstract},
	abstract = {Fast Fourier transforms (FFTs) are used in applications ranging from molecular dynamics and spectrum estimation to machine learn- ing, fast convolution and correlation, signal modulation, wireless multimedia applications, and others. However, FFTs are memory bound, and therefore, to accelerate them, it is crucial to avoid and optimize the FFTs{\textquoteright} communications. To this end, we present a 3-D FFT design for distributed graphics processing unit (GPU) systems that: (1) efficiently uses GPUs{\textquoteright} high bandwidth, (2) reduces global communications algorithmically, when possible, and (3) employs GPUDirect technologies as well as MPI optimizations in the development of high-performance FFTs for large-scale GPU-accelerated systems. We show that these developments and optimizations lead to very good strong scalability and a performance that is close to 90\% of the theoretical peak.},
	keywords = {CUDA-Aware MPI, ECP, FFT, FFT-ECP, gpu, GPUDirect},
	author = {Hejer Shaiek and Stanimire Tomov and Alan Ayala and Azzam Haidar and Jack Dongarra}
}
@conference {1403,
	title = {Impacts of Multi-GPU MPI Collective Communications on Large FFT Computation},
	booktitle = {Workshop on Exascale MPI (ExaMPI) at SC19},
	year = {2019},
	month = {2019-11},
	address = {Denver, CO},
	keywords = {Collective MPI, Exascale applications, FFT, Heterogeneous systems, scalable},
	author = {Alan Ayala and Stanimire Tomov and Xi Luo and Hejer Shaiek and Azzam Haidar and George Bosilca 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}
}
@conference {1316,
	title = {Reservation Strategies for Stochastic Jobs},
	booktitle = {33rd IEEE International Parallel and Distributed Processing Symposium (IPDPS 2019)},
	year = {2019},
	month = {2019-05},
	publisher = {IEEE Computer Society Press},
	organization = {IEEE Computer Society Press},
	address = {Rio de Janeiro, Brazil},
	author = {Guillaume Aupy and Ana Gainaru and Valentin Honor{\'e} and Padma Raghavan and Yves Robert and Hongyang Sun}
}
@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}
}
@inbook {1302,
	title = {System Software for Many-Core and Multi-Core Architectures},
	booktitle = {Advanced Software Technologies for Post-Peta Scale Computing: The Japanese Post-Peta CREST Research Project},
	year = {2019},
	pages = {59{\textendash}75},
	publisher = {Springer Singapore},
	organization = {Springer Singapore},
	address = {Singapore},
	abstract = {In this project, the software technologies for the post-peta scale computing were explored. More specifically, OS technologies for heterogeneous architectures, lightweight thread, scalable I/O, and fault mitigation were investigated. As for the OS technologies, a new parallel execution model, Partitioned Virtual Address Space (PVAS), for the many-core CPU was proposed. For the heterogeneous architectures, where multi-core CPU and many-core CPU are connected with an I/O bus, an extension of PVAS, Multiple-PVAS, to have a unified virtual address space of multi-core and many-core CPUs was proposed. The proposed PVAS was also enhanced to have multiple processes where process context switch can take place at the user level (named User-Level Process: ULP). As for the scalable I/O, EARTH, optimization techniques for MPI collective I/O, was proposed. Lastly, for the fault mitigation, User Level Fault Mitigation, ULFM was improved to have faster agreement process, and sliding methods to substitute failed nodes with spare nodes was proposed. The funding of this project was ended in 2016; however, many proposed technologies are still being propelled.},
	isbn = {978-981-13-1924-2},
	doi = {10.1007/978-981-13-1924-2_4},
	url = {https://doi.org/10.1007/978-981-13-1924-2_4},
	author = {Atsushi Hori and Tsujita, Yuichi and Shimada, Akio and Yoshinaga, Kazumi and Mitaro, Namiki and Fukazawa, Go and Sato, Mikiko and George Bosilca and Aurelien Bouteiller and Thomas Herault},
	editor = {Sato, Mitsuhisa}
}
@article {1331,
	title = {Accelerating 2D FFT: Exploit GPU Tensor Cores through Mixed-Precision},
	year = {2018},
	month = {2018-11},
	publisher = {The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18), ACM Student Research Poster},
	address = {Dallas, TX},
	author = {Xaiohe Cheng and Anumeena Soma and Eduardo D{\textquoteright}Azevedo and Kwai Wong and Stanimire Tomov}
}
@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}
}
@article {1263,
	title = {Computational Benefit of GPU Optimization for Atmospheric Chemistry Modeling},
	journal = {Journal of Advances in Modeling Earth Systems},
	volume = {10},
	year = {2018},
	month = {2018-08},
	pages = {1952{\textendash}1969},
	abstract = {Global chemistry-climate models are computationally burdened as the chemical mechanisms become more complex and realistic. Optimization for graphics processing units (GPU) may make longer global simulation with regional detail possible, but limited study has been done to explore the potential benefit for the atmospheric chemistry modeling. Hence, in this study, the second-order Rosenbrock solver of the chemistry module of CAM4-Chem is ported to the GPU to gauge potential speed-up. We find that on the CPU, the fastest performance is achieved using the Intel compiler with a block interleaved memory layout. Different combinations of compiler and memory layout lead to ~11.02{\texttimes} difference in the computational time. In contrast, the GPU version performs the best when using a combination of fully interleaved memory layout with block size equal to the warp size, CUDA streams for independent kernels, and constant memory. Moreover, the most efficient data transfer between CPU and GPU is gained by allocating the memory contiguously during the data initialization on the GPU. Compared to one CPU core, the speed-up of using one GPU alone reaches a factor of ~11.7{\texttimes} for the computation alone and ~3.82{\texttimes} when the data transfer between CPU and GPU is considered. Using one GPU alone is also generally faster than the multithreaded implementation for 16 CPU cores in a compute node and the single-source solution (OpenACC). The best performance is achieved by the implementation of the hybrid CPU/GPU version, but rescheduling the workload among the CPU cores is required before the practical CAM4-Chem simulation.},
	keywords = {compiler, CUDA, data transfer, gpu, hybrid, memory layout},
	doi = {https://doi.org/10.1029/2018MS001276},
	author = {Jian Sun and Joshua Fu and John Drake and Qingzhao Zhu and Azzam Haidar and Mark Gates and Stanimire Tomov and Jack Dongarra}
}
@article {1218,
	title = {Coping with Silent and Fail-Stop Errors at Scale by Combining Replication and Checkpointing},
	journal = {Journal of Parallel and Distributed Computing},
	volume = {122},
	year = {2018},
	month = {2018-12},
	pages = {209{\textendash}225},
	abstract = {This paper provides a model and an analytical study of replication as a technique to cope with silent errors, as well as a mixture of both silent and fail-stop errors on large-scale platforms. Compared with fail-stop errors that are immediately detected when they occur, silent errors require a detection mechanism. To detect silent errors, many application-specific techniques are available, either based on algorithms (e.g., ABFT), invariant preservation or data analytics, but replication remains the most transparent and least intrusive technique. We explore the right level (duplication, triplication or more) of replication for two frameworks: (i) when the platform is subject to only silent errors, and (ii) when the platform is subject to both silent and fail-stop errors. A higher level of replication is more expensive in terms of resource usage but enables to tolerate more errors and to even correct some errors, hence there is a trade-off to be found. Replication is combined with checkpointing and comes with two flavors: process replication and group replication. Process replication applies to message-passing applications with communicating processes. Each process is replicated, and the platform is composed of process pairs, or triplets. Group replication applies to black-box applications, whose parallel execution is replicated several times. The platform is partitioned into two halves (or three thirds). In both scenarios, results are compared before each checkpoint, which is taken only when both results (duplication) or two out of three results (triplication) coincide. Otherwise, one or more silent errors have been detected, and the application rolls back to the last checkpoint, as well as when fail-stop errors have struck. We provide a detailed analytical study for all of these scenarios, with formulas to decide, for each scenario, the optimal parameters as a function of the error rate, checkpoint cost, and platform size. We also report a set of extensive simulation results that nicely corroborates the analytical model.},
	keywords = {checkpointing, fail-stop errors, Fault tolerance, High-performance computing, Replication, silent errors},
	doi = {https://doi.org/10.1016/j.jpdc.2018.08.002},
	author = {Anne Benoit and Aurelien Cavelan and Franck Cappello and Padma Raghavan and Yves Robert and Hongyang Sun}
}
@article {1198,
	title = {Co-Scheduling Amdhal Applications on Cache-Partitioned Systems},
	journal = {International Journal of High Performance Computing Applications},
	volume = {32},
	year = {2018},
	month = {2018-01},
	pages = {123{\textendash}138},
	abstract = {Cache-partitioned architectures allow subsections of the shared last-level cache (LLC) to be exclusively reserved for some applications. This technique dramatically limits interactions between applications that are concurrently executing on a multicore machine. Consider n applications that execute concurrently, with the objective to minimize the makespan, defined as the maximum completion time of the n applications. Key scheduling questions are as follows: (i) which proportion of cache and (ii) how many processors should be given to each application? In this article, we provide answers to (i) and (ii) for Amdahl applications. Even though the problem is shown to be NP-complete, we give key elements to determine the subset of applications that should share the LLC (while remaining ones only use their smaller private cache). Building upon these results, we design efficient heuristics for Amdahl applications. Extensive simulations demonstrate the usefulness of co-scheduling when our efficient cache partitioning strategies are deployed.},
	keywords = {cache partitioning, co-scheduling, complexity results},
	doi = {https://doi.org/10.1177/1094342017710806},
	author = {Guillaume Aupy and Anne Benoit and Sicheng Dai and Lo{\"\i}c Pottier and Padma Raghavan and Yves Robert and Manu Shantharam}
}
@techreport {1232,
	title = {Evaluation and Design of FFT for Distributed Accelerated Systems},
	journal = {ECP WBS 2.3.3.09 Milestone Report},
	number = {FFT-ECP ST-MS-10-1216},
	year = {2018},
	month = {2018-10},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	author = {Stanimire Tomov and Azzam Haidar and Daniel Schultz and Jack Dongarra}
}
@article {1089,
	title = {A Failure Detector for HPC Platforms},
	journal = {The International Journal of High Performance Computing Applications},
	volume = {32},
	year = {2018},
	month = {2018-01},
	pages = {139{\textendash}158},
	abstract = {Building an infrastructure for exascale applications requires, in addition to many other key components, a stable and efficient failure detector. This article describes the design and evaluation of a robust failure detector that can maintain and distribute the correct list of alive resources within proven and scalable bounds. The detection and distribution of the fault information follow different overlay topologies that together guarantee minimal disturbance to the applications. A virtual observation ring minimizes the overhead by allowing each node to be observed by another single node, providing an unobtrusive behavior. The propagation stage uses a nonuniform variant of a reliable broadcast over a circulant graph overlay network and guarantees a logarithmic fault propagation. Extensive simulations, together with experiments on the Titan Oak Ridge National Laboratory supercomputer, show that the algorithm performs extremely well and exhibits all the desired properties of an exascale-ready algorithm.},
	keywords = {failure detection, Fault tolerance, MPI},
	doi = {https://doi.org/10.1177/1094342017711505},
	author = {George Bosilca and Aurelien Bouteiller and Amina Guermouche and Thomas Herault and Yves Robert and Pierre Sens and Jack Dongarra}
}
@conference {1235,
	title = {High-Performance GPU Implementation of PageRank with Reduced Precision based on Mantissa Segmentation},
	booktitle = {8th Workshop on Irregular Applications: Architectures and Algorithms},
	year = {2018},
	author = {Anzt, Hartwig and Thomas Gruetzmacher and Enrique S. Quintana-Orti and Scheidegger, Florian}
}
@article {1239,
	title = {Multi-Level Checkpointing and Silent Error Detection for Linear Workflows},
	journal = {Journal of Computational Science},
	volume = {28},
	year = {2018},
	month = {2018-09},
	pages = {398{\textendash}415},
	abstract = {We focus on High Performance Computing (HPC) workflows whose dependency graph forms a linear chain, and we extend single-level checkpointing in two important directions. Our first contribution targets silent errors, and combines in-memory checkpoints with both partial and guaranteed verifications. Our second contribution deals with multi-level checkpointing for fail-stop errors. We present sophisticated dynamic programming algorithms that return the optimal solution for each problem in polynomial time. We also show how to combine all these techniques and solve the problem with both fail-stop and silent errors. Simulation results demonstrate that these extensions lead to significantly improved performance compared to the standard single-level checkpointing algorithm.},
	author = {Anne Benoit and Aurelien Cavelan and Yves Robert and Hongyang Sun}
}
@article {1227,
	title = {PMIx: Process Management for Exascale Environments},
	journal = {Parallel Computing},
	volume = {79},
	year = {2018},
	month = {2018-01},
	pages = {9{\textendash}29},
	issn = {01678191},
	doi = {10.1016/j.parco.2018.08.002},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0167819118302424https://api.elsevier.com/content/article/PII:S0167819118302424?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0167819118302424?httpAccept=text/plain},
	author = {Ralph Castain and Joshua Hursey and Aurelien Bouteiller and David Solt}
}
@article {1231,
	title = {A Survey of MPI Usage in the US Exascale Computing Project},
	journal = {Concurrency Computation: Practice and Experience},
	year = {2018},
	month = {2018-09},
	type = {Special Issue},
	abstract = {The Exascale Computing Project (ECP) is currently the primary effort in theUnited States focused on developing {\textquotedblleft}exascale{\textquotedblright} levels of computing capabilities, including hardware, software, and applications. In order to obtain amore thorough understanding of how the software projects under the ECPare using, and planning to use theMessagePassing Interface (MPI), and help guide the work of our own project within the ECP, we created a survey.Of the 97 ECP projects active at the time the survey was distributed, we received 77 responses, 56 of which reported that their projects were usingMPI. This paper reports the results of that survey for the benefit of the broader community of MPI developers.},
	keywords = {exascale, MPI},
	doi = {https://doi.org/10.1002/cpe.4851},
	author = {David E. Bernholdt and Swen Boehm and George Bosilca and Manjunath Gorentla Venkata and Ryan E. Grant and Thomas Naughton and Howard P. Pritchard and Martin Schulz and Geoffroy R. Vallee}
}
@article {1221,
	title = {Using Jacobi Iterations and Blocking for Solving Sparse Triangular Systems in Incomplete Factorization Preconditioning},
	journal = {Journal of Parallel and Distributed Computing},
	volume = {119},
	year = {2018},
	month = {2018-11},
	pages = {219{\textendash}230},
	abstract = {When using incomplete factorization preconditioners with an iterative method to solve large sparse linear systems, each application of the preconditioner involves solving two sparse triangular systems. These triangular systems are challenging to solve efficiently on computers with high levels of concurrency. On such computers, it has recently been proposed to use Jacobi iterations, which are highly parallel, to approximately solve the triangular systems from incomplete factorizations. The effectiveness of this approach, however, is problem-dependent: the Jacobi iterations may not always converge quickly enough for all problems. Thus, as a necessary and important step to evaluate this approach, we experimentally test the approach on a large number of realistic symmetric positive definite problems. We also show that by using block Jacobi iterations, we can extend the range of problems for which such an approach can be effective. For block Jacobi iterations, it is essential for the blocking to be cognizant of the matrix structure.},
	doi = {https://doi.org/10.1016/j.jpdc.2018.04.017},
	author = {Edmond Chow and Hartwig Anzt and Jennifer Scott and Jack Dongarra}
}
@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 {1094,
	title = {Co-Scheduling Algorithms for Cache-Partitioned Systems},
	booktitle = {19th Workshop on Advances in Parallel and Distributed Computational Models},
	year = {2017},
	month = {2017-05},
	publisher = {IEEE Computer Society Press},
	organization = {IEEE Computer Society Press},
	address = {Orlando, FL},
	abstract = {Cache-partitioned architectures allow subsections of the shared last-level cache (LLC) to be exclusively reserved for some applications. This technique dramatically limits interactions between applications that are concurrently executing on a multicore machine. Consider n applications that execute concurrently, with the objective to minimize the makespan, defined as the maximum completion time of the n applications. Key scheduling questions are: (i) which proportion of cache and (ii) how many processors should be given to each application? Here, we assign rational numbers of processors to each application, since they can be shared across applications through multi-threading. In this paper, we provide answers to (i) and (ii) for perfectly parallel applications. Even though the problem is shown to be NP-complete, we give key elements to determine the subset of applications that should share the LLC (while remaining ones only use their smaller private cache). Building upon these results, we design efficient heuristics for general applications. Extensive simulations demonstrate the usefulness of co-scheduling when our efficient cache partitioning strategies are deployed.},
	keywords = {Computational modeling, Degradation, Interference, Mathematical model, Program processors, Supercomputers, Throughput},
	doi = {10.1109/IPDPSW.2017.60},
	author = {Guillaume Aupy and Anne Benoit and Lo{\"\i}c Pottier and Padma Raghavan and Yves Robert and Manu Shantharam}
}
@conference {1096,
	title = {Identifying the Right Replication Level to Detect and Correct Silent Errors at Scale},
	booktitle = {2017 Workshop on Fault-Tolerance for HPC at Extreme Scale},
	year = {2017},
	month = {2017-06},
	publisher = {ACM},
	organization = {ACM},
	address = {Washington, DC},
	abstract = {This paper provides a model and an analytical study of replication as a technique to detect and correct silent errors. Although other detection techniques exist for HPC applications, based on algorithms (ABFT), invariant preservation or data analytics, replication remains the most transparent and least intrusive technique. We explore the right level (duplication, triplication or more) of replication needed to efficiently detect and correct silent errors. Replication is combined with checkpointing and comes with two flavors: process replication and group replication. Process replication applies to message-passing applications with communicating processes. Each process is replicated, and the platform is composed of process pairs, or triplets. Group replication applies to black-box applications, whose parallel execution is replicated several times. The platform is partitioned into two halves (or three thirds). In both scenarios, results are compared before each checkpoint, which is taken only when both results (duplication) or two out of three results (triplication) coincide. If not, one or more silent errors have been detected, and the application rolls back to the last checkpoint. We provide a detailed analytical study of both scenarios, with formulas to decide, for each scenario, the optimal parameters as a function of the error rate, checkpoint cost, and platform size. We also report a set of extensive simulation results that corroborates the analytical model.},
	doi = {10.1145/3086157.3086162},
	author = {Anne Benoit and Franck Cappello and Aurelien Cavelan and Yves Robert and Hongyang Sun}
}
@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}
}
@inproceedings {1307,
	title = {PMIx: Process Management for Exascale Environments},
	journal = {Proceedings of the 24th European MPI Users{\textquoteright} Group Meeting},
	year = {2017},
	pages = {14:1{\textendash}14:10},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {High-Performance Computing (HPC) applications have historically executed in static resource allocations, using programming models that ran independently from the resident system management stack (SMS). Achieving exascale performance that is both cost-effective and fits within site-level environmental constraints will, however, require that the application and SMS collaboratively orchestrate the flow of work to optimize resource utilization and compensate for on-the-fly faults. The Process Management Interface - Exascale (PMIx) community is committed to establishing scalable workflow orchestration by defining an abstract set of interfaces by which not only applications and tools can interact with the resident SMS, but also the various SMS components can interact with each other. This paper presents a high-level overview of the goals and current state of the PMIx standard, and lays out a roadmap for future directions.
},
	isbn = {978-1-4503-4849-2},
	doi = {10.1145/3127024.3127027},
	url = {http://doi.acm.org/10.1145/3127024.3127027},
	author = {Castain, Ralph H. and David Solt and Joshua Hursey and Aurelien Bouteiller}
}
@article {1090,
	title = {Towards Optimal Multi-Level Checkpointing},
	journal = {IEEE Transactions on Computers},
	volume = {66},
	year = {2017},
	month = {2017-07},
	pages = {1212{\textendash}1226},
	keywords = {checkpointing, Dynamic programming, Error analysis, Heuristic algorithms, Optimized production technology, protocols, Shape},
	doi = {10.1109/TC.2016.2643660},
	author = {Anne Benoit and Aurelien Cavelan and Valentin Le F{\`e}vre and Yves Robert and Hongyang Sun}
}
@article {933,
	title = {Assessing General-purpose Algorithms to Cope with Fail-stop and Silent Errors},
	journal = {ACM Transactions on Parallel Computing},
	year = {2016},
	month = {2016-08},
	abstract = {In this paper, we combine the traditional checkpointing and rollback recovery strategies with verification mechanisms to cope with both fail-stop and silent errors. The objective is to minimize makespan and/or energy consumption. For divisible load applications, we use first-order approximations to find the optimal checkpointing period to minimize execution time, with an additional verification mechanism to detect silent
errors before each checkpoint, hence extending the classical formula by Young and Daly for fail-stop errors only. We further extend the approach to include intermediate verifications, and to consider a bi-criteria problem involving both time and energy (linear combination of execution time and energy consumption). Then, we focus on application workflows whose dependence graph is a linear chain of tasks. Here, we determine the optimal checkpointing and verification locations, with or without intermediate verifications, for the bicriteria problem. Rather than using a single speed during the whole execution, we further introduce a new execution scenario, which allows for changing the execution speed via dynamic voltage and frequency scaling (DVFS). We determine in this scenario the optimal checkpointing and verification locations, as well as the optimal speed pairs. Finally, we conduct an extensive set of simulations to support the theoretical study, and to assess the performance of each algorithm, showing that the best overall performance is achieved under the most flexible scenario using intermediate verifications and different speeds.},
	keywords = {checkpoint, fail-stop error, failure, HPC, resilience, silent data corruption, silent error, verification},
	doi = {10.1145/2897189},
	author = {Anne Benoit and Aurelien Cavelan and Yves Robert and Hongyang Sun}
}
@inproceedings {996,
	title = {Domain Overlap for Iterative Sparse Triangular Solves on GPUs},
	journal = {Software for Exascale Computing - SPPEXA},
	volume = {113},
	year = {2016},
	month = {2016-09},
	pages = {527{\textendash}545},
	publisher = {Springer International Publishing},
	abstract = {Iterative methods for solving sparse triangular systems are an attractive alternative to exact forward and backward substitution if an approximation of the solution is acceptable. On modern hardware, performance benefits are available as iterative methods allow for better parallelization. In this paper, we investigate how block-iterative triangular solves can benefit from using overlap. Because the matrices are triangular, we use {\textquotedblleft}directed{\textquotedblright} overlap, depending on whether the matrix is upper or lower triangular. We enhance a GPU implementation of the block-asynchronous Jacobi method with directed overlap. For GPUs and other cases where the problem must be overdecomposed, i.e., more subdomains and threads than cores, there is a preference in processing or scheduling the subdomains in a specific order, following the dependencies specified by the sparse triangular matrix. For sparse triangular factors from incomplete factorizations, we demonstrate that moderate directed overlap with subdomain scheduling can improve convergence and time-to-solution.},
	doi = {10.1007/978-3-319-40528-5_24},
	author = {Hartwig Anzt and Edmond Chow and Daniel Szyld and Jack Dongarra},
	editor = {Hans-Joachim Bungartz and Philipp Neumann and Wolfgang E. Nagel}
}
@inproceedings {979,
	title = {Failure Detection and Propagation in HPC Systems},
	journal = { Proceedings of the The International Conference for High Performance Computing, Networking, Storage and Analysis (SC{\textquoteright}16)},
	year = {2016},
	month = {2016-11},
	pages = {27:1-27:11},
	publisher = {IEEE Press},
	address = {Salt Lake City, Utah},
	keywords = {failure detection, fault-tolerance, MPI},
	isbn = {978-1-4673-8815-3},
	url = {http://dl.acm.org/citation.cfm?id=3014904.3014941},
	author = {George Bosilca and Aurelien Bouteiller and Amina Guermouche and Thomas Herault and Yves Robert and Pierre Sens and Jack Dongarra}
}
@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}
}
@conference {963,
	title = {LU, QR, and Cholesky Factorizations: Programming Model, Performance Analysis and Optimization Techniques for the Intel Knights Landing Xeon Phi},
	booktitle = {IEEE High Performance Extreme Computing Conference (HPEC{\textquoteright}16)},
	year = {2016},
	month = {2016-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Waltham, MA},
	abstract = {A wide variety of heterogeneous compute resources, ranging from multicore CPUs to GPUs and coprocessors, are available to modern computers, making it challenging to design unified numerical libraries that efficiently and productively use all these varied resources. For example, in order to efficiently use Intel{\textquoteright}s Knights Langing (KNL) processor, the next-generation of Xeon Phi architectures, one must design and schedule an application in multiple degrees of parallelism and task grain sizes in order to obtain efficient performance. We propose a productive and portable programming model that allows us to write a serial-looking code, which, however, achieves parallelism and scalability by using a lightweight runtime environment to manage the resource-specific workload, and to control the dataflow and the parallel execution. This is done through multiple techniques ranging from multi-level data partitioning to adaptive task grain sizes, and dynamic task scheduling. In addition, our task abstractions enable unified algorithmic development across all the heterogeneous resources. Finally, we outline the strengths and the effectiveness of this approach {\textendash} especially in regards to hardware trends and ease of programming high-performance numerical software that current applications need {\textendash} in order to motivate current work and future directions for the next generation of parallel programming models for high-performance linear algebra libraries on heterogeneous systems.},
	author = {Azzam Haidar and Stanimire Tomov and Konstantin Arturov and Murat Guney and Shane Story and Jack Dongarra}
}
@conference {930,
	title = {Optimal Resilience Patterns to Cope with Fail-stop and Silent Errors},
	booktitle = {2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
	year = {2016},
	month = {2016-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Chicago, IL},
	abstract = {This work focuses on resilience techniques at extreme scale. Many papers deal with fail-stop errors. Many others deal with silent errors (or silent data corruptions). But very few papers deal with fail-stop and silent errors simultaneously. However, HPC applications will obviously have to cope with both error sources. This paper presents a unified framework and optimal algorithmic solutions to this double challenge. Silent errors are handled via verification mechanisms (either partially or fully accurate) and in-memory checkpoints. Fail-stop errors are processed via disk checkpoints. All verification and checkpoint types are combined into computational patterns. We provide a unified model, and a full characterization of the optimal pattern. Our results nicely extend several published solutions and demonstrate how to make use of different techniques to solve the double threat of fail-stop and silent errors. Extensive simulations based on real data confirm the accuracy of the model, and show that patterns that combine all resilience mechanisms are required to provide acceptable overheads.},
	keywords = {fail-stop errors, multilevel checkpoint, optimal pattern, resilience, silent errors, verification},
	doi = {10.1109/IPDPS.2016.39},
	author = {Anne Benoit and Aurelien Cavelan and Yves Robert and Hongyang Sun}
}
@conference {968,
	title = {Performance Analysis and Acceleration of Explicit Integration for Large Kinetic Networks using Batched GPU Computations},
	booktitle = {2016 IEEE High Performance Extreme Computing Conference (HPEC {\textquoteleft}16)},
	year = {2016},
	month = {2016-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Waltham, MA},
	abstract = {We demonstrate the systematic implementation of recently-developed fast explicit kinetic integration algorithms that solve efficiently N coupled ordinary differential equations (subject to initial conditions) on modern GPUs. We take representative test cases (Type Ia supernova explosions) and demonstrate two or more orders of magnitude increase in efficiency for solving such systems (of realistic thermonuclear networks coupled to fluid dynamics). This implies that important coupled, multiphysics problems in various scientific and technical disciplines that were intractable, or could be simulated only with highly schematic kinetic networks, are now computationally feasible. As examples of such applications we present the computational techniques developed for our ongoing deployment of these new methods on modern GPU accelerators. We show that similarly to many other scientific applications, ranging from national security to medical advances, the computation can be split into many independent computational tasks, each of relatively small-size. As the size of each individual task does not provide sufficient parallelism for the underlying hardware, especially for accelerators, these tasks must be computed concurrently as a single routine, that we call batched routine, in order to saturate the hardware with enough work.},
	author = {Azzam Haidar and Benjamin Brock and Stanimire Tomov and Michael Guidry and Jay Jay Billings and Daniel Shyles and Jack Dongarra}
}
@article {995,
	title = {Updating Incomplete Factorization Preconditioners for Model Order Reduction},
	journal = {Numerical Algorithms},
	volume = {73},
	number = {3},
	year = {2016},
	month = {2016-02},
	pages = {611{\textendash}630},
	abstract = {When solving a sequence of related linear systems by iterative methods, it is common to reuse the preconditioner for several systems, and then to recompute the preconditioner when the matrix has changed significantly. Rather than recomputing the preconditioner from scratch, it is potentially more efficient to update the previous preconditioner. Unfortunately, it is not always known how to update a preconditioner, for example, when the preconditioner is an incomplete factorization. A recently proposed iterative algorithm for computing incomplete factorizations, however, is able to exploit an initial guess, unlike existing algorithms for incomplete factorizations. By treating a previous factorization as an initial guess to this algorithm, an incomplete factorization may thus be updated. We use a sequence of problems from model order reduction. Experimental results using an optimized GPU implementation show that updating a previous factorization can be inexpensive and effective, making solving sequences of linear systems a potential niche problem for the iterative incomplete factorization algorithm.},
	keywords = {key publication},
	doi = {10.1007/s11075-016-0110-2},
	author = {Hartwig Anzt and Edmond Chow and Jens Saak and Jack Dongarra}
}
@article {866,
	title = {Acceleration of GPU-based Krylov solvers via Data Transfer Reduction},
	journal = {International Journal of High Performance Computing Applications},
	year = {2015},
	author = {Hartwig Anzt and William Sawyer and Stanimire Tomov and Piotr Luszczek 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}
}
@inproceedings {1310,
	title = {From MPI to OpenSHMEM: Porting LAMMPS},
	journal = {OpenSHMEM and Related Technologies. Experiences, Implementations, and Technologies},
	year = {2015},
	pages = {121{\textendash}137},
	publisher = {Springer International Publishing},
	address = {Annapolis, MD, USA},
	abstract = {This work details the opportunities and challenges of porting a Petascale, MPI-based application {\textendash}-LAMMPS{\textendash}- to OpenSHMEM. We investigate the major programming challenges stemming from the differences in communication semantics, address space organization, and synchronization operations between the two programming models. This work provides several approaches to solve those challenges for representative communication patterns in LAMMPS, e.g., by considering group synchronization, peer{\textquoteright}s buffer status tracking, and unpacked direct transfer of scattered data. The performance of LAMMPS is evaluated on the Titan HPC system at ORNL. The OpenSHMEM implementations are compared with MPI versions in terms of both strong and weak scaling. The results outline that OpenSHMEM provides a rich semantic to implement scalable scientific applications. In addition, the experiments demonstrate that OpenSHMEM can compete with, and often improve on, the optimized MPI implementation.},
	isbn = {978-3-319-26428-8},
	doi = {10.1007/978-3-319-26428-8_8},
	author = {Tang, Chunyan and Aurelien Bouteiller and Thomas Herault and Manjunath Gorentla Venkata and George Bosilca},
	editor = {Manjunath Gorentla Venkata and Shamis, Pavel and Imam, Neena and M. Graham Lopez}
}
@inbook {927,
	title = {High-Performance Computing},
	booktitle = {The Princeton Companion to Applied Mathematics},
	year = {2015},
	pages = {839-842},
	publisher = {Princeton University Press},
	organization = {Princeton University Press},
	address = {Princeton, New Jersey},
	isbn = {9781400874477},
	author = {Jack Dongarra and Nicholas J. Higham and Mark R. Dennis and Paul Glendinning and Paul A. Martin and Fadil Santosa and Jared Tanner}
}
@conference {893,
	title = {Random-Order Alternating Schwarz for Sparse Triangular Solves},
	booktitle = {2015 SIAM Conference on Applied Linear Algebra (SIAM LA)},
	year = {2015},
	month = {2015-10},
	publisher = {SIAM},
	organization = {SIAM},
	address = {Atlanta, GA},
	abstract = {Block-asynchronous Jacobi is an iteration method where a locally synchronous iteration is embedded in an asynchronous global iteration. The unknowns are partitioned into small subsets, and while the components within the same subset are iterated in Jacobi fashion, no update order in-between the subsets is enforced. The values of the non-local entries remain constant during the local iterations, which can result in slow inter-subset information propagation and slow convergence. Interpreting of the subsets as subdomains allows to transfer the concept of domain overlap typically enhancing the information propagation to block-asynchronous solvers. In this talk we explore the impact of overlapping domains to convergence and performance of block-asynchronous Jacobi iterations, and present results obtained by running this solver class on state-of-the-art HPC systems.},
	author = {Hartwig Anzt and Edmond Chow and Daniel Szyld and Jack Dongarra}
}
@article {925,
	title = {A Scalable Approach to Solving Dense Linear Algebra Problems on Hybrid CPU-GPU Systems},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {27},
	year = {2015},
	month = {2015-09},
	pages = {3702-3723},
	abstract = {Aiming to fully exploit the computing power of all CPUs and all graphics processing units (GPUs) on hybrid CPU-GPU systems to solve dense linear algebra problems, we design a class of heterogeneous tile algorithms to maximize the degree of parallelism, to minimize the communication volume, and to accommodate the heterogeneity between CPUs and GPUs. The new heterogeneous tile algorithms are executed upon our decentralized dynamic scheduling runtime system, which schedules a task graph dynamically and transfers data between compute nodes automatically. The runtime system uses a new distributed task assignment protocol to solve data dependencies between tasks without any coordination between processing units. By overlapping computation and communication through dynamic scheduling, we are able to attain scalable performance for the double-precision Cholesky factorization and QR factorization. Our approach demonstrates a performance comparable to Intel MKL on shared-memory multicore systems and better performance than both vendor (e.g., Intel MKL) and open source libraries (e.g., StarPU) in the following three environments: heterogeneous clusters with GPUs, conventional clusters without GPUs, and shared-memory systems with multiple GPUs.},
	keywords = {dense linear algebra, distributed dataflow scheduling, heterogeneous HPC systems, runtime systems},
	doi = {10.1002/cpe.3403},
	author = {Fengguang Song and Jack Dongarra}
}
@article {926,
	title = {The TOP500 List and Progress in High-Performance Computing},
	journal = {IEEE Computer},
	volume = {48},
	year = {2015},
	month = {2015-11},
	pages = {42-49},
	abstract = {For more than two decades, the TOP500 list has enjoyed incredible success as a metric for supercomputing performance and as a source of data for identifying technological trends. The project{\textquoteright}s editors reflect on its usefulness and limitations for guiding large-scale scientific computing into the exascale era.},
	keywords = {application performance, Benchmark testing, benchmarks, Computer architecture, High Performance Computing, High-performance computing, Linpack, Market research, Parallel computing, Program processors, Scientific computing, Supercomputers, top500},
	doi = {doi:10.1109/MC.2015.338},
	author = {Erich Strohmaier and Hans Meuer and Jack Dongarra and Horst D. Simon}
}
@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}
}
@article {815,
	title = {Communication-Avoiding Symmetric-Indefinite Factorization},
	journal = {SIAM Journal on Matrix Analysis and Application},
	volume = {35},
	year = {2014},
	month = {2014-07},
	pages = {1364-1406},
	abstract = {We describe and analyze a novel symmetric triangular factorization algorithm. The algorithm is essentially a block version of Aasen{\textquoteright}s triangular tridiagonalization. It factors a dense symmetric matrix A as the product A = P LT L T P T where P is a permutation matrix, L is lower triangular, and T is block tridiagonal and banded. The algorithm is the first symmetric-indefinite communication-avoiding factorization: it performs an asymptotically optimal amount of communication in a two-level memory hierarchy for almost any cache-line size. Adaptations of the algorithm to parallel computers are likely to be communication efficient as well; one such adaptation has been recently published. The current paper describes the algorithm, proves that it is numerically stable, and proves that it is communication optimal.},
	keywords = {plasma},
	author = {Grey Ballard and Dulceneia Becker and James Demmel and Jack Dongarra and Alex Druinsky and I Peled and Oded Schwartz and Sivan Toledo and Ichitaro Yamazaki}
}
@article {758,
	title = {A Novel Hybrid CPU-GPU Generalized Eigensolver for Electronic Structure Calculations Based on Fine Grained Memory Aware Tasks},
	journal = {International Journal of High Performance Computing Applications},
	volume = {28},
	year = {2014},
	month = {2014-05},
	pages = {196-209},
	chapter = {196},
	abstract = {The adoption of hybrid CPU{\textendash}GPU nodes in traditional supercomputing platforms such as the Cray-XK6 opens acceleration opportunities for electronic structure calculations in materials science and chemistry applications, where medium-sized generalized eigenvalue problems must be solved many times. These eigenvalue problems are too small to effectively solve on distributed systems, but can benefit from the massive computing power concentrated on a single-node, hybrid CPU{\textendash}GPU system. However, hybrid systems call for the development of new algorithms that efficiently exploit heterogeneity and massive parallelism of not just GPUs, but of multicore/manycore CPUs as well. Addressing these demands, we developed a generalized eigensolver featuring novel algorithms of increased computational intensity (compared with the standard algorithms), decomposition of the computation into fine-grained memory aware tasks, and their hybrid execution. The resulting eigensolvers are state-of-the-art in high-performance computing, significantly outperforming existing libraries. We describe the algorithm and analyze its performance impact on applications of interest when different fractions of eigenvectors are needed by the host electronic structure code. },
	keywords = {Eigensolver, electronic structure calculations, generalized eigensolver, gpu, high performance, hybrid, Multicore, two-stage},
	doi = {10.1177/1094342013502097 },
	author = {Azzam Haidar and Raffaele Solc{\`a} and Mark Gates and Stanimire Tomov and Thomas C. Schulthess and Jack Dongarra}
}
@conference {833,
	title = {Optimizing Krylov Subspace Solvers on Graphics Processing Units},
	booktitle = {Fourth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), IPDPS 2014},
	year = {2014},
	month = {2014-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Phoenix, AZ},
	abstract = {Krylov subspace solvers are often the method of choice when solving sparse linear systems iteratively. At the same time, hardware accelerators such as graphics processing units (GPUs) continue to offer significant floating point performance gains for matrix and vector computations through easy-to-use libraries of computational kernels. However, as these libraries are usually composed of a well optimized but limited set of linear algebra operations, applications that use them often fail to leverage the full potential of the accelerator. In this paper we target the acceleration of the BiCGSTAB solver for GPUs, showing that significant improvement can be achieved by reformulating the method and developing application-specific kernels instead of using the generic CUBLAS library provided by NVIDIA. We propose an implementation that benefits from a significantly reduced number of kernel launches and GPUhost communication events, by means of increased data locality and a simultaneous reduction of multiple scalar products. Using experimental data, we show that, depending on the dominance of the untouched sparse matrix vector products, significant performance improvements can be achieved compared to a reference implementation based on the CUBLAS library. We feel that such optimizations are crucial for the subsequent development of highlevel sparse linear algebra libraries.},
	author = {Stanimire Tomov and Piotr Luszczek and Ichitaro Yamazaki and Jack Dongarra and Hartwig Anzt and William Sawyer}
}
@inproceedings {835,
	title = {Scaling Up Matrix Computations on Shared-Memory Manycore Systems with 1000 CPU Cores},
	journal = {International conference on Supercomputing},
	year = {2014},
	month = {2014-06},
	pages = {333-342},
	publisher = {ACM},
	address = {Munich, Germany},
	abstract = {While the growing number of cores per chip allows researchers to solve larger scientific and engineering problems, the parallel efficiency of the deployed parallel software starts to decrease. This unscalability problem happens to both vendor-provided and open-source software and wastes CPU cycles and energy. By expecting CPUs with hundreds of cores to be imminent, we have designed a new framework to perform matrix computations for massively many cores. Our performance analysis on manycore systems shows that the unscalability bottleneck is related to Non-Uniform Memory Access (NUMA): memory bus contention and remote memory access latency. To overcome the bottleneck, we have designed NUMA-aware tile algorithms with the help of a dynamic scheduling runtime system to minimize NUMA memory accesses. The main idea is to identify the data that is, either read a number of times or written once by a thread resident on a remote NUMA node, then utilize the runtime system to conduct data caching and movement between different NUMA nodes. Based on the experiments with QR factorizations, we demonstrate that our framework is able to achieve great scalability on a 48-core AMD Opteron system (e.g., parallel efficiency drops only 3\% from one core to 48 cores). We also deploy our framework to an extreme-scale shared-memory SGI machine which has 1024 CPU cores and runs a single Linux operating system image. Our framework continues to scale well, and can outperform the vendor-optimized Intel MKL library by up to 750\%.},
	isbn = {978-1-4503-2642-1},
	doi = {10.1145/2597652.2597670},
	author = {Fengguang Song and Jack Dongarra}
}
@article {icl:538,
	title = {Enabling Workflows in GridSolve: Request Sequencing and Service Trading},
	journal = {Journal of Supercomputing},
	volume = {64},
	year = {2013},
	month = {2013-06},
	pages = {1133-1152},
	chapter = {1133},
	abstract = {GridSolve employs a RPC-based client-agent-server model for solving computational problems. There are two deficiencies associated with GridSolve when a computational problem essentially forms a workflow consisting of a sequence of tasks with data dependencies between them. First, intermediate results are always passed through the client, resulting in unnecessary data transport. Second, since the execution of each individual task is a separate RPC session, it is difficult to enable any potential parallelism among tasks. This paper presents a request sequencing technique that addresses these deficiencies and enables workflow executions. Building on the request sequencing work, one way to generate workflows is by taking higher level service requests and decomposing them into a sequence of simpler service requests using a technique called service trading. A service trading component is added to GridSolve to take advantage of the new dynamic request sequencing. The features described here include automatic DAG construction and data dependency analysis, direct interserver data transfer, parallel task execution capabilities, and a service trading component.},
	keywords = {grid computing, gridpac, netsolve, service trading, workflow applications},
	issn = {1573-0484},
	doi = {10.1007/s11227-010-0549-1},
	author = {Yinan Li and Asim YarKhan and Jack Dongarra and Keith Seymour and Aurlie Hurault}
}
@article {icl:691,
	title = {Implementing a Blocked Aasen{\textquoteright}s Algorithm with a Dynamic Scheduler on Multicore Architectures},
	journal = {IPDPS 2013 (submitted)},
	year = {2013},
	month = {2013-00},
	address = {Boston, MA},
	abstract = {Factorization of a dense symmetric indefinite matrix is a key computational kernel in many scientific and engineering simulations. However, there is no scalable factorization algorithm that takes advantage of the symmetry and guarantees numerical stability through pivoting at the same time. This is because such an algorithm exhibits many of the fundamental challenges in parallel programming like irregular data accesses and irregular task dependencies. In this paper, we address these challenges in a tiled implementation of a blocked Aasen{\textquoteright}s algorithm using a dynamic scheduler. To fully exploit the limited parallelism in this left-looking algorithm, we study several performance enhancing techniques; e.g., parallel reduction to update a panel, tall-skinny LU factorization algorithms to factorize the panel, and a parallel implementation of symmetric pivoting. Our performance results on up to 48 AMD Opteron processors demonstrate that our implementation obtains speedups of up to 2.8 over MKL, while losing only one or two digits in the computed residual norms.},
	author = {Ichitaro Yamazaki and Dulceneia Becker and Jack Dongarra and Alex Druinsky and I. Peled and Sivan Toledo and Grey Ballard and James Demmel and Oded Schwartz}
}
@inbook {762,
	title = {Keeneland: Computational Science Using Heterogeneous GPU Computing},
	booktitle = {Contemporary High Performance Computing: From Petascale Toward Exascale},
	series = {CRC Computational Science Series},
	year = {2013},
	publisher = {Taylor and Francis},
	organization = {Taylor and Francis},
	chapter = {7},
	address = {Boca Raton, FL},
	abstract = {The Keeneland Project is a five year Track 2D grant awarded by the National Science Foundation (NSF) under solicitation NSF 08-573 in August 2009 for the development and deployment of an innovative high performance computing system. The Keeneland project is led by the Georgia Institute of Technology (Georgia Tech) in collaboration with the University of Tennessee at Knoxville, National Institute of Computational Sciences, and Oak Ridge National Laboratory.},
	author = {Jeffrey Vetter and Richard Glassbrook and Karsten Schwan and Sudha Yalamanchili and Mitch Horton and Ada Gavrilovska and Magda Slawinska and Jack Dongarra and Jeremy Meredith and Philip Roth and Kyle Spafford and Stanimire Tomov and John Wynkoop}
}
@inproceedings {757,
	title = {Leading Edge Hybrid Multi-GPU Algorithms for Generalized Eigenproblems in Electronic Structure Calculations},
	journal = {International Supercomputing Conference (ISC)},
	volume = {7905},
	year = {2013},
	month = {2013-06},
	pages = {67-80},
	publisher = {Springer Berlin Heidelberg},
	edition = {Lecture Notes in Computer Science},
	address = {Leipzig, Germany},
	abstract = {Today{\textquoteright}s high computational demands from engineering fields and complex hardware development make it necessary to develop and optimize new algorithms toward achieving high performance and good scalability on the next generation of computers. The enormous gap between the high-performance capabilities of GPUs and the slow interconnect between them has made the development of numerical software that is scalable across multiple GPUs extremely challenging. We describe and analyze a successful methodology to address the challenges{\textemdash}starting from our algorithm design, kernel optimization and tuning, to our programming model{\textemdash}in the development of a scalable high-performance generalized eigenvalue solver in the context of electronic structure calculations in materials science applications. We developed a set of leading edge dense linear algebra algorithms, as part of a generalized eigensolver, featuring fine grained memory aware kernels, a task based approach and hybrid execution/scheduling. The goal of the new design is to increase the computational intensity of the major compute kernels and to reduce synchronization and data transfers between GPUs. We report the performance impact on the generalized eigensolver when different fractions of eigenvectors are needed. The algorithm described provides an enormous performance boost compared to current GPU-based solutions, and performance comparable to state-of-the-art distributed solutions, using a single node with multiple GPUs.},
	isbn = {978-3-642-38750-0},
	doi = {10.1007/978-3-642-38750-0_6},
	author = {Azzam Haidar and Stanimire Tomov and Jack Dongarra and Raffaele Solc{\`a} and Thomas C. Schulthess}
}
@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}
}
@article {701,
	title = {Tridiagonalization of a dense symmetric matrix on multiple GPUs and its application to symmetric eigenvalue problems},
	journal = {Concurrency and Computation: Practice and Experience},
	year = {2013},
	month = {2013-10},
	abstract = {For software to fully exploit the computing power of emerging heterogeneous computers, not only must the required computational kernels be optimized for the specific hardware architectures but also an effective scheduling scheme is needed to utilize the available heterogeneous computational units and to hide the communication between them. As a case study, we develop a static scheduling scheme for the tridiagonalization of a symmetric dense matrix on multicore CPUs with multiple graphics processing units (GPUs) on a single compute node. We then parallelize and optimize the Basic Linear Algebra Subroutines (BLAS)-2 symmetric matrix-vector multiplication, and the BLAS-3 low rank symmetric matrix updates on the GPUs. We demonstrate the good scalability of these multi-GPU BLAS kernels and the effectiveness of our scheduling scheme on twelve Intel Xeon processors and three NVIDIA GPUs. We then integrate our hybrid CPU-GPU kernel into computational kernels at higher-levels of software stacks, that is, a shared-memory dense eigensolver and a distributed-memory sparse eigensolver. Our experimental results show that our kernels greatly improve the performance of these higher-level kernels, not only reducing the solution time but also enabling the solution of larger-scale problems. Because such symmetric eigenvalue problems arise in many scientific and engineering simulations, our kernels could potentially lead to new scientific discoveries. Furthermore, these dense linear algebra algorithms present algorithmic characteristics that can be found in other algorithms. Hence, they are not only important computational kernels on their own but also useful testbeds to study the performance of the emerging computers and the effects of the various optimization techniques.},
	author = {Ichitaro Yamazaki and Tingxing Dong and Raffaele Solc{\`a} and Stanimire Tomov and Jack Dongarra and Thomas C. Schulthess}
}
@inproceedings {icl:672,
	title = {Algorithm-Based Fault Tolerance for Dense Matrix Factorization},
	journal = {Proceedings of the 17th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP 2012},
	year = {2012},
	month = {2012-02},
	pages = {225-234},
	publisher = {ACM},
	address = {New Orleans, LA, USA},
	abstract = {Dense matrix factorizations, such as LU, Cholesky and QR, are widely used for scientific applications that require solving systems of linear equations, eigenvalues and linear least squares problems. Such computations are normally carried out on supercomputers, whose ever-growing scale induces a fast decline of the Mean Time To Failure (MTTF). This paper proposes a new hybrid approach, based on Algorithm-Based Fault Tolerance (ABFT), to help matrix factorizations algorithms survive fail-stop failures. We consider extreme conditions, such as the absence of any reliable component and the possibility of loosing both data and checksum from a single failure. We will present a generic solution for protecting the right factor, where the updates are applied, of all above mentioned factorizations. For the left factor, where the panel has been applied, we propose a scalable checkpointing algorithm. This algorithm features high degree of checkpointing parallelism and cooperatively utilizes the checksum storage leftover from the right factor protection. The fault-tolerant algorithms derived from this hybrid solution is applicable to a wide range of dense matrix factorizations, with minor modifications. Theoretical analysis shows that the fault tolerance overhead sharply decreases with the scaling in the number of computing units and the problem size. Experimental results of LU and QR factorization on the Kraken (Cray XT5) supercomputer validate the theoretical evaluation and confirm negligible overhead, with- and without-errors.},
	keywords = {ft-la, ftmpi},
	doi = {10.1145/2145816.2145845},
	author = {Peng Du and Aurelien Bouteiller and George Bosilca and Thomas Herault and Jack Dongarra},
	editor = {J. Ramanujam and P. Sadayappan}
}
@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}
}
@inproceedings {icl:669,
	title = {Enabling and Scaling Matrix Computations on Heterogeneous Multi-Core and Multi-GPU Systems},
	journal = {26th ACM International Conference on Supercomputing (ICS 2012)},
	year = {2012},
	month = {2012-06},
	publisher = {ACM},
	address = {San Servolo Island, Venice, Italy},
	keywords = {magma},
	author = {Fengguang Song and Stanimire Tomov and Jack Dongarra}
}
@article {icl:694,
	title = {High Performance Computing Systems: Status and Outlook},
	journal = {Acta Numerica},
	volume = {21},
	year = {2012},
	month = {2012-05},
	pages = {379-474},
	publisher = {Cambridge University Press},
	address = {Cambridge, UK},
	author = {Jack Dongarra and Aad J. van der Steen}
}
@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 {1355,
	title = {MAGMA: A Breakthrough in Solvers for Eigenvalue Problems},
	year = {2012},
	month = {2012-05},
	publisher = {GPU Technology Conference (GTC12), Presentation},
	address = {San Jose, CA},
	author = {Stanimire Tomov and Jack Dongarra and Azzam Haidar and Ichitaro Yamazaki and Tingxing Dong and Thomas Schulthess and Raffaele Solc{\`a}}
}
@article {icl:729,
	title = {A Novel Hybrid CPU-GPU Generalized Eigensolver for Electronic Structure Calculations Based on Fine Grained Memory Aware Tasks},
	journal = {Supercomputing {\textquoteright}12 (poster)},
	year = {2012},
	month = {2012-11},
	address = {Salt Lake City, Utah},
	author = {Raffaele Solc{\`a} and Azzam Haidar and Stanimire Tomov and Jack Dongarra and Thomas C. Schulthess}
}
@inproceedings {icl:681,
	title = {A Scalable Framework for Heterogeneous GPU-Based Clusters},
	journal = {The 24th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2012)},
	year = {2012},
	month = {2012-06},
	publisher = {ACM},
	address = {Pittsburgh, PA, USA},
	keywords = {magma},
	author = {Fengguang Song and Jack Dongarra}
}
@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}
}
@inproceedings {icl:607,
	title = {3-D parallel frequency-domain visco-acoustic wave modelling based on a hybrid direct/iterative solver},
	journal = {73rd EAGE Conference \& Exhibition incorporating SPE EUROPEC 2011, Vienna, Austria, 23-26 May},
	year = {2011},
	month = {2011-00},
	author = {Azzam Haidar and Luc Giraud and Hafedh Ben-Hadj-Ali and Florent Sourbier and St{\'e}phane Operto and Jean Virieux}
}
@techreport {icl:628,
	title = {Efficient Support for Matrix Computations on Heterogeneous Multi-core and Multi-GPU Architectures},
	journal = {University of Tennessee Computer Science Technical Report, UT-CS-11-668, (also Lawn 250)},
	year = {2011},
	month = {2011-06},
	keywords = {magma, plasma},
	author = {Fengguang Song and Stanimire Tomov 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 {,
	title = {Keeneland: Bringing Heterogeneous GPU Computing to the Computational Science Community},
	journal = {IEEE Computing in Science \& Engineering},
	volume = {13},
	year = {2011},
	month = {2011-08},
	pages = {90-95},
	abstract = {The Keeneland project{\textquoteright}s goal is to develop and deploy an innovative, GPU-based high-performance computing system for the NSF computational science community.},
	keywords = {Benchmark testing, Computational modeling, Computer architecture, Graphics processing unit, Hardware, Random access memory, Scientific computing},
	doi = {https://doi.org/10.1109/MCSE.2011.83},
	author = {Jeffrey Vetter and Richard Glassbrook and Jack Dongarra and Karsten Schwan and Bruce Loftis and Stephen McNally and Jeremy Meredith and James Rogers and Philip Roth and Kyle Spafford and Sudhakar Yalamanchili}
}
@inproceedings {icl:649,
	title = {Kernel Assisted Collective Intra-node MPI Communication Among Multi-core and Many-core CPUs},
	journal = {Int{\textquoteright}l Conference on Parallel Processing (ICPP {\textquoteright}11)},
	year = {2011},
	month = {2011-09},
	address = {Taipei, Taiwan},
	author = {Teng Ma and George Bosilca and Aurelien Bouteiller and Brice Goglin and J. Squyres and Jack Dongarra}
}
@conference {icl:633,
	title = {Parallel Performance Measurement of Heterogeneous Parallel Systems with GPUs},
	booktitle = {International Conference on Parallel Processing (ICPP{\textquoteright}11)},
	year = {2011},
	month = {2011-09},
	publisher = {ACM},
	organization = {ACM},
	address = {Taipei, Taiwan},
	abstract = {The power of GPUs is giving rise to heterogeneous parallel computing, with new demands on programming environments, runtime systems, and tools to deliver high-performing applications. This paper studies the problems associated with performance measurement of heterogeneous machines with GPUs. A heterogeneous computation model and alternative host-GPU measurement approaches are discussed to set the stage for reporting new capabilities for heterogeneous parallel performance measurement in three leading HPC tools: PAPI, Vampir, and the TAU Performance System. Our work leverages the new CUPTI tool support in NVIDIA{\textquoteright}s CUDA device library. Heterogeneous benchmarks from the SHOC suite are used to demonstrate the measurement methods and tool support.},
	keywords = {magma, mumi, papi},
	isbn = {978-0-7695-4510-3},
	doi = {10.1109/ICPP.2011.71},
	author = {Allen D. Malony and Scott Biersdorff and Sameer Shende and Heike Jagode and Stanimire Tomov and Guido Juckeland and Robert Dietrich and Duncan Poole and Christopher Lamb}
}
@article {icl:636,
	title = {Performance Portability of a GPU Enabled Factorization with the DAGuE Framework},
	journal = {IEEE Cluster: workshop on Parallel Programming on Accelerator Clusters (PPAC)},
	year = {2011},
	month = {2011-06},
	keywords = {dague, magma, parsec},
	author = {George Bosilca and Aurelien Bouteiller and Thomas Herault and Pierre Lemariner and Narapat Ohm Saengpatsa and Stanimire Tomov and Jack Dongarra}
}
@inproceedings {icl:619,
	title = {Power-Aware Prediction Models of Hybrid (MPI/OpenMP) Scientific Applications},
	journal = {International Conference on Energy-Aware High Performance Computing (EnA-HPC 2011)},
	year = {2011},
	month = {2011-09},
	address = {Hamburg, Germany},
	keywords = {mumi},
	author = {Charles Lively and Xingfu Wu and Valerie Taylor and Shirley Moore and Hung-Ching Chang and Chun-Yi Su and Kirk Cameron}
}
@article {icl:603,
	title = {Three-dimensional parallel frequency-domain visco-acoustic wave modelling based on a hybrid direct/iterative solver.},
	journal = {To appear in Geophysical Prospecting journal.},
	year = {2011},
	month = {2011-00},
	author = {Florent Sourbier and Azzam Haidar and Luc Giraud and Hafedh Ben-Hadj-Ali and St{\'e}phane Operto and Jean Virieux}
}
@inproceedings {icl:593,
	title = {A Unified HPC Environment for Hybrid Manycore/GPU Distributed Systems},
	journal = {IEEE International Parallel and Distributed Processing Symposium (submitted)},
	year = {2011},
	month = {2011-05},
	address = {Anchorage, AK},
	keywords = {dague},
	author = {George Bosilca and Aurelien Bouteiller and Thomas Herault and Pierre Lemariner and Narapat Ohm Saengpatsa and Stanimire Tomov and Jack Dongarra}
}
@techreport {icl:597,
	title = {Kernel Assisted Collective Intra-node Communication Among Multicore and Manycore CPUs},
	journal = {University of Tennessee Computer Science Technical Report, UT-CS-10-663},
	year = {2010},
	month = {2010-11},
	author = {Teng Ma and George Bosilca and Aurelien Bouteiller and Brice Goglin and J. Squyres and Jack Dongarra}
}
@article {icl:579,
	title = {Proceedings of the International Conference on Computational Science},
	journal = {ICCS 2010},
	year = {2010},
	month = {2010-05},
	publisher = {Elsevier},
	address = {Amsterdam},
	editor = {Peter M. Sloot and Geert Dick van Albada and Jack Dongarra}
}
@techreport {icl:530,
	title = {Scalable Tile Communication-Avoiding QR Factorization on Multicore Cluster Systems},
	journal = {University of Tennessee Computer Science Technical Report},
	volume = {{\textendash}10-653},
	year = {2010},
	month = {2010-04},
	keywords = {plasma},
	author = {Fengguang Song and Hatem Ltaeif and Bilel Hadri and Jack Dongarra}
}
@article {icl:559,
	title = {Scalable Tile Communication-Avoiding QR Factorization on Multicore Cluster Systems},
	journal = {SC{\textquoteright}10},
	year = {2010},
	month = {2010-11},
	publisher = {ACM SIGARCH/ IEEE Computer Society},
	address = {New Orleans, LA},
	keywords = {plasma},
	author = {Fengguang Song and Hatem Ltaeif and Bilel Hadri and Jack Dongarra}
}
@article {icl:566,
	title = {SmartGridRPC: The new RPC model for high performance Grid Computing and Its Implementation in SmartGridSolve},
	journal = {Concurrency and Computation: Practice and Experience (to appear)},
	year = {2010},
	month = {2010-01},
	keywords = {netsolve},
	author = {Thomas Brady and Alexey Lastovetsky and Keith Seymour and Michele Guidolin and Jack Dongarra}
}
@article {icl:545,
	title = {Sparse approximations of the Schur complement for parallel algebraic hybrid solvers in 3D},
	journal = {Numerical Mathematics: Theory, Methods and Applications},
	volume = {3},
	number = {3},
	year = {2010},
	month = {2010-00},
	pages = {64-82},
	publisher = {Golbal Science Press},
	address = {Beijing},
	author = {Luc Giraud and Azzam Haidar and Yousef Saad},
	editor = {C. Zhiming}
}
@article {icl:561,
	title = {Tuning Principal Component Analysis for GRASS GIS on Multi-core and GPU Architectures},
	journal = {FOSS4G 2010},
	year = {2010},
	month = {2010-09},
	address = {Barcelona, Spain},
	keywords = {magma},
	author = {Peng Du and Matthew Parsons and Erika Fuentes and Shih-Lung Shaw and Jack Dongarra}
}
@article {icl:544,
	title = {Using multiple levels of parallelism to enhance the performance of domain decomposition solvers},
	journal = {Parallel Computing},
	volume = {36},
	number = {5-6},
	year = {2010},
	month = {2010-00},
	pages = {285-296},
	publisher = {Elsevier journals},
	author = {Luc Giraud and Azzam Haidar and Stephane Pralet},
	editor = {Costas Bekas and Pascua D{\textquoteright}Ambra and Ananth Grama and Yousef Saad and Petko Yanev}
}
@article {icl:479,
	title = {Analytical Modeling and Optimization for Affinity Based Thread Scheduling on Multicore Systems},
	journal = {IEEE Cluster 2009},
	year = {2009},
	month = {2009-08},
	address = {New Orleans},
	keywords = {gridpac, mumi},
	author = {Fengguang Song and Shirley Moore and Jack Dongarra}
}
@article {icl:507,
	title = {Computational Science {\textendash} ICCS 2009, Proceedings of the 9th International Conference},
	journal = {Lecture Notes in Computer Science: Theoretical Computer Science and General Issues},
	volume = {-},
	number = {5544-5545},
	year = {2009},
	month = {2009-05},
	address = {Baton Rouge, LA},
	editor = {Gabrielle Allen and Jaros{\l}aw Nabrzyski and E. Seidel and Geert Dick van Albada and Jack Dongarra and Peter M. Sloot}
}
@inproceedings {icl:493,
	title = {Dynamic Task Scheduling for Linear Algebra Algorithms on Distributed-Memory Multicore Systems},
	journal = {International Conference for High Performance Computing, Networking, Storage, and Analysis (SC {\textquoteright}09)},
	year = {2009},
	month = {2009-11},
	address = {Portland, OR},
	keywords = {mumi, plasma},
	author = {Fengguang Song and Asim YarKhan and Jack Dongarra}
}
@inproceedings {icl:474,
	title = {A Holistic Approach for Performance Measurement and Analysis for Petascale Applications},
	journal = {ICCS 2009 Joint Workshop: Tools for Program Development and Analysis in Computational Science and Software Engineering for Large-Scale Computing},
	volume = {2009},
	year = {2009},
	month = {2009-05},
	pages = {686-695},
	publisher = {Springer-Verlag Berlin Heidelberg 2009},
	address = {Baton Rouge, Louisiana},
	keywords = {point, test},
	author = {Heike Jagode and Jack Dongarra and Sadaf Alam and Jeffrey Vetter and W. Spear and Allen D. Malony},
	editor = {Gabrielle Allen}
}
@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:481,
	title = {The International Exascale Software Project: A Call to Cooperative Action by the Global High Performance Community},
	journal = {International Journal of High Performance Computing Applications (to appear)},
	year = {2009},
	month = {2009-07},
	author = {Jack Dongarra and Pete Beckman and Patrick Aerts and Franck Cappello and Thomas Lippert and Satoshi Matsuoka and Paul Messina and Terry Moore and Rick Stevens and Anne Trefethen and Mateo Valero}
}
@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}
}
@inproceedings {icl:503,
	title = {MPI-aware Compiler Optimizations for Improving Communication-Computation Overlap},
	journal = {Proceedings of the 23rd annual International Conference on Supercomputing (ICS {\textquoteright}09)},
	year = {2009},
	month = {2009-06},
	pages = {316-325},
	publisher = {ACM},
	address = {Yorktown Heights, NY, USA},
	author = {Anthony Danalis and Lori Pollock and Martin Swany and John Cavazos}
}
@inproceedings {icl:512,
	title = {A Note on Auto-tuning GEMM for GPUs},
	journal = {9th International Conference on Computational Science (ICCS 2009)},
	number = {5544-5545},
	year = {2009},
	month = {2009-05},
	pages = {884-892},
	address = {Baton Rouge, LA},
	doi = {10.1007/978-3-642-01970-8_89},
	author = {Yinan Li and Jack Dongarra and Stanimire Tomov},
	editor = {Gabrielle Allen and Jaros{\l}aw Nabrzyski and E. Seidel and Geert Dick van Albada and Jack Dongarra and Peter M. Sloot}
}
@article {icl:500,
	title = {Paravirtualization Effect on Single- and Multi-threaded Memory-Intensive Linear Algebra Software},
	journal = {Cluster Computing Journal: Special Issue on High Performance Distributed Computing},
	volume = {12},
	number = {2},
	year = {2009},
	month = {2009-00},
	pages = {101-122},
	publisher = {Springer Netherlands},
	author = {Lamia Youseff and Keith Seymour and Haihang You and Dmitrii Zagorodnov and Jack Dongarra and Rich Wolski}
}
@article {icl:513,
	title = {Recent Trends in High Performance Computing},
	journal = {in Birth of Numerical Analysis (to appear)},
	year = {2009},
	month = {2009-00},
	author = {Jack Dongarra and Hans Meuer and Horst D. Simon and Erich Strohmaier}
}
@inproceedings {icl:501,
	title = {A Scalable Non-blocking Multicast Scheme for Distributed DAG Scheduling},
	journal = {The International Conference on Computational Science 2009 (ICCS 2009)},
	volume = {5544},
	year = {2009},
	month = {2009-05},
	pages = {195-204},
	address = {Baton Rouge, LA},
	keywords = {plasma},
	author = {Fengguang Song and Shirley Moore and Jack Dongarra}
}
@article {icl:515,
	title = {Transparent Cross-Platform Access to Software Services using GridSolve and GridRPC},
	journal = {in Cloud Computing and Software Services: Theory and Techniques (to appear)},
	year = {2009},
	month = {2009-00},
	publisher = {CRC Press},
	keywords = {netsolve},
	author = {Keith Seymour and Asim YarKhan and Jack Dongarra},
	editor = {Syed Ahson and Mohammad Ilyas}
}
@inproceedings {icl:453,
	journal = {8th International Conference on Computational Science (ICCS), Proceedings Parts I, II, and III, Lecture Notes in Computer Science},
	volume = {5101},
	year = {2008},
	month = {2008-01},
	publisher = {Springer Berlin},
	address = {Krakow, Poland},
	editor = {Marian Bubak and Geert Dick van Albada and Jack Dongarra and Peter M. Sloot}
}
@techreport {icl:432,
	title = {Analytical Modeling for Affinity-Based Thread Scheduling on Multicore Platforms},
	journal = {University of Tennessee Computer Science Technical Report, UT-CS-08-626},
	year = {2008},
	month = {2008-01},
	author = {Fengguang Song and Shirley Moore and Jack Dongarra}
}
@inproceedings {icl:418,
	title = {A Comparison of Search Heuristics for Empirical Code Optimization},
	journal = {The 3rd international Workshop on Automatic Performance Tuning},
	year = {2008},
	month = {2008-10},
	address = {Tsukuba, Japan},
	keywords = {gco},
	author = {Keith Seymour and Haihang You 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:409,
	title = {High Performance GridRPC Middleware},
	journal = {Recent developments in Grid Technology and Applications},
	year = {2008},
	month = {2008-00},
	publisher = {Nova Science Publishers},
	keywords = {netsolve},
	author = {Yves Caniou and Eddy Caron and Frederic Desprez and Hidemoto Nakada and Yoshio Tanaka and Keith Seymour},
	editor = {George A. Gravvanis and John P. Morrison and Hamid R. Arabnia and D. A. Power}
}
@inproceedings {icl:410,
	title = {The Impact of Paravirtualized Memory Hierarchy on Linear Algebra Computational Kernels and Software},
	journal = {ACM/IEEE International Symposium on High Performance Distributed Computing},
	year = {2008},
	month = {2008-06},
	address = {Boston, MA.},
	keywords = {gco, netsolve},
	author = {Lamia Youseff and Keith Seymour and Haihang You and Jack Dongarra and Rich Wolski}
}
@article {icl:408,
	title = {Interactive Grid-Access Using Gridsolve and Giggle},
	journal = {Computing and Informatics},
	volume = {27},
	number = {2},
	year = {2008},
	month = {2008-00},
	pages = {233-248,ISSN1335-9150},
	keywords = {netsolve},
	author = {Marcus Hardt and Keith Seymour and Jack Dongarra and Michael Zapf and Nicole Ruiter}
}
@article {icl:417,
	title = {Performance Instrumentation and Compiler Optimizations for MPI/OpenMP Applications},
	journal = {Lecture Notes in Computer Science, OpenMP Shared Memory Parallel Programming},
	volume = {4315},
	year = {2008},
	month = {2008-00},
	publisher = {Springer Berlin / Heidelberg},
	author = {Oscar Hernandez and Fengguang Song and Barbara Chapman and Jack Dongarra and Bernd Mohr and Shirley Moore and Felix Wolf}
}
@article {icl:462,
	title = {PERI Auto-tuning},
	journal = {Proc. SciDAC 2008},
	volume = {125},
	year = {2008},
	month = {2008-01},
	publisher = {Journal of Physics},
	address = {Seatlle, Washington},
	keywords = {gco},
	author = {David Bailey and Jacqueline Chame and Chun Chen and Jack Dongarra and Mary Hall and Jeffrey K. Hollingsworth and Paul D. Hovland and Shirley Moore and Keith Seymour and Jaewook Shin and Ananta Tiwari and Sam Williams and Haihang You}
}
@inproceedings {icl:419,
	title = {Request Sequencing: Enabling Workflow for Efficient Problem Solving in GridSolve},
	journal = {International Conference on Grid and Cooperative Computing (GCC 2008) (submitted)},
	year = {2008},
	month = {2008-10},
	address = {Shenzhen, China},
	author = {Yinan Li and Jack Dongarra and Keith Seymour and Asim YarKhan}
}
@article {icl:504,
	title = {Revisiting Matrix Product on Master-Worker Platforms},
	journal = {International Journal of Foundations of Computer Science (IJFCS)},
	volume = {19},
	number = {6},
	year = {2008},
	month = {2008-12},
	pages = {1317-1336},
	author = {Jack Dongarra and Jean-Francois Pineau and Yves Robert and Zhiao Shi and Frederic Vivien}
}
@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}
}
@techreport {icl:336,
	title = {Automated Empirical Tuning of a Multiresolution Analysis Kernel},
	journal = {ICL Technical Report},
	number = {ICL-UT-07-01},
	year = {2007},
	month = {2007-01},
	pages = {10},
	keywords = {gco},
	author = {Haihang You and Keith Seymour and Jack Dongarra and Shirley Moore}
}
@inproceedings {icl:372,
	title = {Bi-objective Scheduling Algorithms for Optimizing Makespan and Reliability on Heterogeneous Systems},
	journal = {19th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA) (submitted)},
	year = {2007},
	month = {2007-06},
	address = {San Diego, CA},
	author = {Jack Dongarra and Emmanuel Jeannot and Erik Saule and Zhiao Shi}
}
@techreport {icl:338,
	title = {Empirical Tuning of a Multiresolution Analysis Kernel using a Specialized Code Generator},
	journal = {ICL Technical Report},
	number = {ICL-UT-07-02},
	year = {2007},
	month = {2007-01},
	keywords = {gco},
	author = {Haihang You and Keith Seymour and Jack Dongarra and Shirley Moore}
}
@inproceedings {icl:367,
	title = {Feedback-Directed Thread Scheduling with Memory Considerations},
	journal = {IEEE International Symposium on High Performance Distributed Computing},
	year = {2007},
	month = {2007-06},
	address = {Monterey Bay, CA},
	author = {Fengguang Song and Shirley Moore and Jack Dongarra}
}
@inproceedings {icl:339,
	title = {GridSolve: The Evolution of Network Enabled Solver},
	journal = {Grid-Based Problem Solving Environments: IFIP TC2/WG 2.5 Working Conference on Grid-Based Problem Solving Environments (Prescott, AZ, July 2006)},
	year = {2007},
	month = {2007-00},
	pages = {215-226},
	publisher = {Springer},
	keywords = {netsolve},
	author = {Asim YarKhan and Jack Dongarra and Keith Seymour},
	editor = {Patrick Gaffney}
}
@article {icl:403,
	title = {Improved Runtime and Transfer Time Prediction Mechanisms in a Network Enabled Servers Middleware},
	journal = {Parallel Processing Letters},
	volume = {17},
	number = {1},
	year = {2007},
	month = {2007-03},
	pages = {47-59},
	author = {Emmanuel Jeannot and Keith Seymour and Asim YarKhan and Jack Dongarra}
}
@inproceedings {icl:386,
	title = {L2 Cache Modeling for Scientific Applications on Chip Multi-Processors},
	journal = {Proceedings of the 2007 International Conference on Parallel Processing},
	year = {2007},
	month = {2007-01},
	publisher = {IEEE Computer Society},
	address = {Xi{\textquoteright}an, China},
	author = {Fengguang Song and Shirley Moore and Jack Dongarra}
}
@inproceedings {icl:390,
	title = {Memory Leak Detection in Fortran Applications using TAU},
	journal = {Proc. DoD HPCMP Users Group Conference (HPCMP-UGC{\textquoteright}07)},
	year = {2007},
	month = {2007-01},
	publisher = {IEEE Computer Society},
	address = {Pittsburgh, PA},
	author = {Sameer Shende and Allen D. Malony and Shirley Moore and David Cronk}
}
@inbook {875,
	title = {A New Approach to MPI Collective Communication Implementations},
	booktitle = {Distributed and Parallel Systems},
	year = {2007},
	pages = {45-54},
	publisher = {Springer US},
	organization = {Springer US},
	abstract = {Recent research into the optimization of collective MPI operations has resulted in a wide variety of algorithms and corresponding implementations, each typically only applicable in a relatively narrow scope: on a specific architecture, on a specific network, with a specific number of processes, with a specific data size and/or data-type {\textendash} or any combination of these (or other) factors. This situation presents an enormous challenge to portable MPI implementations which are expected to provide optimized collective operation performance on all platforms. Many portable implementations have attempted to provide a token number of algorithms that are intended to realize good performance on most systems. However, many platform configurations are still left without well-tuned collective operations. This paper presents a proposal for a framework that will allow a wide variety of collective algorithm implementations and a flexible, multi-tiered selection process for choosing which implementation to use when an application invokes an MPI collective function.},
	keywords = {Automatic Selection, Collective Operation, Framework, Message Passing (MPI), Open MPI},
	isbn = {978-0-387-69857-1},
	doi = {10.1007/978-0-387-69858-8_5},
	author = {Torsten Hoefler and Jeffrey M. Squyres and Graham Fagg and George Bosilca and Wolfgang Rehm and Andrew Lumsdaine}
}
@inproceedings {icl:387,
	title = {Results of the PERI survey of SciDAC applications},
	journal = {Journal of Physics: Conference Series, SciDAC 2007},
	volume = {78},
	number = {2007},
	year = {2007},
	month = {2007-01},
	author = {Bronis R. de Supinski and Jeffrey K. Hollingsworth and Shirley Moore and Patrick H. Worley}
}
@article {icl:371,
	title = {Revisiting Matrix Product on Master-Worker Platforms},
	journal = {International Journal of Foundations of Computer Science (IJFCS) (accepted)},
	year = {2007},
	month = {2007-00},
	author = {Jack Dongarra and Jean-Francois Pineau and Yves Robert and Zhiao Shi and Frederic Vivien}
}
@inproceedings {icl:384,
	title = {Scalability Analysis of the SPEC OpenMP Benchmarks on Large-Scale Shared Memory Multiprocessors},
	journal = {Proceedings of the 2007 International Conference on Computational Science (ICCS 2007)},
	volume = {4487-4490},
	year = {2007},
	pages = {815-822},
	publisher = {Springer LNCS},
	address = {Beijing, China},
	keywords = {kojak},
	doi = {10.1007/978-3-540-72586-2_115},
	author = {Karl F{\"u}rlinger and Michael Gerndt and Jack Dongarra},
	editor = {Yong Shi and Jack Dongarra and Geert Dick van Albada and Peter M. Sloot}
}
@article {,
	title = {An Asynchronous Algorithm on NetSolve Global Computing System},
	journal = {Future Generation Computer Systems},
	volume = {22},
	year = {2006},
	month = {2006-02},
	pages = {279-290},
	abstract = {The explicitly restarted Arnoldi method (ERAM) allows one to find a few eigenpairs of a large sparse matrix. The multiple explicitly restarted Arnoldi method (MERAM) is a technique based upon a multiple projection of ERAM and accelerates its convergence [N. Emamad, S. Petiton, G. Edjlali, Multiple explicitly restarted Arnoldi method for solving large eigenproblems, SIAM J. Sci. Comput. SJSC 27 (1) (2005) 253-277]. MERAM allows one to update the restarting vector of an ERAM by taking into account the interesting eigen-information obtained by its other ERAM processes. This method is particularly well suited to the GRID-type environments. We present an adaptation of the asynchronous version of MERAM for the NetSolve global computing system. We point out some advantages and limitations of this kind of system to implement the asynchronous hybrid algorithms. We give some results of our experiments and show that we can obtain a good acceleration of the convergence compared to ERAM. These results also show the potential of the MERAM-like hybrid methods for the GRID computing environments.},
	keywords = {Arnoldi method, Explicit restarting, Global computing, Large eigenproblem, netsolve},
	doi = {https://doi.org/10.1016/j.future.2005.10.003},
	author = {Jack Dongarra and Nahid Emad and Seyed Abolfazl Shahzadeh-Fazeli}
}
@techreport {icl:313,
	title = {ATLAS on the BlueGene/L {\textendash} Preliminary Results},
	journal = {ICL Technical Report},
	number = {ICL-UT-06-10},
	year = {2006},
	month = {2006-01},
	keywords = {gco},
	author = {Keith Seymour and Haihang You and Jack Dongarra}
}
@inproceedings {icl:329,
	title = {Experiments with Strassen{\textquoteright}s Algorithm: From Sequential to Parallel},
	journal = {18th IASTED International Conference on Parallel and Distributed Computing and Systems PDCS 2006 (submitted)},
	year = {2006},
	month = {2006-01},
	address = {Dallas, Texas},
	author = {Fengguang Song and Jack Dongarra and Shirley Moore}
}
@article {icl:322,
	title = {FT-MPI, Fault-Tolerant Metacomputing and Generic Name Services: A Case Study},
	journal = {Lecture Notes in Computer Science},
	volume = {4192},
	number = {ICL-UT-06-14},
	year = {2006},
	month = {2006-00},
	pages = {133-140},
	publisher = {Springer Berlin / Heidelberg},
	keywords = {ftmpi},
	author = {David Dewolfs and Jan Broeckhove and Vaidy Sunderam and Graham Fagg}
}
@article {icl:651,
	title = {High Performance RDMA Protocols in HPC},
	journal = {Euro PVM/MPI 2006},
	year = {2006},
	month = {2006-09},
	address = {Bonn, Germany},
	author = {Galen M. Shipman and George Bosilca and Maccabe, Arthur B.}
}
@article {icl:652,
	title = {A High-Performance, Heterogeneous MPI},
	journal = {HeteroPar 2006},
	year = {2006},
	month = {2006-09},
	address = {Barcelona, Spain},
	author = {Richard L. Graham and Galen M. Shipman and Brian Barrett and Ralph Castain and George Bosilca and Andrew Lumsdaine}
}
@article {icl:340,
	title = {Improved Runtime and Transfer Time Prediction Mechanisms in a Network Enabled Server},
	journal = {Parallel Processing Letters},
	volume = {17},
	number = {1},
	year = {2006},
	month = {2006-03},
	pages = {47-59},
	keywords = {netsolve},
	author = {Emmanuel Jeannot and Keith Seymour and Asim YarKhan and Jack Dongarra}
}
@techreport {icl:334,
	title = {Modeling of L2 Cache Behavior for Thread-Parallel Scientific Programs on Chip Multi-Processors},
	journal = {University of Tennessee Computer Science Technical Report},
	number = {UT-CS-06-583},
	year = {2006},
	month = {2006-01},
	author = {Fengguang Song and Shirley Moore and Jack Dongarra}
}
@inproceedings {icl:319,
	title = {Performance Instrumentation and Compiler Optimizations for MPI/OpenMP Applications},
	journal = {Second International Workshop on OpenMP},
	year = {2006},
	month = {2006-01},
	address = {Reims, France},
	keywords = {kojak},
	author = {Oscar Hernandez and Fengguang Song and Barbara Chapman and Jack Dongarra and Bernd Mohr and Shirley Moore and Felix Wolf}
}
@article {icl:294,
	title = {Recent Developments in GridSolve},
	journal = {International Journal of High Performance Computing Applications (Special Issue: Scheduling for Large-Scale Heterogeneous Platforms)},
	volume = {20},
	number = {1},
	year = {2006},
	month = {2006-00},
	publisher = {Sage Science Press},
	keywords = {netsolve},
	author = {Asim YarKhan and Keith Seymour and Kiran Sagi and Zhiao Shi and Jack Dongarra},
	editor = {Yves Robert}
}
@article {icl:332,
	title = {Self Adapting Numerical Software SANS Effort},
	journal = {IBM Journal of Research and Development},
	volume = {50},
	number = {2/3},
	year = {2006},
	month = {2006-01},
	pages = {223-238},
	keywords = {gco},
	author = {George Bosilca and Zizhong Chen and Jack Dongarra and Victor Eijkhout and Graham Fagg and Erika Fuentes and Julien Langou and Piotr Luszczek and Jelena Pjesivac{\textendash}Grbovic and Keith Seymour and Haihang You and Sathish Vadhiyar}
}
@inproceedings {icl:288,
	title = {Automatic Experimental Analysis of Communication Patterns in Virtual Topologies},
	journal = {In Proceedings of the International Conference on Parallel Processing},
	year = {2005},
	month = {2005-06},
	publisher = {IEEE Computer Society},
	address = {Oslo, Norway},
	keywords = {kojak},
	author = {Nikhil Bhatia and Fengguang Song and Felix Wolf and Jack Dongarra and Bernd Mohr and Shirley Moore}
}
@inproceedings {icl:284,
	title = {Comparison of Nonlinear Conjugate-Gradient methods for computing the Electronic Properties of Nanostructure Architectures},
	journal = {Proceedings of 5th International Conference on Computational Science (ICCS)},
	year = {2005},
	month = {2005-01},
	pages = {317-325},
	publisher = {Springer{\textquoteright}s Lecture Notes in Computer Science},
	address = {Atlanta, GA, USA},
	keywords = {doe-nano},
	author = {Stanimire Tomov and Julien Langou and Andrew Canning and Lin-Wang Wang and Jack Dongarra},
	editor = {V. S. Sunderman and Geert Dick van Albada and Peter M. Sloot and Jack Dongarra}
}
@techreport {icl:255,
	title = {An Effective Empirical Search Method for Automatic Software Tuning},
	journal = {ICL Technical Report},
	number = {ICL-UT-05-02},
	year = {2005},
	month = {2005-01},
	keywords = {gco},
	author = {Haihang You and Keith Seymour and Jack Dongarra}
}
@article {icl:276,
	title = {NetSolve: Grid Enabling Scientific Computing Environments},
	journal = {Grid Computing and New Frontiers of High Performance Processing},
	number = {14},
	year = {2005},
	month = {2005-00},
	publisher = {Elsevier},
	keywords = {netsolve},
	author = {Keith Seymour and Asim YarKhan and Sudesh Agrawal and Jack Dongarra},
	editor = {Lucio Grandinetti}
}
@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}
}
@article {icl:305,
	title = {Optimization Problem Solving System Using GridRPC},
	journal = {IEEE Transactions on Parallel and Distributed Systems (submitted)},
	year = {2005},
	month = {2005-01},
	author = {Hisashi Shimosaka and Tomoyuki Hiroyasu and Mitsunori Miki and Jack Dongarra}
}
@article {icl:286,
	title = {On the Parallel Solution of Large Industrial Wave Propagation Problems},
	journal = {Journal of Computational Acoustics (to appear)},
	year = {2005},
	month = {2005-01},
	author = {Luc Giraud and Julien Langou and G. Sylvand}
}
@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:269,
	title = {Performance Profiling Overhead Compensation for MPI Programs},
	journal = {In Proc. of the 12th European Parallel Virtual Machine and Message Passing Interface Conference},
	year = {2005},
	month = {2005-09},
	publisher = {Springer LNCS},
	keywords = {kojak},
	author = {Sameer Shende and Allen D. Malony and Alan Morris and Felix Wolf}
}
@inproceedings {icl:270,
	title = {A Scalable Approach to MPI Application Performance Analysis},
	journal = {In Proc. of the 12th European Parallel Virtual Machine and Message Passing Interface Conference},
	year = {2005},
	month = {2005-09},
	publisher = {Springer LNCS},
	keywords = {kojak},
	author = {Shirley Moore and Felix Wolf and Jack Dongarra and Sameer Shende and Allen D. Malony and Bernd Mohr}
}
@inproceedings {icl:268,
	title = {Trace-Based Parallel Performance Overhead Compensation},
	journal = {In Proc. of the International Conference on High Performance Computing and Communications (HPCC)},
	year = {2005},
	month = {2005-09},
	address = {Sorrento (Naples), Italy},
	keywords = {kojak},
	author = {Felix Wolf and Allen D. Malony and Sameer Shende and Alan Morris}
}
@conference {icl:197,
	title = {Accurate Cache and TLB Characterization Using Hardware Counters},
	booktitle = {International Conference on Computational Science (ICCS 2004)},
	year = {2004},
	month = {2004-06},
	publisher = {Springer},
	organization = {Springer},
	address = {Krakow, Poland},
	abstract = {We have developed a set of microbenchmarks for accurately determining the structural characteristics of data cache memories and TLBs. These characteristics include cache size, cache line size, cache associativity, memory page size, number of data TLB entries, and data TLB associativity. Unlike previous microbenchmarks that used time-based measurements, our microbenchmarks use hardware event counts to more accurately and quickly determine these characteristics while requiring fewer limiting assumptions.},
	keywords = {gco, lacsi, papi},
	doi = {https://doi.org/10.1007/978-3-540-24688-6_57},
	author = {Jack Dongarra and Shirley Moore and Phil Mucci and Keith Seymour and Haihang You}
}
@inproceedings {icl:233,
	title = {An Algebra for Cross-Experiment Performance Analysis},
	journal = {2004 International Conference on Parallel Processing (ICCP-04)},
	year = {2004},
	month = {2004-08},
	address = {Montreal, Quebec, Canada},
	keywords = {kojak},
	author = {Fengguang Song and Felix Wolf and Nikhil Bhatia and Jack Dongarra and Shirley Moore}
}
@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}
}
@techreport {icl:196,
	title = {CUBE User Manual},
	journal = {ICL Technical Report},
	number = {ICL-UT-04-01},
	year = {2004},
	month = {2004-02},
	keywords = {kojak},
	author = {Fengguang Song and Felix Wolf}
}
@inproceedings {icl:201,
	title = {Design of an Interactive Environment for Numerically Intensive Parallel Linear Algebra Calculations},
	journal = {International Conference on Computational Science},
	year = {2004},
	month = {2004-06},
	publisher = {Springer Verlag},
	address = {Poland},
	keywords = {lacsi, lfc},
	doi = {10.1007/978-3-540-25944-2_35},
	author = {Piotr Luszczek and Jack Dongarra},
	editor = {Marian Bubak and Geert Dick van Albada and Peter M. Sloot and Jack Dongarra}
}
@article {icl:202,
	title = {The Virtual Instrument: Support for Grid-enabled Scientific Simulations},
	journal = {International Journal of High Performance Computing Applications},
	volume = {18},
	number = {1},
	year = {2004},
	month = {2004-01},
	pages = {3-17},
	author = {Henri Casanova and Thomas Bartol and Francine Berman and Adam Birnbaum and Jack Dongarra and Mark Ellisman and Marcio Faerman and Erhan Gockay and Michelle Miller and Graziano Obertelli and Stuart Pomerantz and Terry Sejnowski and Joel Stiles and Rich Wolski}
}
@article {icl:156,
	title = {Automatic Translation of Fortran to JVM Bytecode},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {15},
	number = {3-5},
	year = {2003},
	month = {2003-00},
	pages = {202-207},
	keywords = {f2j},
	author = {Keith Seymour and Jack Dongarra}
}
@article {icl:169,
	title = {Computational Science {\textemdash} ICCS 2003},
	journal = {Lecture Notes in Computer Science},
	volume = {2657-2660},
	year = {2003},
	month = {2003-06},
	publisher = {Springer-Verlag, Berlin},
	address = {ICCS 2003, International Conference. Melbourne, Australia},
	issn = {978-3-540-40194-0},
	author = {Peter M. Sloot and David Abramson and Alexander V. Bogdanov and Jack Dongarra and Albert Zomaya and Yuriy Gorbachev}
}
@article {icl:175,
	title = {Distributed Probablistic Model-Building Genetic Algorithm},
	journal = {Lecture Notes in Computer Science},
	volume = {2723},
	year = {2003},
	month = {2003-01},
	pages = {1015-1028},
	publisher = {Springer-Verlag, Heidelberg},
	author = {Tomoyuki Hiroyasu and Mitsunori Miki and Masaki Sano and Hisashi Shimosaka and Shigeyoshi Tsutsui and Jack Dongarra}
}
@article {icl:143,
	title = {NetSolve: Past, Present, and Future - A Look at a Grid Enabled Server},
	journal = {Making the Global Infrastructure a Reality},
	year = {2003},
	month = {2003-00},
	publisher = {Wiley Publishing},
	keywords = {netsolve},
	author = {Sudesh Agrawal and Jack Dongarra and Keith Seymour and Sathish Vadhiyar},
	editor = {Francine Berman and Geoffrey Fox and Anthony Hey}
}
@inproceedings {icl:179,
	title = {Optimization of Injection Schedule of Diesel Engine Using GridRPC},
	journal = {Information Processing Society of Japan Symposium Series},
	volume = {2003},
	number = {14},
	year = {2003},
	month = {2003-01},
	pages = {189-197},
	author = {Tomoyuki Hiroyasu and Mitsunori Miki and Junji Sawada and Jack Dongarra}
}
@inproceedings {icl:128,
	title = {Optimization Problem Solving System using Grid RPC},
	journal = {3rd IEEE/ACM International Symposium on Cluster Computing and the Grid},
	year = {2003},
	month = {2003-03},
	address = {Tokyo, Japan},
	author = {Tomoyuki Hiroyasu and Mitsunori Miki and Hisashi Shimosaka and Jack Dongarra}
}
@conference {icl:159,
	title = {Performance Instrumentation and Measurement for Terascale Systems},
	booktitle = {ICCS 2003 Terascale Workshop},
	year = {2003},
	month = {2003-06},
	publisher = {Springer, Berlin, Heidelberg},
	organization = {Springer, Berlin, Heidelberg},
	address = {Melbourne, Australia},
	abstract = {As computer systems grow in size and complexity, tool support is needed to facilitate the efficient mapping of large-scale applications onto these systems. To help achieve this mapping, performance analysis tools must provide robust performance observation capabilities at all levels of the system, as well as map low-level behavior to high-level program constructs. Instrumentation and measurement strategies, developed over the last several years, must evolve together with performance analysis infrastructure to address the challenges of new scalable parallel systems.},
	keywords = {papi},
	doi = {https://doi.org/10.1007/3-540-44864-0_6},
	author = {Jack Dongarra and Allen D. Malony and Shirley Moore and Phil Mucci and Sameer Shende}
}
@article {icl:138,
	title = {Scheduling in the Grid Application Development Software Project},
	journal = {Resource Management in the Grid},
	year = {2003},
	month = {2003-03},
	publisher = {Kluwer Publishers},
	keywords = {grads},
	author = {Holly Dail and Otto Sievert and Francine Berman and Henri Casanova and Asim YarKhan and Sathish Vadhiyar and Jack Dongarra and Chuang Liu and Lingyun Yang and Dave Angulo and Ian Foster}
}
@inproceedings {icl:177,
	title = {Static Scheduling for ScaLAPACK on the Grid Using Genetic Algorithm},
	journal = {Information Processing Society of Japan Symposium Series},
	volume = {2003},
	number = {14},
	year = {2003},
	month = {2003-01},
	pages = {3-10},
	author = {Tomoyuki Hiroyasu and Mitsunori Miki and Hiroki Saito and Yusuke Tanimura and Jack Dongarra}
}
@article {icl:109,
	title = {Biannual Top-500 Computer Lists Track Changing Environments for Scientific Computing},
	journal = {SIAM News},
	volume = {34},
	number = {9},
	year = {2002},
	month = {2002-10},
	keywords = {top500},
	author = {Jack Dongarra and Hans Meuer and Horst D. Simon and Erich Strohmaier}
}
@techreport {icl:97,
	title = {GridRPC: A Remote Procedure Call API for Grid Computing},
	journal = {ICL Technical Report},
	number = {ICL-UT-02-06},
	year = {2002},
	month = {2002-11},
	author = {Keith Seymour and Hidemoto Nakada and Satoshi Matsuoka and Jack Dongarra and Craig Lee and Henri Casanova}
}
@inproceedings {icl:122,
	title = {The Internet BackPlane Protocol: A Study in Resource Sharing},
	journal = {Proceedings of the second IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID 2002)},
	year = {2002},
	month = {2002-10},
	address = {Berlin, Germany},
	keywords = {ftmpi},
	author = {Alessandro Bassi and Micah Beck and Graham Fagg and Terry Moore and James Plank and Martin Swany and Rich Wolski}
}
@article {icl:116,
	title = {JLAPACK - Compiling LAPACK Fortran to Java},
	journal = {Scientific Programming},
	volume = {7},
	number = {2},
	year = {2002},
	month = {2002-10},
	pages = {111-138},
	keywords = {f2j},
	author = {David Doolin and Jack Dongarra and Keith Seymour}
}
@article {icl:115,
	title = {The Marketplace for High-Performance Computers},
	journal = {Parallel Computing},
	volume = {25},
	number = {13-14},
	year = {2002},
	month = {2002-10},
	pages = {1517-1545},
	author = {Erich Strohmaier and Jack Dongarra and Hans Meuer and Horst D. Simon}
}
@article {icl:101,
	title = {Middleware for the Use of Storage in Communication},
	journal = {Parallel Computing},
	volume = {28},
	number = {12},
	year = {2002},
	month = {2002-08},
	pages = {1773-1788},
	keywords = {netsolve},
	author = {Micah Beck and Dorian Arnold and Alessandro Bassi and Francine Berman and Henri Casanova and Jack Dongarra and Terry Moore and Graziano Obertelli and James Plank and Martin Swany and Sathish Vadhiyar and Rich Wolski}
}
@article {icl:104,
	title = {Optimization System Using Grid RPC},
	journal = {Meeting of the Japan Society of Mechanical Engineers},
	year = {2002},
	month = {2002-10},
	address = {Kyoto University, Kyoto, Japan},
	author = {Tomoyuki Hiroyasu and Mitsunori Miki and Hisashi Shimosaka and Yusuke Tanimura and Jack Dongarra}
}
@inproceedings {icl:187,
	title = {Overview of GridRPC: A Remote Procedure Call API for Grid Computing},
	journal = {Proceedings of the Third International Workshop on Grid Computing},
	year = {2002},
	month = {2002-01},
	pages = {274-278},
	author = {Keith Seymour and Hidemoto Nakada and Satoshi Matsuoka and Jack Dongarra and Craig Lee and Henri Casanova},
	editor = {Manish Parashar}
}
@article {icl:103,
	title = {Truss Structural Optimization Using NetSolve System},
	journal = {Meeting of the Japan Society of Mechanical Engineers},
	year = {2002},
	month = {2002-10},
	address = {Kyoto University, Kyoto, Japan},
	keywords = {netsolve},
	author = {Tomoyuki Hiroyasu and Mitsunori Miki and Hisashi Shimosaka and Masaki Sano and Yusuke Tanimura and Yasunari Mimura and Shinobu Yoshimura and Jack Dongarra}
}
@techreport {icl:96,
	title = {Users{\textquoteright} Guide to NetSolve v1.4.1},
	journal = {ICL Technical Report},
	number = {ICL-UT-02-05},
	year = {2002},
	month = {2002-06},
	keywords = {netsolve},
	author = {Sudesh Agrawal and Dorian Arnold and Susan Blackford and Jack Dongarra and Michelle Miller and Kiran Sagi and Zhiao Shi and Keith Seymour and Sathish Vadhiyar}
}
@article {icl:95,
	title = {The Virtual Instrument: Support for Grid-enabled Scientific Simulations},
	journal = {Journal of Parallel and Distributed Computing (submitted)},
	year = {2002},
	month = {2002-10},
	author = {Henri Casanova and Thomas Bartol and Francine Berman and Adam Birnbaum and Jack Dongarra and Mark Ellisman and Marcio Faerman and Erhan Gockay and Michelle Miller and Graziano Obertelli and Stuart Pomerantz and Terry Sejnowski and Joel Stiles and Rich Wolski}
}
@inproceedings {icl:22,
	title = {Automatic Translation of Fortran to JVM Bytecode},
	journal = {Joint ACM Java Grande - ISCOPE 2001 Conference (submitted)},
	year = {2001},
	month = {2001-06},
	address = {Stanford University, California},
	keywords = {f2j},
	author = {Keith Seymour and Jack Dongarra}
}
@conference {icl:15,
	title = {End-user Tools for Application Performance Analysis, Using Hardware Counters},
	booktitle = {International Conference on Parallel and Distributed Computing Systems},
	year = {2001},
	month = {2001-08},
	address = {Dallas, TX},
	abstract = {One purpose of the end-user tools described in this paper is to give users a graphical representation of performance information that has been gathered by instrumenting an application with the PAPI library. PAPI is a project that specifies a standard API for accessing hardware performance counters available on most modern microprocessors. These counters exist as a small set of registers that count \&quot;events\&quot;, which are occurrences of specific signals and states related to a processor{\textquoteright}s function. Monitoring these events facilitates correlation between the structure of source/object code and the efficiency of the mapping of that code to the underlying architecture. The perfometer tool developed by the PAPI project provides a graphical view of this information, allowing users to quickly see where performance bottlenecks are in their application. Only one function call has to be added by the user to their program to take advantage of perfometer. This makes it quick and simple to add and remove instrumentation from a program. Also, perfometer allows users to change the \&quot;event\&quot; they are monitoring. Add the ability to monitor parallel applications, set alarms and a Java front-end that can run anywhere, and this gives the user a powerful tool for quickly discovering where and why a bottleneck exists. A number of third-party tools for analyzing performance of message-passing and/or threaded programs have also incorporated support for PAPI so as to be able to display and analyze hardware counter data from their interfaces.},
	keywords = {papi},
	author = {Kevin London and Jack Dongarra and Shirley Moore and Phil Mucci and Keith Seymour and T. Spencer}
}
@article {icl:222,
	title = {High Performance Computing Trends},
	journal = {HERMIS},
	volume = {2},
	year = {2001},
	month = {2001-11},
	pages = {155-163},
	author = {Jack Dongarra and Hans Meuer and Horst D. Simon and Erich Strohmaier}
}
@article {icl:4,
	title = {Logistical Computing and Internetworking: Middleware for the Use of Storage in Communication},
	journal = {submitted to SC2001},
	year = {2001},
	month = {2001-11},
	address = {Denver, Colorado},
	keywords = {netsolve},
	author = {Micah Beck and Dorian Arnold and Alessandro Bassi and Francine Berman and Henri Casanova and Jack Dongarra and Terry Moore and Graziano Obertelli and James Plank and Martin Swany and Sathish Vadhiyar and Rich Wolski}
}
@article {icl:23,
	title = {Overview of High Performance Computers},
	journal = {Handbook of Massive Data Sets},
	year = {2001},
	month = {2001-01},
	pages = {791-852},
	publisher = {Kluwer Academic Publishers},
	author = {Aad J. van der Steen and Jack Dongarra},
	editor = {James Abello and Panos Pardalos and Mauricio Resende}
}
@conference {icl:16,
	title = {The PAPI Cross-Platform Interface to Hardware Performance Counters},
	booktitle = {Department of Defense Users{\textquoteright} Group Conference Proceedings},
	year = {2001},
	month = {2001-06},
	address = {Biloxi, Mississippi},
	abstract = {The purpose of the PAPI project is to specify a standard API for accessing hardware performance counters available on most modern microprocessors. These counters exist as a small set of registers that count \&quot;events,\&quot; which are occurrences of specific signals and states related to the processor{\textquoteright}s function. Monitoring these events facilitates correlation between the structure of source/object code and the efficiency of the mapping of that code to the underlying architecture. This correlation has a variety of uses in performance analysis and tuning. The PAPI project has developed a standard set of hardware events and a standard cross-platform library interface to the underlying counter hardware. The PAPI library has been implemented for a number of Shared Resource Center platforms. The PAPI project is developing end-user tools for dynamically selecting and displaying hardware counter performance data. PAPI support is also being incorporated into a number of third-party tools.},
	keywords = {papi},
	author = {Kevin London and Shirley Moore and Phil Mucci and Keith Seymour and Richard Luczak}
}
@inproceedings {icl:41,
	title = {High Performance Computing Today},
	journal = {FOMMS 2000: Foundations of Molecular Modeling and Simulation Conference (to appear)},
	year = {2000},
	month = {2000-01},
	author = {Jack Dongarra and Hans Meuer and Horst D. Simon and Erich Strohmaier}
}
@article {icl:29,
	title = {Logistical Networking: Sharing More Than the Wires},
	journal = {In Active Middleware Services, Ed. Salim Hariri, Craig A. Lee, Cauligi S. Raghavendra (2000), Kluwer Academic},
	year = {2000},
	month = {2000-01},
	address = {Norwell, MA},
	author = {Micah Beck and Terry Moore and James Plank and Martin Swany}
}
@techreport {icl:40,
	title = {Top500 Supercomputer Sites (15th edition)},
	journal = {University of Tennessee Computer Science Department Technical Report},
	number = {UT-CS-00-442},
	year = {2000},
	month = {2000-06},
	keywords = {top500},
	author = {Jack Dongarra and Hans Meuer and Erich Strohmaier}
}
@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:50,
	title = {LAPACK Users{\textquoteright} Guide, 3rd ed.},
	journal = {Philadelphia: Society for Industrial and Applied Mathematics},
	year = {1999},
	month = {1999-01},
	author = {Ed Anderson and Zhaojun Bai and Christian Bischof and Susan Blackford and James Demmel and Jack Dongarra and Jeremy Du Croz and Anne Greenbaum and Sven Hammarling and Alan McKenney and Danny Sorensen}
}
@techreport {icl:67,
	title = {Top500 Supercomputer Sites (13th edition)},
	journal = {University of Tennessee Computer Science Department Technical Report},
	number = {UT-CS-99-425},
	year = {1999},
	month = {1999-06},
	keywords = {top500},
	author = {Jack Dongarra and Hans Meuer and Erich Strohmaier}
}
@techreport {icl:68,
	title = {Top500 Supercomputer Sites (14th edition)},
	journal = {University of Tennessee Computer Science Department Technical Report},
	number = {UT-CS-99-434},
	year = {1999},
	month = {1999-11},
	keywords = {top500},
	author = {Jack Dongarra and Hans Meuer and Erich Strohmaier}
}
@book {1469,
	title = {MPI - The Complete Reference, Volume 1: The MPI Core},
	year = {1998},
	month = {1998-08},
	pages = {426},
	publisher = {MIT Press},
	organization = {MIT Press},
	edition = {Second},
	address = {Cambridge, MA, USA},
	abstract = {Since its release in summer 1994, the Message Passing Interface (MPI) specification has become a standard for message-passing libraries for parallel computations. There exist more than a dozen implementations on a variety of computing platforms, from the IBM SP-2 supercomputer to PCs running Windows NT. The initial MPI Standard, known as MPI-1, has been modified over the last two years. This volume, the definitive reference manual for the latest version of MPI-1, contains a complete specification of the MPI Standard. It is annotated with comments that clarify complicated issues, including why certain design choices were made, how users are intended to use the interface, and how they should construct their version of MPI. The volume also provides many detailed, illustrative programming examples.},
	isbn = {978-0-262-69215-1},
	author = {Marc Snir and Steve Otto and Steven Huss-Lederman and David Walker and Jack Dongarra}
}
@book {1468,
	title = {Numerical Linear Algebra for High-Performance Computers},
	series = {Software, Environments and Tools},
	year = {1998},
	publisher = {SIAM},
	organization = {SIAM},
	abstract = {This book presents a unified treatment of recently developed techniques and current understanding about solving systems of linear equations and large scale eigenvalue problems on high-performance computers. It provides a rapid introduction to the world of vector and parallel processing for these linear algebra applications.

Topics include major elements of advanced-architecture computers and their performance, recent algorithmic development, and software for direct solution of dense matrix problems, direct solution of sparse systems of equations, iterative solution of sparse systems of equations, and solution of large sparse eigenvalue problems.

This book supersedes the SIAM publication Solving Linear Systems on Vector and Shared Memory Computers, which appeared in 1990. The new book includes a considerable amount of new material in addition to incorporating a substantial revision of existing text.},
	doi = {https://doi.org/10.1137/1.9780898719611},
	author = {Jack Dongarra and Iain Duff and Danny Sorensen and Henk van der Vorst}
}
@article {1467,
	title = {ScaLAPACK: A Portable Linear Algebra Library for Distributed Memory Computers - Design Issues and Performance},
	journal = {Computer Physics Communications},
	volume = {97},
	year = {1996},
	month = {1996-08},
	pages = {1-15},
	abstract = {This paper outlines the content and performance of ScaLAPACK, a collection of mathematical software for linear algebra computations on distributed memory computers. The importance of developing standards for computational and message passing interfaces is discussed. We present the different components and building blocks of ScaLAPACK. This paper outlines the difficulties inherent in producing correct codes for networks of heterogeneous processors. We define a theoretical model of parallel computers dedicated to linear algebra applications: the Distributed Linear Algebra Machine (DLAM). This model provides a convenient framework for developing parallel algorithms and investigating their scalability, performance and programmability. Extensive performance results on various platforms are presented and analyzed with the help of the DLAM. Finally, this paper briefly describes future directions for the ScaLAPACK library and concludes by suggesting alternative approaches to mathematical libraries, explaining how ScaLAPACK could be integrated into efficient and user-friendly distributed systems.},
	doi = {https://doi.org/10.1016/0010-4655(96)00017-3},
	author = {Jaeyoung Choi and Jim Demmel and Inderjit Dhillon and Jack Dongarra and Susan Ostrouchov and Antoine Petitet and Kendall Stanley and David Walker and Clint Whaley}
}
