@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}
}
@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 = {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 = {Extending MAGMA Portability with OneAPI},
	year = {2022},
	month = {2022-11},
	publisher = {The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC22), ACM Student Research Competition},
	address = {Dallas, TX},
	url = {https://sc22.supercomputing.org/proceedings/src_poster/poster_files/spostu105s3-file1.pdf},
	author = {Anna Fortenberry and Stanimire Tomov and Kwai Wong}
}
@conference {,
	title = {Extending MAGMA Portability with OneAPI},
	booktitle = {The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC22), Ninth Workshop on Accelerator Programming Using Directives (WACCPD 2022)},
	year = {2022},
	month = {2022-11},
	address = {Dallas, TX},
	author = {Anna Fortenberry and Stanimire Tomov}
}
@article {,
	title = {Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing},
	journal = {ACM Transactions on Mathematical Software},
	volume = {48},
	year = {2022},
	month = {2022-03},
	pages = {1 - 33},
	abstract = {In this article, we present Ginkgo, a modern C++ math library for scientific high performance computing. While classical linear algebra libraries act on matrix and vector objects, Ginkgo{\textquoteright}s design principle abstracts all functionality as {\textquotedblleft}linear operators,{\textquotedblright} motivating the notation of a {\textquotedblleft}linear operator algebra library.{\textquotedblright} Ginkgo{\textquoteright}s current focus is oriented toward providing sparse linear algebra functionality for high performance graphics processing unit (GPU) architectures, but given the library design, this focus can be easily extended to accommodate other algorithms and hardware architectures. We introduce this sophisticated software architecture that separates core algorithms from architecture-specific backends and provide details on extensibility and sustainability measures. We also demonstrate Ginkgo{\textquoteright}s usability by providing examples on how to use its functionality inside the MFEM and deal.ii finite element ecosystems. Finally, we offer a practical demonstration of Ginkgo{\textquoteright}s high performance on state-of-the-art GPU architectures.},
	issn = {0098-3500},
	doi = {10.1145/3480935},
	url = {https://dl.acm.org/doi/10.1145/3480935},
	author = {Anzt, Hartwig and Cojean, Terry and Flegar, Goran and G{\"o}bel, Fritz and Gr{\"u}tzmacher, Thomas and Nayak, Pratik and Ribizel, Tobias and Tsai, Yuhsiang Mike and Quintana-Ort{\'\i}, Enrique S}
}
@article {,
	title = {OpenMP application experiences: Porting to accelerated nodes},
	journal = {Parallel Computing},
	volume = {109},
	year = {2022},
	month = {2022-03},
	abstract = {As recent enhancements to the OpenMP specification become available in its implementations, there is a need to share the results of experimentation in order to better understand the OpenMP implementation{\textquoteright}s behavior in practice, to identify pitfalls, and to learn how the implementations can be effectively deployed in scientific codes. We report on experiences gained and practices adopted when using OpenMP to port a variety of ECP applications, mini-apps and libraries based on different computational motifs to accelerator-based leadership-class high-performance supercomputer systems at the United States Department of Energy. Additionally, we identify important challenges and open problems related to the deployment of OpenMP. Through our report of experiences, we find that OpenMP implementations are successful on current supercomputing platforms and that OpenMP is a promising programming model to use for applications to be run on emerging and future platforms with accelerated nodes.},
	issn = {01678191},
	doi = {10.1016/j.parco.2021.102856},
	url = {https://www.sciencedirect.com/science/article/pii/S0167819121001009},
	author = {Bak, Seonmyeong and Bertoni, Colleen and Boehm, Swen and Budiardja, Reuben and Chapman, Barbara M. and Doerfert, Johannes and Eisenbach, Markus and Finkel, Hal and Hernandez, Oscar and Huber, Joseph and Iwasaki, Shintaro and Kale, Vivek and Kent, Paul R.C. and Kwack, JaeHyuk and Lin, Meifeng and Luszczek, Piotr and Luo, Ye and Pham, Buu and Pophale, Swaroop and Ravikumar, Kiran and Sarkar, Vivek and Scogland, Thomas and Tian, Shilei and Yeung, P.K.}
}
@inproceedings {,
	title = {Prediction of Optimal Solvers for Sparse Linear Systems Using Deep Learning},
	journal = {2022 SIAM Conference on Parallel Processing for Scientific Computing (PP)},
	year = {2022},
	month = {2022},
	pages = {14 - 24},
	publisher = {Society for Industrial and Applied Mathematics},
	address = {Philadelphia, PA},
	abstract = {Solving sparse linear systems is a key task in a number of computational problems, such as data analysis and simulations, and majorly determines overall execution time. Choosing a suitable iterative solver algorithm, however, can significantly improve time-to-completion. We present a deep learning approach designed to predict the optimal iterative solver for a given sparse linear problem. For this, we detail useful linear system features to drive the prediction process, the metrics we use to quantify the iterative solvers{\textquoteright} time-to-approximation performance and a comprehensive experimental evaluation of the prediction quality of the neural network. Using a hyperparameter optimization and an ablation study on the SuiteSparse matrix collection we have inferred the importance of distinct features, achieving a top-1 classification accuracy of 60\%.},
	doi = {10.1137/1.978161197714110.1137/1.9781611977141.2},
	url = {https://epubs.siam.org/doi/10.1137/1.9781611977141.2},
	author = {Funk, Yannick and G{\"o}tz, Markus and Anzt, Hartwig},
	editor = {Li, Xiaoye S. and Teranishi, Keita}
}
@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 = {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 = {Linear Algebra Prepara.on for Emergent Neural Network Architectures: MAGMA, BLAS, and Batched GPU Computing},
	year = {2021},
	month = {2021-11},
	publisher = {LAPENNA Workshop},
	address = {Virtual},
	author = {Stanimire Tomov and Kwai Wong and Rocco Febbo and Julian Halloy}
}
@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}
}
@article {,
	title = {A survey of numerical linear algebra methods utilizing mixed-precision arithmetic},
	journal = {The International Journal of High Performance Computing Applications},
	volume = {35},
	number = {4},
	year = {2021},
	pages = {344{\textendash}369},
	abstract = {The efficient utilization of mixed-precision numerical linear algebra algorithms can offer attractive acceleration to scientific computing applications. Especially with the hardware integration of low-precision special-function units designed for machine learning applications, the traditional numerical algorithms community urgently needs to reconsider the floating point formats used in the distinct operations to efficiently leverage the available compute power. In this work, we provide a comprehensive survey of mixed-precision numerical linear algebra routines, including the underlying concepts, theoretical background, and experimental results for both dense and sparse linear algebra problems.},
	keywords = {GPUs, High-performance computing, linear algebra, Mixed-precision arithmetic, numerical mathematics},
	doi = {10.1177/10943420211003313},
	author = {Abdelfattah, Ahmad and Anzt, Hartwig and Boman, Erik G and Carson, Erin and Cojean, Terry and Jack Dongarra and Fox, Alyson and Mark Gates and Higham, Nicholas J and Li, Xiaoye S and others}
}
@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}
}
@conference {,
	title = {FFT-Based Gradient Sparsification for the Distributed Training of Deep Neural Networks},
	booktitle = {9th International Symposium on High-Performance Parallel and Distributed Computing (HPDC 20)},
	year = {2020},
	month = {2020-06},
	publisher = {ACM},
	organization = {ACM},
	address = {Stockholm, Sweden},
	abstract = {The performance and efficiency of distributed training of Deep Neural Networks (DNN) highly depend on the performance of gradient averaging among participating processes, a step bound by communication costs. There are two major approaches to reduce communication overhead: overlap communications with computations (lossless), or reduce communications (lossy). The lossless solution works well for linear neural architectures, e.g. VGG, AlexNet, but more recent networks such as ResNet and Inception limit the opportunity for such overlapping. Therefore, approaches that reduce the amount of data (lossy) become more suitable. In this paper, we present a novel, explainable lossy method that sparsifies gradients in the frequency domain, in addition to a new range-based float point representation to quantize and further compress gradients. These dynamic techniques strike a balance between compression ratio, accuracy, and computational overhead, and are optimized to maximize performance in heterogeneous environments.

Unlike existing works that strive for a higher compression ratio, we stress the robustness of our methods, and provide guidance to recover accuracy from failures. To achieve this, we prove how the FFT sparsification affects the convergence and accuracy, and show that our method is guaranteed to converge using a diminishing θ in training. Reducing θ can also be used to recover accuracy from the failure. Compared to STOA lossy methods, e.g., QSGD, TernGrad, and Top-k sparsification, our approach incurs less approximation error, thereby better in both the wall-time and accuracy. On an 8 GPUs, InfiniBand interconnected cluster, our techniques effectively accelerate AlexNet training up to 2.26x to the baseline of no compression, and 1.31x to QSGD, 1.25x to Terngrad and 1.47x to Top-K sparsification.},
	keywords = {FFT, Gradient Compression, Loosy Gradients, Machine Learning, Neural Networks},
	doi = {https://doi.org/10.1145/3369583.3392681},
	author = {Linnan Wang and Wei Wu and Junyu Zhang and Hang Liu and George Bosilca and Maurice Herlihy and Rodrigo Fonseca}
}
@inbook {1477,
	title = {Harnessing the Computing Continuum for Programming Our World},
	booktitle = { Fog Computing: Theory and Practice},
	year = {2020},
	publisher = {John Wiley \& Sons, Inc.},
	organization = {John Wiley \& Sons, Inc.},
	chapter = {7},
	abstract = {This chapter outlines a vision for how best to harness the computing continuum of interconnected sensors, actuators, instruments, and computing systems, from small numbers of very large devices to large numbers of very small devices. The hypothesis is that only via a continuum perspective one can intentionally specify desired continuum actions and effectively manage outcomes and systemic properties{\textemdash}adaptability and homeostasis, temporal constraints and deadlines{\textemdash}and elevate the discourse from device programming to intellectual goals and outcomes. Development of a framework for harnessing the computing continuum would catalyze new consumer services, business processes, social services, and scientific discovery. Realizing and implementing a continuum programming model requires balancing conflicting constraints and translating the high-level specification into a form suitable for execution on a unifying abstract machine model. In turn, the abstract machine must implement the mapping of specification demands to end-to-end resources.},
	isbn = {9781119551713},
	doi = {https://doi.org/10.1002/9781119551713.ch7},
	author = {Pete Beckman and Jack Dongarra and Nicola Ferrier and Geoffrey Fox and Terry Moore and Dan Reed and Micah Beck}
}
@article {,
	title = {How to Build Your Own Deep Neural Network},
	year = {2020},
	month = {2020-07},
	publisher = {PEARC20},
	keywords = {AI, Deep Neural Networks, dense linear algebra, HPC, ML},
	author = {Kwai Wong and Stanimire Tomov and Daniel Nichols and Rocco Febbo and Florent Lopez and Julian Halloy and Xianfeng Ma}
}
@article {,
	title = {Integrating Deep Learning in Domain Science at Exascale (MagmaDNN)},
	year = {2020},
	month = {2020-12},
	publisher = {DOD HPCMP seminar},
	address = {virtual},
	abstract = {We will present some of the current challenges in the design and integration of deep learning AI with traditional HPC simulations. We evaluate existing packages for readiness to run efficiently deep learning models and applications on large scale HPC systems, identify challenges, and propose new asynchronous parallelization and optimization techniques for current large-scale heterogeneous systems and up-coming exascale systems. These developments, along with existing HPC AI software capabilities, have been integrated in MagmaDNN, an open source HPC deep learning framework. 
Many deep learning frameworks are targeted towards data scientists and fall short in providing quality integration into existing HPC workflows. This paper discusses the necessities of an HPC deep learning framework and how these can be provided, e.g., as in MagmaDNN, through a deep integration with existing HPC libraries such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP. Advancements are also illustrated through the use of algorithmic enhancements in reduced and mixed-precision and asynchronous optimization methods. Finally, we present illustrations and potential solutions on enhancing traditional compute and data intensive applications at ORNL and UTK with AI. The approaches and future challenges are illustrated on materials science, imaging, and climate applications.},
	author = {Stanimire Tomov and Kwai Wong and Jack Dongarra and Rick Archibald and Edmond Chow and Eduardo D{\textquoteright}Azevedo and Markus Eisenbach and Rocco Febbo and Florent Lopez and Daniel Nichols and Junqi Yin}
}
@techreport {,
	title = {Integrating Deep Learning in Domain Sciences at Exascale},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-20-10},
	year = {2020},
	month = {2020-08},
	publisher = {University of Tennessee},
	abstract = {This paper presents some of the current challenges in designing deep learning artificial intelligence (AI) and integrating it with traditional high-performance computing (HPC) simulations. We evaluate existing packages for their ability to run deep learning models and applications on large-scale HPC systems e ciently, identify challenges, and propose new asynchronous parallelization and optimization techniques for current large-scale heterogeneous systems and upcoming exascale systems. These developments, along with existing HPC AI software capabilities, have been integrated into MagmaDNN, an open-source HPC deep learning framework. Many deep learning frameworks are targeted at data scientists and fall short in providing quality integration into existing HPC workflows. This paper discusses the necessities of an HPC deep learning framework and how those needs can be provided (e.g., as in MagmaDNN) through a deep integration with existing HPC libraries, such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP. Advancements are also illustrated through the use of algorithmic enhancements in reduced- and mixed-precision, as well as asynchronous optimization methods. Finally, we present illustrations and potential solutions for enhancing traditional compute- and data-intensive applications at ORNL and UTK with AI. The approaches and future challenges are illustrated in materials science, imaging, and climate applications.},
	author = {Rick Archibald and Edmond Chow and Eduardo D{\textquoteright}Azevedo and Jack Dongarra and Markus Eisenbach and Rocco Febbo and Florent Lopez and Daniel Nichols and Stanimire Tomov and Kwai Wong and Junqi Yin}
}
@conference {,
	title = {Integrating Deep Learning in Domain Sciences at Exascale},
	booktitle = {2020 Smoky Mountains Computational Sciences and Engineering Conference (SMC 2020)},
	year = {2020},
	month = {2020-08},
	abstract = {This paper presents some of the current challenges in designing deep learning artificial intelligence (AI) and integrating it with traditional high-performance computing (HPC) simulations. We evaluate existing packages for their ability to run deep learning models and applications on large-scale HPC systems e ciently, identify challenges, and propose new asynchronous parallelization and optimization techniques for current large-scale heterogeneous systems and upcoming exascale systems. These developments, along with existing HPC AI software capabilities, have been integrated into MagmaDNN, an open-source HPC deep learning framework. Many deep learning frameworks are targeted at data scientists and fall short in providing quality integration into existing HPC workflows. This paper discusses the necessities of an HPC deep learning framework and how those needs can be provided (e.g., as in MagmaDNN) through a deep integration with existing HPC libraries, such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP. Advancements are also illustrated through the use of algorithmic enhancements in reduced- and mixed-precision, as well as asynchronous optimization methods. Finally, we present illustrations and potential solutions for enhancing traditional compute- and data-intensive applications at ORNL and UTK with AI. The approaches and future challenges are illustrated in materials science, imaging, and climate applications.},
	author = {Rick Archibald and Edmond Chow and Eduardo D{\textquoteright}Azevedo and Jack Dongarra and Markus Eisenbach and Rocco Febbo and Florent Lopez and Daniel Nichols and Stanimire Tomov and Kwai Wong and Junqi Yin}
}
@article {,
	title = {Load-Balancing Sparse Matrix Vector Product Kernels on GPUs},
	journal = {ACM Transactions on Parallel Computing},
	volume = {7},
	year = {2020},
	month = {2020-03},
	abstract = {Efficient processing of Irregular Matrices on Single Instruction, Multiple Data (SIMD)-type architectures is a persistent challenge. Resolving it requires innovations in the development of data formats, computational techniques, and implementations that strike a balance between thread divergence, which is inherent for Irregular Matrices, and padding, which alleviates the performance-detrimental thread divergence but introduces artificial overheads. To this end, in this article, we address the challenge of designing high performance sparse matrix-vector product (SpMV) kernels designed for Nvidia Graphics Processing Units (GPUs). We present a compressed sparse row (CSR) format suitable for unbalanced matrices. We also provide a load-balancing kernel for the coordinate (COO) matrix format and extend it to a hybrid algorithm that stores part of the matrix in SIMD-friendly Ellpack format (ELL) format. The ratio between the ELL- and the COO-part is determined using a theoretical analysis of the nonzeros-per-row distribution. For the over 2,800 test matrices available in the Suite Sparse matrix collection, we compare the performance against SpMV kernels provided by NVIDIA{\textquoteright}s cuSPARSE library and a heavily-tuned sliced ELL (SELL-P) kernel that prevents unnecessary padding by considering the irregular matrices as a combination of matrix blocks stored in ELL format.},
	doi = {https://doi.org/10.1145/3380930},
	author = {Hartwig Anzt and Terry Cojean and Chen Yen-Chen and Jack Dongarra and Goran Flegar and Pratik Nayak and Stanimire Tomov and Yuhsiang M. Tsai and Weichung Wang}
}
@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}
}
@conference {,
	title = {Multiprecision Block-Jacobi for Iterative Triangular Solves},
	booktitle = {European Conference on Parallel Processing (Euro-Par 2020)},
	year = {2020},
	month = {2020-08},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Recent research efforts have shown that Jacobi and block-Jacobi relaxation methods can be used as an effective and highly parallel approach for the solution of sparse triangular linear systems arising in the application of ILU-type preconditioners. Simultaneously, a few independent works have focused on designing efficient high performance adaptive-precision block-Jacobi preconditioning (block-diagonal scaling), in the context of the iterative solution of sparse linear systems, on manycore architectures. In this paper, we bridge the gap between relaxation methods based on regular splittings and preconditioners by demonstrating that iterative refinement can be leveraged to construct a relaxation method from the preconditioner. In addition, we exploit this insight to construct a highly-efficient sparse triangular system solver for graphics processors that combines iterative refinement with the block-Jacobi preconditioner available in the Ginkgo library.},
	keywords = {Block-Jacobi, graphics processing units (GPUs), incomplete factorization preconditioning, multiprecision, sparse linear algebra},
	doi = {https://doi.org/10.1007/978-3-030-57675-2_34},
	author = {Fritz Goebel and Hartwig Anzt and Terry Cojean and Goran Flegar and Enrique S. Quintana-Orti}
}
@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}
}
@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}
}
@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}
}
@article {1319,
	title = {Adaptive Precision in Block-Jacobi Preconditioning for Iterative Sparse Linear System Solvers},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {31},
	number = {6},
	year = {2019},
	month = {2019-03},
	pages = {e4460},
	abstract = {Summary We propose an adaptive scheme to reduce communication overhead caused by data movement by selectively storing the diagonal blocks of a block-Jacobi preconditioner in different precision formats (half, single, or double). This specialized preconditioner can then be combined with any Krylov subspace method for the solution of sparse linear systems to perform all arithmetic in double precision. We assess the effects of the adaptive precision preconditioner on the iteration count and data transfer cost of a preconditioned conjugate gradient solver. A preconditioned conjugate gradient method is, in general, a memory bandwidth-bound algorithm, and therefore its execution time and energy consumption are largely dominated by the costs of accessing the problem{\textquoteright}s data in memory. Given this observation, we propose a model that quantifies the time and energy savings of our approach based on the assumption that these two costs depend linearly on the bit length of a floating point number. Furthermore, we use a number of test problems from the SuiteSparse matrix collection to estimate the potential benefits of the adaptive block-Jacobi preconditioning scheme.},
	keywords = {adaptive precision, block-Jacobi preconditioning, communication reduction, energy efficiency, Krylov subspace methods, sparse linear systems},
	doi = {https://doi.org/10.1002/cpe.4460},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.4460},
	author = {Hartwig Anzt and Jack Dongarra and Goran Flegar and Nicholas J. Higham and Enrique S. Quintana-Orti}
}
@article {1262,
	title = {Algorithms and Optimization Techniques for High-Performance Matrix-Matrix Multiplications of Very Small Matrices},
	journal = {Parallel Computing},
	volume = {81},
	year = {2019},
	month = {2019-01},
	pages = {1{\textendash}21},
	abstract = {Expressing scientific computations in terms of BLAS, and in particular the general dense matrix-matrix multiplication (GEMM), is of fundamental importance for obtaining high performance portability across architectures. However, GEMMs for small matrices of sizes smaller than 32 are not sufficiently optimized in existing libraries. We consider the computation of many small GEMMs and its performance portability for a wide range of computer architectures, including Intel CPUs, ARM, IBM, Intel Xeon Phi, and GPUs. These computations often occur in applications like big data analytics, machine learning, high-order finite element methods (FEM), and others. The GEMMs are grouped together in a single batched routine. For these cases, we present algorithms and their optimization techniques that are specialized for the matrix sizes and architectures of interest. We derive a performance model and show that the new developments can be tuned to obtain performance that is within 90\% of the optimal for any of the architectures of interest. For example, on a V100 GPU for square matrices of size 32, we achieve an execution rate of about 1600 gigaFLOP/s in double-precision arithmetic, which is 95\% of the theoretically derived peak for this computation on a V100 GPU. We also show that these results outperform currently available state-of-the-art implementations such as vendor-tuned math libraries, including Intel MKL and NVIDIA CUBLAS, as well as open-source libraries like OpenBLAS and Eigen.},
	keywords = {Autotuning, Batched GEMM, HPC, Matrix-matrix product, optimization, Small matrices},
	doi = {https://doi.org/10.1016/j.parco.2018.10.003},
	author = {Ian Masliah and Ahmad Abdelfattah and Azzam Haidar and Stanimire Tomov and Marc Baboulin and Jo{\"e}l Falcou and Jack Dongarra}
}
@conference {1367,
	title = {Are we Doing the Right Thing? {\textendash} A Critical Analysis of the Academic HPC Community},
	booktitle = {2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
	year = {2019},
	month = {2019-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Rio de Janeiro, Brazil},
	abstract = {Like in any other research field, academically surviving in the High Performance Computing (HPC) community generally requires to publish papers, in the bast case many of them and in high-ranked journals or at top-tier conferences. As a result, the number of scientific papers published each year in this relatively small community easily outnumbers what a single researcher can read. At the same time, many of the proposed and analyzed strategies, algorithms, and hardware-optimized implementations never make it beyond the prototype stage, as they are abandoned once they served the single purpose of yielding (another) publication. In a time and field where high-quality manpower is a scarce resource, this is extremely inefficient. In this position paper we promote a radical paradigm shift towards accepting high-quality software patches to community software packages as legitimate conference contributions. In consequence, the reputation and appointability of researchers is no longer based on the classical scientific metrics, but on the quality and documentation of open source software contributions - effectively improving and accelerating the collaborative development of community software.},
	doi = {10.1109/IPDPSW.2019.00122},
	author = {Hartwig Anzt and Goran Flegar}
}
@techreport {1433,
	title = {CEED ECP Milestone Report: Performance Tuning of CEED Software and 1st and 2nd Wave Apps},
	year = {2019},
	month = {2019-10},
	publisher = {Zenodo},
	doi = {https://doi.org/10.5281/zenodo.3477618},
	author = {Stanimire Tomov and Ahmad Abdelfattah and Valeria Barra and Natalie Beams and Jed Brown and Jean-Sylvain Camier and Veselin Dobrev and Jack Dongarra and Yohann Dudouit and Paul Fischer and Ali Karakus and Stefan Kerkemeier and Tzanio Kolev and YuHsiang Lan and Elia Merzari and Misun Min and Aleks Obabko and Scott Parker and Thilina Ratnayaka and Jeremy Thompson and Ananias Tomboulides and Vladimir Tomov and Tim Warburton}
}
@techreport {1434,
	title = {CEED ECP Milestone Report: Public release of CEED 2.0},
	year = {2019},
	month = {2019-04},
	publisher = {Zenodo},
	doi = {10.5281/zenodo.2641316},
	url = {https://doi.org/10.5281/zenodo.2641316},
	author = {Jed Brown and Ahmad Abdelfattah and Valeria Barra and Veselin Dobrev and Yohann Dudouit and Paul Fischer and Tzanio Kolev and David Medina and Misun Min and Thilina Ratnayaka and Cameron Smith and Jeremy Thompson and Stanimire Tomov and Vladimir Tomov and Tim Warburton}
}
@article {1305,
	title = {Checkpointing Strategies for Shared High-Performance Computing Platforms},
	journal = {International Journal of Networking and Computing},
	volume = {9},
	number = {1},
	year = {2019},
	pages = {28{\textendash}52},
	abstract = {Input/output (I/O) from various sources often contend for scarcely available bandwidth. For example, checkpoint/restart (CR) protocols can help to ensure application progress in failure-prone environments. However, CR I/O alongside an application{\textquoteright}s normal, requisite I/O can increase I/O contention and might negatively impact performance. In this work, we consider different aspects (system-level scheduling policies and hardware) that optimize the overall performance of concurrently executing CR-based applications that share I/O resources. We provide a theoretical model and derive a set of necessary constraints to minimize the global waste on a given platform. Our results demonstrate that Young/Daly{\textquoteright}s optimal checkpoint interval, despite providing a sensible metric for a single, undisturbed application, is not sufficient to optimally address resource contention at scale. We show that by combining optimal checkpointing periods with contention-aware system-level I/O scheduling strategies, we can significantly improve overall application performance and maximize the platform throughput. Finally, we evaluate how specialized hardware, namely burst buffers, may help to mitigate the I/O contention problem. Overall, these results provide critical analysis and direct guidance on how to design efficient, CR ready, large -scale platforms without a large investment in the I/O subsystem.},
	issn = {2185-2847},
	url = {http://www.ijnc.org/index.php/ijnc/article/view/195},
	author = {Thomas Herault and Yves Robert and Aurelien Bouteiller and Dorian Arnold and Kurt Ferreira and George Bosilca and Jack Dongarra}
}
@techreport {1398,
	title = {A Collection of Presentations from the BDEC2 Workshop in Kobe, Japan},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-19-09},
	year = {2019},
	month = {2019-02},
	publisher = {University of Tennessee, Knoxville},
	author = {Rosa M. Badia and Micah Beck and Fran{\c c}ois Bodin and Taisuke Boku and Franck Cappello and Alok Choudhary and Carlos Costa and Ewa Deelman and Nicola Ferrier and Katsuki Fujisawa and Kohei Fujita and Maria Girone and Geoffrey Fox and Shantenu Jha and Yoshinari Kameda and Christian Kniep and William Kramer and James Lin and Kengo Nakajima and Yiwei Qiu and Kishore Ramachandran and Glenn Ricart and Kim Serradell and Dan Stanzione and Lin Gan and Martin Swany and Christine Sweeney and Alex Szalay and Christine Kirkpatrick and Kenton McHenry and Alainna White and Steve Tuecke and Ian Foster and Joe Mambretti and William. M Tang and Michela Taufer and Miguel V{\'a}zquez}
}
@techreport {1399,
	title = {A Collection of White Papers from the BDEC2 Workshop in Poznan, Poland},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-19-10},
	year = {2019},
	month = {2019-05},
	publisher = {University of Tennessee, Knoxville},
	author = {Gabriel Antoniu and Alexandru Costan and Ovidiu Marcu and Maria S. P{\'e}rez and Nenad Stojanovic and Rosa M. Badia and Miguel V{\'a}zquez and Sergi Girona and Micah Beck and Terry Moore and Piotr Luszczek and Ezra Kissel and Martin Swany and Geoffrey Fox and Vibhatha Abeykoon and Selahattin Akkas and Kannan Govindarajan and Gurhan Gunduz and Supun Kamburugamuve and Niranda Perera and Ahmet Uyar and Pulasthi Wickramasinghe and Chathura Widanage and Maria Girone and Toshihiro Hanawa and Richard Moreno and Ariel Oleksiak and Martin Swany and Ryousei Takano and M.P. van Haarlem and J. van Leeuwen and J.B.R. Oonk and T. Shimwell and L.V.E. Koopmans}
}
@techreport {1408,
	title = {A Collection of White Papers from the BDEC2 Workshop in San Diego, CA},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-19-13},
	year = {2019},
	month = {2019-10},
	publisher = {University of Tennessee},
	author = {Ilkay Altintas and Kyle Marcus and Volkan Vural and Shweta Purawat and Daniel Crawl and Gabriel Antoniu and Alexandru Costan and Ovidiu Marcu and Prasanna Balaprakash and Rongqiang Cao and Yangang Wang and Franck Cappello and Robert Underwood and Sheng Di and Justin M. Wozniak and Jon C. Calhoun and Cong Xu and Antonio Lain and Paolo Faraboschi and Nic Dube and Dejan Milojicic and Balazs Gerofi and Maria Girone and Viktor Khristenko and Tony Hey and Erza Kissel and Yu Liu and Richard Loft and Pekka Manninen and Sebastian von Alfthan and Takemasa Miyoshi and Bruno Raffin and Olivier Richard and Denis Trystram and Maryam Rahnemoonfar and Robin Murphy and Joel Saltz and Kentaro Sano and Rupak Roy and Kento Sato and Jian Guo and Jen s Domke and Weikuan Yu and Takaki Hatsui and Yasumasa Joti and Alex Szalay and William M. Tang and Michael R. Wyatt II and Michela Taufer and Todd Gamblin and Stephen Herbein and Adam Moody and Dong H. Ahn and Rich Wolski and Chandra Krintz and Fatih Bakir and Wei-tsung Lin and Gareth George}
}
@article {1369,
	title = {A Customized Precision Format Based on Mantissa Segmentation for Accelerating Sparse Linear Algebra},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {40319},
	year = {2019},
	month = {2019-01},
	issn = {1532-0626},
	doi = {https://doi.org/10.1002/cpe.5418},
	author = {Thomas Gruetzmacher and Terry Cojean and Goran Flegar and Fritz G{\"o}bel and Hartwig Anzt}
}
@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}
}
@conference {1436,
	title = {ParILUT {\textendash} A Parallel Threshold ILU for GPUs},
	booktitle = {IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
	year = {2019},
	month = {2019-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Rio de Janeiro, Brazil},
	abstract = {In this paper, we present the first algorithm for computing threshold ILU factorizations on GPU architectures. The proposed ParILUT-GPU algorithm is based on interleaving parallel fixed-point iterations that approximate the incomplete factors for an existing nonzero pattern with a strategy that dynamically adapts the nonzero pattern to the problem characteristics. This requires the efficient selection of thresholds that separate the values to be dropped from the incomplete factors, and we design a novel selection algorithm tailored towards GPUs. All components of the ParILUT-GPU algorithm make heavy use of the features available in the latest NVIDIA GPU generations, and outperform existing multithreaded CPU implementations.},
	doi = {https://doi.org/10.1109/IPDPS.2019.00033},
	author = {Hartwig Anzt and Tobias Ribizel and Goran Flegar and Edmond Chow and Jack Dongarra}
}
@techreport {1394,
	title = {SLATE Working Note 13: Implementing Singular Value and Symmetric/Hermitian Eigenvalue Solvers},
	journal = {SLATE Working Notes},
	number = {13, ICL-UT-19-07},
	year = {2019},
	note = {revision 06-2023},
	month = {2019-09},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	type = {SLATE Working Notes},
	author = {Mark Gates and Mohammed Al Farhan and Ali Charara and Jakub Kurzak and Dalal Sukkari and Asim YarKhan and Jack Dongarra}
}
@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 {1317,
	title = {Toward a Modular Precision Ecosystem for High-Performance Computing},
	journal = {The International Journal of High Performance Computing Applications},
	volume = {33},
	year = {2019},
	month = {2019-11},
	pages = {1069-1078},
	abstract = {With the memory bandwidth of current computer architectures being significantly slower than the (floating point) arithmetic performance, many scientific computations only leverage a fraction of the computational power in today{\textquoteright}s high-performance architectures. At the same time, memory operations are the primary energy consumer of modern architectures, heavily impacting the resource cost of large-scale applications and the battery life of mobile devices. This article tackles this mismatch between floating point arithmetic throughput and memory bandwidth by advocating a disruptive paradigm change with respect to how data are stored and processed in scientific applications. Concretely, the goal is to radically decouple the data storage format from the processing format and, ultimately, design a {\textquotedblleft}modular precision ecosystem{\textquotedblright} that allows for more flexibility in terms of customized data access. For memory-bounded scientific applications, dynamically adapting the memory precision to the numerical requirements allows for attractive resource savings. In this article, we demonstrate the potential of employing a modular precision ecosystem for the block-Jacobi preconditioner and the PageRank algorithm{\textemdash}two applications that are popular in the communities and at the same characteristic representatives for the field of numerical linear algebra and data analytics, respectively.},
	keywords = {conjugate gradient, GPUs, Jacobi method, Modular precision, multicore processors, PageRank, parallel numerical linear algebra},
	issn = {1094-3420},
	doi = {https://doi.org/10.1177/1094342019846547},
	author = {Hartwig Anzt and Goran Flegar and Thomas Gruetzmacher and Enrique S. Quintana-Orti}
}
@conference {1318,
	title = {Towards Continuous Benchmarking},
	booktitle = {Platform for Advanced Scientific Computing Conference (PASC 2019)},
	year = {2019},
	month = {2019-06},
	publisher = {ACM Press},
	organization = {ACM Press},
	address = {Zurich, Switzerland},
	abstract = {We present an automated performance evaluation framework that enables an automated workflow for testing and performance evaluation of software libraries. Integrating this component into an ecosystem enables sustainable software development, as a community effort, via a web application for interactively evaluating the performance of individual software components. The performance evaluation tool is based exclusively on web technologies, which removes the burden of downloading performance data or installing additional software. We employ this framework for the Ginkgo software ecosystem, but the framework can be used with essentially any software project, including the comparison between different software libraries. The Continuous Integration (CI) framework of Ginkgo is also extended to automatically run a benchmark suite on predetermined HPC systems, store the state of the machine and the environment along with the compiled binaries, and collect results in a publicly accessible performance data repository based on Git. The Ginkgo performance explorer (GPE) can be used to retrieve the performance data from the repository, and visualizes it in a web browser. GPE also implements an interface that allows users to write scripts, archived in a Git repository, to extract particular data, compute particular metrics, and visualize them in many different formats (as specified by the script). The combination of these approaches creates a workflow which enables performance reproducibility and software sustainability of scientific software. In this paper, we present example scripts that extract and visualize performance data for Ginkgo{\textquoteright}s SpMV kernels that allow users to identify the optimal kernel for specific problem characteristics.},
	isbn = {9781450367707},
	doi = {https://doi.org/10.1145/3324989.3325719},
	author = {Hartwig Anzt and Yen Chen Chen and Terry Cojean and Jack Dongarra and Goran Flegar and Pratik Nayak and Enrique S. Quintana-Orti and Yuhsiang M. Tsai and Weichung Wang}
}
@article {1220,
	title = {Variable-Size Batched Gauss-Jordan Elimination for Block-Jacobi Preconditioning on Graphics Processors},
	journal = {Parallel Computing},
	volume = {81},
	year = {2019},
	month = {2019-01},
	pages = {131-146},
	abstract = {In this work, we address the efficient realization of block-Jacobi preconditioning on graphics processing units (GPUs). This task requires the solution of a collection of small and independent linear systems. To fully realize this implementation, we develop a variable-size batched matrix inversion kernel that uses Gauss-Jordan elimination (GJE) along with a variable-size batched matrix{\textendash}vector multiplication kernel that transforms the linear systems{\textquoteright} right-hand sides into the solution vectors. Our kernels make heavy use of the increased register count and the warp-local communication associated with newer GPU architectures. Moreover, in the matrix inversion, we employ an implicit pivoting strategy that migrates the workload (i.e., operations) to the place where the data resides instead of moving the data to the executing cores. We complement the matrix inversion with extraction and insertion strategies that allow the block-Jacobi preconditioner to be set up rapidly. The experiments on NVIDIA{\textquoteright}s K40 and P100 architectures reveal that our variable-size batched matrix inversion routine outperforms the CUDA basic linear algebra subroutine (cuBLAS) library functions that provide the same (or even less) functionality. We also show that the preconditioner setup and preconditioner application cost can be somewhat offset by the faster convergence of the iterative solver.},
	keywords = {Batched algorithms, Block-Jacobi, Gauss{\textendash}Jordan elimination, Graphics processor, matrix inversion, sparse linear systems},
	doi = {https://doi.org/10.1016/j.parco.2017.12.006},
	author = {Hartwig Anzt and Jack Dongarra and Goran Flegar and Enrique S. Quintana-Orti}
}
@techreport {1229,
	title = {Algorithms and Optimization Techniques for High-Performance Matrix-Matrix Multiplications of Very Small Matrices},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-18-09},
	year = {2018},
	month = {2018-09},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	abstract = {Expressing scientific computations in terms of BLAS, and in particular the general dense matrix-matrix multiplication (GEMM), is of fundamental importance for obtaining high performance portability across architectures. However, GEMMs for small matrices of sizes smaller than 32 are not sufficiently optimized in existing libraries. We consider the computation of many small GEMMs and its performance portability for a wide range of computer architectures, including Intel CPUs, ARM, IBM, Intel Xeon Phi, and GPUs. These computations often occur in applications like big data analytics, machine learning, high-order finite element methods (FEM), and others. The GEMMs are grouped together in a single batched routine. For these cases, we present algorithms and their optimization techniques that are specialized for the matrix sizes and architectures of interest. We derive a performance model and show that the new developments can be tuned to obtain performance that is within 90\% of the optimal for any of the architectures of interest. For example, on a V100 GPU for square matrices of size 32, we achieve an execution rate of about 1; 600 gigaFLOP/s in double-precision arithmetic, which is 95\% of the theoretically derived peak for this computation on a V100 GPU. We also show that these results outperform currently available state-of-the-art implementations such as vendor-tuned math libraries, including Intel MKL and NVIDIA CUBLAS, as well as open-source libraries like OpenBLAS and Eigen.},
	author = {Ian Masliah and Ahmad Abdelfattah and Azzam Haidar and Stanimire Tomov and Marc Baboulin and Jo{\"e}l Falcou and Jack Dongarra}
}
@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 {1201,
	title = {Evaluation of Dataflow Programming Models for Electronic Structure Theory},
	journal = {Concurrency and Computation: Practice and Experience: Special Issue on Parallel and Distributed Algorithms},
	volume = {2018},
	year = {2018},
	month = {2018-05},
	pages = {1{\textendash}20},
	abstract = {Dataflow programming models have been growing in popularity as a means to deliver a good balance between performance and portability in the post-petascale era. In this paper, we evaluate different dataflow programming models for electronic structure methods and compare them in terms of programmability, resource utilization, and scalability. In particular, we evaluate two programming paradigms for expressing scientific applications in a dataflow form: (1) explicit dataflow, where the dataflow is specified explicitly by the developer, and (2) implicit dataflow, where a task scheduling runtime derives the dataflow using per-task data-access information embedded in a serial program. We discuss our findings and present a thorough experimental analysis using methods from the NWChem quantum chemistry application as our case study, and OpenMP, StarPU, and PaRSEC as the task-based runtimes that enable the different forms of dataflow execution. Furthermore, we derive an abstract model to explore the limits of the different dataflow programming paradigms.},
	keywords = {CCSD, coupled cluster methods, dataflow, NWChem, OpenMP, parsec, StarPU, task-based runtime},
	doi = {https://doi.org/10.1002/cpe.4490},
	author = {Heike Jagode and Anthony Danalis and Reazul Hoque and Mathieu Faverge and Jack Dongarra}
}
@techreport {1228,
	title = {Linear Systems Performance Report},
	journal = {SLATE Working Notes},
	number = {08, ICL-UT-18-08},
	year = {2018},
	month = {2018-09},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	type = {SLATE Working Notes},
	author = {Jakub Kurzak and Mark Gates and Ichitaro Yamazaki and Ali Charara and Asim YarKhan and Jamie Finney and Gerald Ragghianti and Piotr Luszczek and Jack Dongarra}
}
@conference {1196,
	title = {Optimal Cooperative Checkpointing for Shared High-Performance Computing Platforms},
	booktitle = {2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Best Paper Award},
	year = {2018},
	month = {2018-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Vancouver, BC, Canada},
	abstract = {In high-performance computing environments, input/output (I/O) from various sources often contend for scarce available bandwidth. Adding to the I/O operations inherent to the failure-free execution of an application, I/O from checkpoint/restart (CR) operations (used to ensure progress in the presence of failures) place an additional burden as it increase I/O contention, leading to degraded performance. In this work, we consider a cooperative scheduling policy that optimizes the overall performance of concurrently executing CR-based applications which share valuable I/O resources. First, we provide a theoretical model and then derive a set of necessary constraints needed to minimize the global waste on the platform. Our results demonstrate that the optimal checkpoint interval, as defined by Young/Daly, despite providing a sensible metric for a single application, is not sufficient to optimally address resource contention at the platform scale. We therefore show that combining optimal checkpointing periods with I/O scheduling strategies can provide a significant improvement on the overall application performance, thereby maximizing platform throughput. Overall, these results provide critical analysis and direct guidance on checkpointing large-scale workloads in the presence of competing I/O while minimizing the impact on application performance.},
	doi = {10.1109/IPDPSW.2018.00127},
	author = {Thomas Herault and Yves Robert and Aurelien Bouteiller and Dorian Arnold and Kurt Ferreira and George Bosilca and Jack Dongarra}
}
@techreport {1191,
	title = {Parallel BLAS Performance Report},
	journal = {SLATE Working Notes},
	number = {05, ICL-UT-18-01},
	year = {2018},
	month = {2018-04},
	publisher = {University of Tennessee},
	author = {Jakub Kurzak and Mark Gates and Asim YarKhan and Ichitaro Yamazaki and Panruo Wu and Piotr Luszczek and Jamie Finney and Jack Dongarra}
}
@techreport {1206,
	title = {Parallel Norms Performance Report},
	journal = {SLATE Working Notes},
	number = {06, ICL-UT-18-06},
	year = {2018},
	month = {2018-06},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	author = {Jakub Kurzak and Mark Gates and Asim YarKhan and Ichitaro Yamazaki and Piotr Luszczek and Jamie Finney and Jack Dongarra}
}
@conference {1234,
	title = {Variable-Size Batched Condition Number Calculation on GPUs},
	booktitle = {SBAC-PAD},
	year = {2018},
	month = {2018-09},
	address = {Lyon, France},
	url = {https://ieeexplore.ieee.org/document/8645907},
	author = {Hartwig Anzt and Jack Dongarra and Goran Flegar and Thomas Gruetzmacher}
}
@article {1341,
	title = {Accelerating Tensor Contractions in High-Order FEM with MAGMA Batched},
	year = {2017},
	month = {2017-03},
	publisher = {SIAM Conference on Computer Science and Engineering (SIAM CSE17), Presentation},
	address = {Atlanta, GA},
	author = {Ahmad Abdelfattah and Marc Baboulin and Veselin Dobrev and Jack Dongarra and Christopher Earl and Jo{\"e}l Falcou and Azzam Haidar and Ian Karlin and Tzanio Kolev and Ian Masliah and Stanimire Tomov}
}
@inproceedings {998,
	title = {Batched Gauss-Jordan Elimination for Block-Jacobi Preconditioner Generation on GPUs},
	journal = {Proceedings of the 8th International Workshop on Programming Models and Applications for Multicores and Manycores},
	year = {2017},
	month = {2017-02},
	pages = {1{\textendash}10},
	publisher = {ACM},
	address = {New York, NY, USA},
	abstract = {In this paper, we design and evaluate a routine for the efficient generation of block-Jacobi preconditioners on graphics processing units (GPUs). Concretely, to exploit the architecture of the graphics accelerator, we develop a batched Gauss-Jordan elimination CUDA kernel for matrix inversion that embeds an implicit pivoting technique and handles the entire inversion process in the GPU registers. In addition, we integrate extraction and insertion CUDA kernels to rapidly set up the block-Jacobi preconditioner.

Our experiments compare the performance of our implementation against a sequence of batched routines from the MAGMA library realizing the inversion via the LU factorization with partial pivoting. Furthermore, we evaluate the costs of different strategies for the block-Jacobi extraction and insertion steps, using a variety of sparse matrices from the SuiteSparse matrix collection. Finally, we assess the efficiency of the complete block-Jacobi preconditioner generation in the context of an iterative solver applied to a set of computational science problems, and quantify its benefits over a scalar Jacobi preconditioner.},
	keywords = {block-Jacobi preconditioner, Gauss-Jordan elimination, graphics processing units (GPUs), iterative methods, matrix inversion, sparse linear systems},
	isbn = {978-1-4503-4883-6},
	doi = {10.1145/3026937.3026940},
	url = {http://doi.acm.org/10.1145/3026937.3026940},
	author = {Hartwig Anzt and Jack Dongarra and Goran Flegar and Enrique S. Quintana-Orti}
}
@conference {1093,
	title = {Bidiagonalization and R-Bidiagonalization: Parallel Tiled Algorithms, Critical Paths and Distributed-Memory Implementation},
	booktitle = {IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
	year = {2017},
	month = {2017-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Orlando, FL},
	abstract = {We study tiled algorithms for going from a "full" matrix to a condensed "band bidiagonal" form using orthog-onal transformations: (i) the tiled bidiagonalization algorithm BIDIAG, which is a tiled version of the standard scalar bidiago-nalization algorithm; and (ii) the R-bidiagonalization algorithm R-BIDIAG, which is a tiled version of the algorithm which consists in first performing the QR factorization of the initial matrix, then performing the band-bidiagonalization of the R- factor. For both BIDIAG and R-BIDIAG, we use four main types of reduction trees, namely FLATTS, FLATTT, GREEDY, and a newly introduced auto-adaptive tree, AUTO. We provide a study of critical path lengths for these tiled algorithms, which shows that (i) R-BIDIAG has a shorter critical path length than BIDIAG for tall and skinny matrices, and (ii) GREEDY based schemes are much better than earlier proposed algorithms with unbounded resources. We provide experiments on a single multicore node, and on a few multicore nodes of a parallel distributed shared- memory system, to show the superiority of the new algorithms on a variety of matrix sizes, matrix shapes and core counts.},
	keywords = {Algorithm design and analysis, Approximation algorithms, Kernel, Multicore processing, Shape, Software algorithms, Transforms},
	doi = {10.1109/IPDPS.2017.46},
	author = {Mathieu Faverge and Julien Langou and Yves Robert and Jack Dongarra}
}
@techreport {1175,
	title = {C++ API for Batch BLAS},
	journal = {SLATE Working Notes},
	number = {04, ICL-UT-17-12},
	year = {2017},
	month = {2017-12},
	publisher = {University of Tennessee},
	author = {Ahmad Abdelfattah and Konstantin Arturov and Cris Cecka and Jack Dongarra and Chip Freitag and Mark Gates and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov and Panruo Wu}
}
@techreport {1081,
	title = {C++ API for BLAS and LAPACK},
	journal = {SLATE Working Notes},
	number = {02, ICL-UT-17-03},
	year = {2017},
	note = {Revision 02-21-2018},
	month = {2017-06},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	author = {Mark Gates and Piotr Luszczek and Ahmad Abdelfattah and Jakub Kurzak and Jack Dongarra and Konstantin Arturov and Cris Cecka and Chip Freitag}
}
@techreport {1138,
	title = {The Case for Directive Programming for Accelerator Autotuner Optimization},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-17-07},
	year = {2017},
	month = {2017-10},
	publisher = {University of Tennessee},
	abstract = {In this work, we present the use of compiler pragma directives for parallelizing autotuning of specialized compute kernels for hardware accelerators. A set of constructs, that include prallelizing a source code that prune a generated search space with a large number of constraints for an autotunning infrastructure. For a better performance we studied optimization aimed at minimization of the run time.We also studied the behavior of the parallel load balance and the speedup on four different machines: x86, Xeon Phi, ARMv8, and POWER8.},
	author = {Diana Fayad and Jakub Kurzak and Piotr Luszczek and Panruo Wu and Jack Dongarra}
}
@article {1163,
	title = {Flexible Batched Sparse Matrix Vector Product on GPUs},
	year = {2017},
	month = {2017-11},
	publisher = {ScalA{\textquoteright}17: 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems},
	address = {Denver, Colorado},
	author = {Hartwig Anzt and Collins, Gary and Jack Dongarra and Goran Flegar and Enrique S. Quintana-Orti}
}
@conference {,
	title = {Flexible Batched Sparse Matrix-Vector Product on GPUs},
	booktitle = {8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA {\textquoteright}17)},
	year = {2017},
	month = {2017-11},
	publisher = {ACM Press},
	organization = {ACM Press},
	address = {Denver, CO},
	abstract = {	
We propose a variety of batched routines for concurrently processing a large collection of small-size, independent sparse matrix-vector products (SpMV) on graphics processing units (GPUs). These batched SpMV kernels are designed to be flexible in order to handle a batch of matrices which differ in size, nonzero count, and nonzero distribution. Furthermore, they support three most commonly used sparse storage formats: CSR, COO and ELL. Our experimental results on a state-of-the-art GPU reveal performance improvements of up to 25X compared to non-batched SpMV routines.},
	doi = {http://dx.doi.org/10.1145/3148226.3148230},
	author = {Hartwig Anzt and Gary Collins and Jack Dongarra and Goran Flegar and Enrique S. Quintana-Orti}
}
@techreport {1130,
	title = {MAGMA-sparse Interface Design Whitepaper},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-17-05},
	year = {2017},
	month = {2017-09},
	type = {Technical Report},
	abstract = {In this report we describe the logic and interface we develop for the MAGMA-sparse library
to allow for easy integration as third-party library into a top-level software ecosystem. The
design choices are based on extensive consultation with other software library developers, in
particular the Trilinos software development team. The interface documentation is at this point
not exhaustive, but a first proposal for setting a standard. Although the interface description
targets the MAGMA-sparse software module, we hope that the design choices carry beyond this
specific library, and are attractive for adoption in other packages.
This report is not intended as static document, but will be updated over time to reflect the agile
software development in the ECP 1.3.3.11 STMS11-PEEKS project.},
	author = {Hartwig Anzt and Erik Boman and Jack Dongarra and Goran Flegar and Mark Gates and Mike Heroux and Mark Hoemmen and Jakub Kurzak and Piotr Luszczek and Sivasankaran Rajamanickam and Stanimire Tomov and Stephen Wood and Ichitaro Yamazaki}
}
@conference {1141,
	title = {Out of Memory SVD Solver for Big Data},
	booktitle = {2017 IEEE High Performance Extreme Computing Conference (HPEC{\textquoteright}17)},
	year = {2017},
	month = {2017-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Waltham, MA},
	abstract = {Many applications {\textendash} from data compression to numerical weather prediction and information retrieval {\textendash} need to compute large dense singular value decompositions (SVD). When the problems are too large to fit into the computer{\textquoteright}s main memory, specialized out-of-core algorithms that use disk storage are required. A typical example is when trying to analyze a large data set through tools like MATLAB or Octave, but the data is just too large to be loaded. To overcome this, we designed a class of out-of-memory (OOM) algorithms to reduce, as well as overlap communication with computation. Of particular interest is OOM algorithms for matrices of size m{\texttimes}n, where m >> n or m << n, e.g., corresponding to cases of too many variables, or too many observations. To design OOM SVDs, we first study the communications cost for the SVD techniques as well as for the QR/LQ factorization followed by SVD. We present the theoretical analysis about the data movement cost and strategies to design OOM SVD algorithms. We show performance results for multicore architecture that illustrate our theoretical findings and match our performance models. Moreover, our experimental results show the feasibility and superiority of the OOM SVD.},
	author = {Azzam Haidar and Khairul Kabir and Diana Fayad and Stanimire Tomov and Jack Dongarra}
}
@conference {1097,
	title = {Resilience for Stencil Computations with Latent Errors},
	booktitle = {International Conference on Parallel Processing (ICPP)},
	year = {2017},
	month = {2017-08},
	publisher = {IEEE Computer Society Press},
	organization = {IEEE Computer Society Press},
	address = {Bristol, UK},
	abstract = {Projections and measurements of error rates in near-exascale and exascale systems suggest a dramatic growth, due to extreme scale (109,109 cores), concurrency, software complexity, and deep submicron transistor scaling. Such a growth makes resilience a critical concern, and may increase the incidence of errors that "escape," silently corrupting application state. Such errors can often be revealed by application software tests but with long latencies, and thus are known as latent errors. We explore how to efficiently recover from latent errors, with an approach called application-based focused recovery (ABFR). Specifically we present a case study of stencil computations, a widely useful computational structure, showing how ABFR focuses recovery effort where needed, using intelligent testing and pruning to reduce recovery effort, and enables recovery effort to be overlapped with application computation. We analyze and characterize the ABFR approach on stencils, creating a performance model parameterized by error rate and detection interval (latency). We compare projections from the model to experimental results with the Chombo stencil application, validating the model and showing that ABFR on stencil can achieve a significant reductions in error recovery cost (up to 400x) and recovery latency (up to 4x). Such reductions enable efficient execution at scale with high latent error rates.},
	author = {Aiman Fang and Aurelien Cavelan and Yves Robert and Andrew Chien}
}
@inproceedings {1088,
	title = {Variable-Size Batched Gauss-Huard for Block-Jacobi Preconditioning},
	journal = {International Conference on Computational Science (ICCS 2017)},
	volume = {108},
	year = {2017},
	month = {2017-06},
	pages = {1783-1792},
	publisher = {Procedia Computer Science},
	address = {Zurich, Switzerland},
	abstract = {In this work we present new kernels for the generation and application of block-Jacobi precon-ditioners that accelerate the iterative solution of sparse linear systems on graphics processing units (GPUs). Our approach departs from the conventional LU factorization and decomposes the diagonal blocks of the matrix using the Gauss-Huard method. When enhanced with column pivoting, this method is as stable as LU with partial/row pivoting. Due to extensive use of GPU registers and integration of implicit pivoting, our variable size batched Gauss-Huard implementation outperforms the batched version of LU factorization. In addition, the application kernel combines the conventional two-stage triangular solve procedure, consisting of a backward solve followed by a forward solve, into a single stage that performs both operations simultaneously.},
	doi = {https://doi.org/10.1016/j.procs.2017.05.186},
	author = {Hartwig Anzt and Jack Dongarra and Goran Flegar and Enrique S. Quintana-Orti and Andres E. Thomas}
}
@conference {1160,
	title = {Variable-Size Batched LU for Small Matrices and Its Integration into Block-Jacobi Preconditioning},
	booktitle = {46th International Conference on Parallel Processing (ICPP)},
	year = {2017},
	month = {2017-08},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Bristol, United Kingdom},
	abstract = {We present a set of new batched CUDA kernels for the LU factorization of a large collection of independent problems of different size, and the subsequent triangular solves. All kernels heavily exploit the registers of the graphics processing unit (GPU) in order to deliver high performance for small problems. The development of these kernels is motivated by the need for tackling this embarrassingly parallel scenario in the context of block-Jacobi preconditioning that is relevant for the iterative solution of sparse linear systems.},
	keywords = {graphics processing units, Jacobian matrices, Kernel, linear systems, Parallel processing, Sparse matrices},
	doi = {10.1109/ICPP.2017.18},
	url = {http://ieeexplore.ieee.org/abstract/document/8025283/?reload=true},
	author = {Hartwig Anzt and Jack Dongarra and Goran Flegar and Enrique S. Quintana-Orti}
}
@article {994,
	title = {Accelerating the Conjugate Gradient Algorithm with GPU in CFD Simulations},
	journal = {VECPAR},
	year = {2016},
	abstract = {This paper illustrates how GPU computing can be used to accelerate computational fluid dynamics (CFD) simulations. For sparse linear systems arising from finite volume discretization, we evaluate and optimize the performance of Conjugate Gradient (CG) routines designed for manycore accelerators and compare against an industrial CPU-based implementation. We also investigate how the recent advances in preconditioning, such as iterative Incomplete Cholesky (IC, as symmetric case of ILU) preconditioning, match the requirements for solving real world problems.},
	url = {http://hgpu.org/?p=16264},
	author = {Hartwig Anzt and Marc Baboulin and Jack Dongarra and Yvan Fournier and Frank Hulsemann and Amal Khabou and Yushan Wang}
}
@conference {964,
	title = {High-performance Matrix-matrix Multiplications of Very Small Matrices},
	booktitle = {22nd International European Conference on Parallel and Distributed Computing (Euro-Par{\textquoteright}16)},
	year = {2016},
	month = {2016-08},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Grenoble, France},
	abstract = {The use of the general dense matrix-matrix multiplication (GEMM) is fundamental for obtaining high performance in many scientific computing applications. GEMMs for small matrices (of sizes less than 32) however, are not sufficiently optimized in existing libraries. In this paper we consider the case of many small GEMMs on either CPU or GPU architectures. This is a case that often occurs in applications like big data analytics, machine learning, high-order FEM, and others. The GEMMs are grouped together in a single batched routine. We present specialized for these cases algorithms and optimization techniques to obtain performance that is within 90\% of the optimal. We show that these results outperform currently available state-of-the-art implementations and vendor-tuned math libraries.},
	author = {Ian Masliah and Ahmad Abdelfattah and Azzam Haidar and Stanimire Tomov and Jo{\"e}l Falcou and Jack Dongarra}
}
@techreport {929,
	title = {High-Performance Tensor Contractions for GPUs},
	journal = {University of Tennessee Computer Science Technical Report},
	number = {UT-EECS-16-738},
	year = {2016},
	month = {2016-01},
	publisher = {University of Tennessee},
	abstract = {We present a computational framework for high-performance tensor contractions on GPUs. High-performance is difficult to obtain using existing libraries, especially for many  independent contractions where each contraction is very small, e.g., sub-vector/warp in size. However, using our framework to batch contractions plus application-specifics, we demonstrate close to peak performance results. In particular, to accelerate large scale tensor-formulated high-order finite element method (FEM) simulations, which is the main focus and motivation for this work, we represent contractions as tensor index reordering plus matrix-matrix multiplications (GEMMs). This is a key factor to achieve algorithmically many-fold acceleration (vs. not using it) due to possible reuse of data loaded in fast memory. In addition to using this context knowledge, we design tensor data-structures, tensor algebra interfaces, and new tensor contraction algorithms and implementations to achieve 90+\% of a theoretically derived peak on GPUs. On a K40c GPU for contractions resulting in GEMMs on square matrices of size 8 for example, we are 2.8{\texttimes} faster than CUBLAS, and 8.5{\texttimes} faster than MKL on 16 cores of Intel Xeon ES-2670 (Sandy Bridge) 2.60GHz CPUs. Finally, we apply autotuning and code generation techniques to simplify tuning and provide an architecture-aware, user-friendly interface.},
	author = {Ahmad Abdelfattah and Marc Baboulin and Veselin Dobrev and Jack Dongarra and Christopher Earl and Jo{\"e}l Falcou and Azzam Haidar and Ian Karlin and Tzanio Kolev and Ian Masliah and Stanimire Tomov}
}
@conference {942,
	title = {High-Performance Tensor Contractions for GPUs},
	booktitle = {International Conference on Computational Science (ICCS{\textquoteright}16)},
	year = {2016},
	month = {2016-06},
	address = {San Diego, CA},
	abstract = {We present a computational framework for high-performance tensor contractions on GPUs. High-performance is difficult to obtain using existing libraries, especially for many independent contractions where each contraction is very small, e.g., sub-vector/warp in size. However, using our framework to batch contractions plus application-specifics, we demonstrate close to peak performance results. In particular, to accelerate large scale tensor-formulated high-order finite element method (FEM) simulations, which is the main focus and motivation for this work, we represent contractions as tensor index reordering plus matrix-matrix multiplications (GEMMs). This is a key factor to achieve algorithmically many-fold acceleration (vs. not using it) due to possible reuse of data loaded in fast memory. In addition to using this context knowledge, we design tensor data-structures, tensor algebra interfaces, and new tensor contraction algorithms and implementations to achieve 90+\% of a theoretically derived peak on GPUs. On a K40c GPU for contractions resulting in GEMMs on square matrices of size 8 for example, we are 2.8{\texttimes} faster than CUBLAS, and 8.5{\texttimes} faster than MKL on 16 cores of Intel Xeon E5-2670 (Sandy Bridge) 2.60GHz CPUs. Finally, we apply autotuning and code generation techniques to simplify tuning and provide an architecture-aware, user-friendly interface.},
	keywords = {Applications, Batched linear algebra, FEM, gpu, Tensor contractions, Tensor HPC},
	author = {Ahmad Abdelfattah and Marc Baboulin and Veselin Dobrev and Jack Dongarra and Christopher Earl and Jo{\"e}l Falcou and Azzam Haidar and Ian Karlin and Tzanio Kolev and Ian Masliah and Stanimire Tomov}
}
@conference {842,
	title = { A Data Flow Divide and Conquer Algorithm for Multicore Architecture},
	booktitle = {29th IEEE International Parallel \& Distributed Processing Symposium (IPDPS)},
	year = {2015},
	month = {2015-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Hyderabad, India},
	abstract = {Computing eigenpairs of a symmetric matrix is a problem arising in many industrial applications, including quantum physics and finite-elements computation for automobiles. A classical approach is to reduce the matrix to tridiagonal form before computing eigenpairs of the tridiagonal matrix. Then, a back-transformation allows one to obtain the final solution. Parallelism issues of the reduction stage have already been tackled in different shared-memory libraries. In this article, we focus on solving the tridiagonal eigenproblem, and we describe a novel implementation of the Divide and Conquer algorithm. The algorithm is expressed as a sequential task-flow, scheduled in an out-of-order fashion by a dynamic runtime which allows the programmer to play with tasks granularity. The resulting implementation is between two and five times faster than the equivalent routine from the Intel MKL library, and outperforms the best MRRR implementation for many matrices.},
	keywords = {Eigensolver, lapack, Multicore, plasma, task-based programming},
	author = {Azzam Haidar and Jakub Kurzak and Gregoire Pichon and Mathieu Faverge}
}
@conference {841,
	title = {Hierarchical DAG scheduling for Hybrid Distributed Systems},
	booktitle = {29th IEEE International Parallel \& Distributed Processing Symposium (IPDPS)},
	year = {2015},
	month = {2015-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Hyderabad, India},
	abstract = {Accelerator-enhanced computing platforms have drawn a lot of attention due to their massive peak com-putational capacity. Despite significant advances in the pro-gramming interfaces to such hybrid architectures, traditional programming paradigms struggle mapping the resulting multi-dimensional heterogeneity and the expression of algorithm parallelism, resulting in sub-optimal effective performance. Task-based programming paradigms have the capability to alleviate some of the programming challenges on distributed hybrid many-core architectures. In this paper we take this concept a step further by showing that the potential of task-based programming paradigms can be greatly increased with minimal modification of the underlying runtime combined with the right algorithmic changes. We propose two novel recursive algorithmic variants for one-sided factorizations and describe the changes to the PaRSEC task-scheduling runtime to build a framework where the task granularity is dynamically adjusted to adapt the degree of available parallelism and kernel effi-ciency according to runtime conditions. Based on an extensive set of results we show that, with one-sided factorizations, i.e. Cholesky and QR, a carefully written algorithm, supported by an adaptive tasks-based runtime, is capable of reaching a degree of performance and scalability never achieved before in distributed hybrid environments. },
	keywords = {dense linear algebra, gpu, heterogeneous architecture, PaRSEC runtime},
	author = {Wei Wu and Aurelien Bouteiller and George Bosilca and Mathieu Faverge and Jack Dongarra}
}
@article {917,
	title = {Mixing LU-QR Factorization Algorithms to Design High-Performance Dense Linear Algebra Solvers},
	journal = {Journal of Parallel and Distributed Computing},
	volume = {85},
	year = {2015},
	month = {2015-11},
	pages = {32-46},
	abstract = {This paper introduces hybrid LU{\textendash}QR algorithms for solving dense linear systems of the form Ax=b. Throughout a matrix factorization, these algorithms dynamically alternate LU with local pivoting and QR elimination steps based upon some robustness criterion. LU elimination steps can be very efficiently parallelized, and are twice as cheap in terms of floating-point operations, as QR steps. However, LU steps are not necessarily stable, while QR steps are always stable. The hybrid algorithms execute a QR step when a robustness criterion detects some risk for instability, and they execute an LU step otherwise. The choice between LU and QR steps must have a small computational overhead and must provide a satisfactory level of stability with as few QR steps as possible. In this paper, we introduce several robustness criteria and we establish upper bounds on the growth factor of the norm of the updated matrix incurred by each of these criteria. In addition, we describe the implementation of the hybrid algorithms through an extension of the PaRSEC software to allow for dynamic choices during execution. Finally, we analyze both stability and performance results compared to state-of-the-art linear solvers on parallel distributed multicore platforms. A comprehensive set of experiments shows that hybrid LU{\textendash}QR algorithms provide a continuous range of trade-offs between stability and performances.},
	keywords = {lu factorization, Numerical algorithms, QR factorization, Stability; Performance},
	doi = {doi:10.1016/j.jpdc.2015.06.007},
	author = {Mathieu Faverge and Julien Herrmann and Julien Langou and Bradley Lowery and Yves Robert and Jack Dongarra}
}
@article {699,
	title = {A Survey of Recent Developments in Parallel Implementations of Gaussian Elimination},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {27},
	year = {2015},
	month = {2015-04},
	pages = {1292-1309},
	abstract = {Gaussian elimination is a canonical linear algebra procedure for solving linear systems of equations. In the last few years, the algorithm has received a lot of attention in an attempt to improve its parallel performance. This article surveys recent developments in parallel implementations of Gaussian elimination for shared memory architecture. Five different flavors are investigated. Three of them are based on different strategies for pivoting: partial pivoting, incremental pivoting, and tournament pivoting. The fourth one replaces pivoting with the Partial Random Butterfly Transformation, and finally, an implementation without pivoting is used as a performance baseline. The technique of iterative refinement is applied to recover numerical accuracy when necessary. All parallel implementations are produced using dynamic, superscalar, runtime scheduling and tile matrix layout. Results on two multisocket multicore systems are presented. Performance and numerical accuracy is analyzed.},
	keywords = {Gaussian elimination, lu factorization, Multicore, parallel, plasma, shared memory},
	doi = {10.1002/cpe.3306},
	author = {Simplice Donfack and Jack Dongarra and Mathieu Faverge and Mark Gates and Jakub Kurzak and Piotr Luszczek and Ichitaro Yamazaki}
}
@article {1346,
	title = {Towards a High-Performance Tensor Algebra Package for Accelerators},
	year = {2015},
	month = {2015-09},
	publisher = {moky Mountains Computational Sciences and Engineering Conference (SMC15)},
	address = {Gatlinburg, TN},
	author = {Marc Baboulin and Veselin Dobrev and Jack Dongarra and Christopher Earl and Jo{\"e}l Falcou and Azzam Haidar and Ian Karlin and Tzanio Kolev and Ian Masliah and Stanimire Tomov}
}
@article {698,
	title = {Achieving numerical accuracy and high performance using recursive tile LU factorization with partial pivoting},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {26},
	year = {2014},
	month = {2014-05},
	pages = {1408-1431},
	chapter = {1408},
	abstract = {The LU factorization is an important numerical algorithm for solving systems of linear equations in science and engineering and is a characteristic of many dense linear algebra computations. For example, it has become the de facto numerical algorithm implemented within the LINPACK benchmark to rank the most powerful supercomputers in the world, collected by the TOP500 website. Multicore processors continue to present challenges to the development of fast and robust numerical software due to the increasing levels of hardware parallelism and widening gap between core and memory speeds. In this context, the difficulty in developing new algorithms for the scientific community resides in the combination of two goals: achieving high performance while maintaining the accuracy of the numerical algorithm. This paper proposes a new approach for computing the LU factorization in parallel on multicore architectures, which not only improves the overall performance but also sustains the numerical quality of the standard LU factorization algorithm with partial pivoting. While the update of the trailing submatrix is computationally intensive and highly parallel, the inherently problematic portion of the LU factorization is the panel factorization due to its memory-bound characteristic as well as the atomicity of selecting the appropriate pivots. Our approach uses a parallel fine-grained recursive formulation of the panel factorization step and implements the update of the trailing submatrix with the tile algorithm. Based on conflict-free partitioning of the data and lockless synchronization mechanisms, our implementation lets the overall computation flow naturally without contention. The dynamic runtime system called QUARK is then able to schedule tasks with heterogeneous granularities and to transparently introduce algorithmic lookahead. The performance results of our implementation are competitive compared to the currently available software packages and libraries. For example, it is up to 40\% faster when compared to the equivalent Intel MKL routine and up to threefold faster than LAPACK with multithreaded Intel MKL BLAS.},
	keywords = {factorization, parallel linear algebra, plasma, recursion, shared memory synchronization, threaded parallelism},
	doi = {10.1002/cpe.3110},
	url = {http://doi.wiley.com/10.1002/cpe.3110},
	author = {Jack Dongarra and Mathieu Faverge and Hatem Ltaeif and Piotr Luszczek}
}
@conference {813,
	title = {Designing LU-QR Hybrid Solvers for Performance and Stability},
	booktitle = {IPDPS 2014},
	year = {2014},
	month = {2014-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Phoenix, AZ},
	abstract = {This paper introduces hybrid LU-QR algorithms for solving dense linear systems of the form Ax = b. Throughout a matrix factorization, these algorithms dynamically alternate LU with local pivoting and QR elimination steps, based upon some robustness criterion. LU elimination steps can be very efficiently parallelized, and are twice as cheap in terms of operations, as QR steps. However, LU steps are not necessarily stable, while QR steps are always stable. The hybrid algorithms execute a QR step when a robustness criterion detects some risk for instability, and they execute an LU step otherwise. Ideally, the choice between LU and QR steps must have a small computational overhead and must provide a satisfactory level of stability with as few QR steps as possible. In this paper, we introduce several robustness criteria and we establish upper bounds on the growth factor of the norm of the updated matrix incurred by each of these criteria. In addition, we describe the implementation of the hybrid algorithms through an extension of the Parsec software to allow for dynamic choices during execution. Finally, we analyze both stability and performance results compared to state-of-the-art linear solvers on parallel distributed multicore platforms.},
	keywords = {plasma},
	isbn = {978-1-4799-3800-1},
	doi = {10.1109/IPDPS.2014.108},
	author = {Mathieu Faverge and Julien Herrmann and Julien Langou and Bradley Lowery and Yves Robert and Jack Dongarra}
}
@conference {709,
	title = {Taking Advantage of Hybrid Systems for Sparse Direct Solvers via Task-Based Runtimes},
	booktitle = {23rd International Heterogeneity in Computing Workshop, IPDPS 2014},
	year = {2014},
	month = {2014-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Phoenix, AZ},
	abstract = {The ongoing hardware evolution exhibits an escalation in the number, as well as in the heterogeneity, of the computing resources. The pressure to maintain reasonable levels of performance and portability, forces the application developers to leave the traditional programming paradigms and explore alternative solutions. PaStiX is a parallel sparse direct solver, based on a dynamic scheduler for modern hierarchical architectures. In this paper, we study the replacement of the highly specialized internal scheduler in PaStiX by two generic runtime frameworks: PaRSEC and StarPU. The tasks graph of the factorization step is made available to the two runtimes, providing them with the opportunity to optimize it in order to maximize the algorithm eefficiency for a predefined execution environment. A comparative study of the performance of the PaStiX solver with the three schedulers { native PaStiX, StarPU and PaRSEC schedulers { on different execution contexts is performed. The analysis highlights the similarities from a performance point of view between the different execution supports. These results demonstrate that these generic DAG-based runtimes provide a uniform and portable programming interface across heterogeneous environments, and are, therefore, a sustainable solution for hybrid environments.},
	keywords = {DAG based runtime, gpu, Multicore, Sparse linear solver},
	author = {Xavier Lacoste and Mathieu Faverge and Pierre Ramet and Samuel Thibault and George Bosilca}
}
@article {826,
	title = {Unveiling the Performance-energy Trade-off in Iterative Linear System Solvers for Multithreaded Processors},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {27},
	year = {2014},
	month = {2014-09},
	pages = {885-904},
	chapter = {885},
	abstract = {In this paper, we analyze the interactions occurring in the triangle performance-power-energy for the execution of a pivotal numerical algorithm, the iterative conjugate gradient (CG) method, on a diverse collection of parallel multithreaded architectures. This analysis is especially timely in a decade where the power wall has arisen as a major obstacle to build faster processors. Moreover, the CG method has recently been proposed as a complement to the LINPACK benchmark, as this iterative method is argued to be more archetypical of the performance of today{\textquoteright}s scientific and engineering applications. To gain insights about the benefits of hands-on optimizations we include runtime and energy efficiency results for both out-of-the-box usage relying exclusively on compiler optimizations, and implementations manually optimized for target architectures, that range from general-purpose and digital signal multicore processors to manycore graphics processing units, all representative of current multithreaded systems.},
	keywords = {CG, CPUs, energy efficiency, GPUs, low-power architectures},
	doi = {10.1002/cpe.3341},
	url = {http://dx.doi.org/10.1002/cpe.3341},
	author = {Jos{\'e} I. Aliaga and Hartwig Anzt and Maribel Castillo and Juan C. Fern{\'a}ndez and Germ{\'a}n Le{\'o}n and Joaqu{\'\i}n P{\'e}rez and Enrique S. Quintana-Orti}
}
@techreport {703,
	title = {Designing LU-QR hybrid solvers for performance and stability},
	journal = {University of Tennessee Computer Science Technical Report (also LAWN 282)},
	number = {ut-eecs-13-719},
	year = {2013},
	month = {2013-10},
	publisher = {University of Tennessee},
	author = {Mathieu Faverge and Julien Herrmann and Julien Langou and Bradley Lowery and Yves Robert and Jack Dongarra}
}
@article {752,
	title = {Hierarchical QR Factorization Algorithms for Multi-core Cluster Systems},
	journal = {Parallel Computing},
	volume = {39},
	year = {2013},
	month = {2013-05},
	pages = {212-232},
	abstract = {This paper describes a new QR factorization algorithm which is especially designed for massively parallel platforms combining parallel distributed nodes, where a node is a multi-core processor. These platforms represent the present and the foreseeable future of high-performance computing. Our new QR factorization algorithm falls in the category of the tile algorithms which naturally enables good data locality for the sequential kernels executed by the cores (high sequential performance), low number of messages in a parallel distributed setting (small latency term), and fine granularity (high parallelism). Each tile algorithm is uniquely characterized by its sequence of reduction trees. In the context of a cluster of nodes, in order to minimize the number of inter-processor communications (aka, {\textquoteleft}{\textquoteleft}communication-avoiding{\textquoteright}{\textquoteright}), it is natural to consider hierarchical trees composed of an {\textquoteleft}{\textquoteleft}inter-node{\textquoteright}{\textquoteright} tree which acts on top of {\textquoteleft}{\textquoteleft}intra-node{\textquoteright}{\textquoteright} trees. At the intra-node level, we propose a hierarchical tree made of three levels: (0) {\textquoteleft}{\textquoteleft}TS level{\textquoteright}{\textquoteright} for cache-friendliness, (1) {\textquoteleft}{\textquoteleft}low-level{\textquoteright}{\textquoteright} for decoupled highly parallel inter-node reductions, (2) {\textquoteleft}{\textquoteleft}domino level{\textquoteright}{\textquoteright} to efficiently resolve interactions between local reductions and global reductions. Our hierarchical algorithm and its implementation are flexible and modular, and can accommodate several kernel types, different distribution layouts, and a variety of reduction trees at all levels, both inter-node and intra-node. Numerical experiments on a cluster of multi-core nodes (i) confirm that each of the four levels of our hierarchical tree contributes to build up performance and (ii) build insights on how these levels influence performance and interact within each other. Our implementation of the new algorithm with the DAGUE scheduling tool significantly outperforms currently available QR factorization software for all matrix shapes, thereby bringing a new advance in numerical linear algebra for petascale and exascale platforms.},
	keywords = {Cluster, Distributed memory, Hierarchical architecture, multi-core, numerical linear algebra, QR factorization},
	author = {Jack Dongarra and Mathieu Faverge and Thomas Herault and Mathias Jacquelin and Julien Langou and Yves Robert}
}
@techreport {682,
	title = {Implementing a systolic algorithm for QR factorization on multicore clusters with PaRSEC},
	journal = {Lawn 277},
	number = {UT-CS-13-709},
	year = {2013},
	month = {2013-05},
	abstract = {This article introduces a new systolic algorithm for QR factorization, and its implementation on a supercomputing cluster of multicore nodes. The algorithm targets a virtual 3D-array and requires only local communications. The implementation of the algorithm uses threads at the node level, and MPI for inter-node communications. The complexity of the implementation is addressed with the PaRSEC software, which takes as input a parametrized dependence graph, which is derived from the algorithm, and only requires the user to decide, at the high-level, the allocation of tasks to nodes. We show that the new algorithm exhibits competitive performance with state-of-the-art QR routines on a supercomputer called Kraken, which shows that high-level programming environments, such as PaRSEC, provide a viable alternative to enhance the production of quality software on complex and hierarchical architectures},
	author = {Guillaume Aupy and Mathieu Faverge and Yves Robert and Jakub Kurzak and Piotr Luszczek and Jack Dongarra}
}
@article {icl:704,
	title = {Multithreading in the PLASMA Library},
	journal = {Multi and Many-Core Processing: Architecture, Programming, Algorithms, \& Applications},
	year = {2013},
	month = {2013-00},
	publisher = {Taylor \& Francis},
	keywords = {plasma},
	author = {Jakub Kurzak and Piotr Luszczek and Asim YarKhan and Mathieu Faverge and Julien Langou and Henricus Bouwmeester and Jack Dongarra},
	editor = {Mohamed Ahmed and Reda Ammar and Sanguthevar Rajasekaran}
}
@conference {763,
	title = {A Parallel Solver for Incompressible Fluid Flows},
	booktitle = {International Conference on Computational Science (ICCS 2013)},
	year = {2013},
	month = {2013-06},
	publisher = {Elsevier B.V.},
	organization = {Elsevier B.V.},
	address = {Barcelona, Spain},
	abstract = {The Navier-Stokes equations describe a large class of fluid flows but are difficult to solve analytically because of their nonlin- earity. We present in this paper a parallel solver for the 3-D Navier-Stokes equations of incompressible unsteady flows with constant coefficients, discretized by the finite difference method. We apply the prediction-projection method which transforms the Navier-Stokes equations into three Helmholtz equations and one Poisson equation. For each Helmholtz system, we apply the Alternating Direction Implicit (ADI) method resulting in three tridiagonal systems. The Poisson equation is solved using partial diagonalization which transforms the Laplacian operator into a tridiagonal one. We describe an implementation based on MPI where the computations are performed on each subdomain and information is exchanged on the interfaces, and where the tridiagonal system solutions are accelerated using vectorization techniques. We present performance results on a current multicore system.},
	keywords = {ADI, Navier-Stokes equations, Parallel computing, Partial diagonalization, Prediction-projection, SIMD},
	doi = {DOI: 10.1016/j.procs.2013.05.207},
	author = {Yushan Wang and Marc Baboulin and Jo{\"e}l Falcou and Yann Fraigneau and Olivier Le Ma{\^\i}tre}
}
@article {749,
	title = {PaRSEC: Exploiting Heterogeneity to Enhance Scalability},
	journal = {IEEE Computing in Science and Engineering},
	volume = {15},
	year = {2013},
	month = {2013-11},
	pages = {36-45},
	abstract = {New high-performance computing system designs with steeply escalating processor and core counts, burgeoning heterogeneity and accelerators, and increasingly unpredictable memory access times call for dramatically new programming paradigms. These new approaches must react and adapt quickly to unexpected contentions and delays, and they must provide the execution environment with sufficient intelligence and flexibility to rearrange the execution to improve resource utilization.},
	doi = {10.1109/MCSE.2013.98},
	author = {George Bosilca and Aurelien Bouteiller and Anthony Danalis and Mathieu Faverge and Thomas Herault and Jack Dongarra}
}
@conference {761,
	title = {Standards for Graph Algorithm Primitives},
	booktitle = {17th IEEE High Performance Extreme Computing Conference (HPEC {\textquoteright}13)},
	year = {2013},
	month = {2013-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Waltham, MA},
	abstract = {It is our view that the state of the art in constructing a large collection of graph algorithms in terms of linear algebraic operations is mature enough to support the emergence of a standard set of primitive building blocks. This paper is a position paper defining the problem and announcing our intention to launch an open effort to define this standard.},
	keywords = {algorithms, graphs, linear algebra, software standards},
	doi = {10.1109/HPEC.2013.6670338},
	author = {Tim Mattson and David Bader and Jon Berry and Aydin Buluc and Jack Dongarra and Christos Faloutsos and John Feo and John Gilbert and Joseph Gonzalez and Bruce Hendrickson and Jeremy Kepner and Charles Lieserson and Andrew Lumsdaine and David Padua and Steve W. Poole and Steve Reinhardt and Mike Stonebraker and Steve Wallach and Andrew Yoo}
}
@techreport {688,
	title = {On Algorithmic Variants of Parallel Gaussian Elimination: Comparison of Implementations in Terms of Performance and Numerical Properties},
	journal = {University of Tennessee Computer Science Technical Report},
	number = {UT-CS-13-715},
	year = {2012},
	month = {2013-07},
	abstract = {Gaussian elimination is a canonical linear algebra procedure for solving linear systems of equations. In the last few years, the algorithm received a lot of attention in an attempt to improve its parallel performance. This article surveys recent developments in parallel implementations of the Gaussian elimination. Five different flavors are investigated. Three of them are based on different strategies for pivoting: partial pivoting, incremental pivoting, and tournament pivoting. The fourth one replaces pivoting with the Random Butterfly Transformation, and finally, an implementation without pivoting is used as a performance baseline. The technique of iterative refinement is applied to recover numerical accuracy when necessary. All parallel implementations are produced using dynamic, superscalar, runtime scheduling and tile matrix layout. Results on two multi-socket multicore systems are presented. Performance and numerical accuracy is analyzed.},
	author = {Simplice Donfack and Jack Dongarra and Mathieu Faverge and Mark Gates and Jakub Kurzak and Piotr Luszczek and Ichitaro Yamazaki}
}
@inproceedings {icl:687,
	title = {Hierarchical QR Factorization Algorithms for Multi-Core Cluster Systems},
	journal = {IPDPS 2012, the 26th IEEE International Parallel and Distributed Processing Symposium},
	year = {2012},
	month = {2012-05},
	publisher = {IEEE Computer Society Press},
	address = {Shanghai, China},
	author = {Jack Dongarra and Mathieu Faverge and Thomas Herault and Julien Langou and Yves Robert}
}
@inproceedings {icl:665,
	title = {Programming the LU Factorization for a Multicore System with Accelerators},
	journal = {Proceedings of VECPAR{\textquoteright}12},
	year = {2012},
	month = {2012-04},
	address = {Kobe, Japan},
	keywords = {plasma, quark},
	author = {Jakub Kurzak and Piotr Luszczek and Mathieu Faverge and Jack Dongarra}
}
@techreport {icl:660,
	title = {Achieving Numerical Accuracy and High Performance using Recursive Tile LU Factorization},
	journal = {University of Tennessee Computer Science Technical Report (also as a LAWN)},
	number = {ICL-UT-11-08},
	year = {2011},
	month = {2011-09},
	keywords = {plasma, quark},
	author = {Jack Dongarra and Mathieu Faverge and Hatem Ltaeif and Piotr Luszczek}
}
@inproceedings {icl:611,
	title = {Exploiting Fine-Grain Parallelism in Recursive LU Factorization},
	journal = {Proceedings of PARCO{\textquoteright}11},
	number = {ICL-UT-11-04},
	year = {2011},
	month = {2011-04},
	address = {Gent, Belgium},
	keywords = {plasma},
	author = {Jack Dongarra and Mathieu Faverge and Hatem Ltaeif and Piotr Luszczek}
}
@inproceedings {icl:676,
	title = {Flexible Development of Dense Linear Algebra Algorithms on Massively Parallel Architectures with DPLASMA},
	journal = {Proceedings of the Workshops of the 25th IEEE International Symposium on Parallel and Distributed Processing (IPDPS 2011 Workshops)},
	year = {2011},
	month = {2011-05},
	pages = {1432-1441},
	publisher = {IEEE},
	address = {Anchorage, Alaska, USA},
	keywords = {dague, dplasma, parsec},
	author = {George Bosilca and Aurelien Bouteiller and Anthony Danalis and Mathieu Faverge and Azzam Haidar and Thomas Herault and Jakub Kurzak and Julien Langou and Pierre Lemariner and Hatem Ltaeif and Piotr Luszczek and Asim YarKhan and Jack Dongarra}
}
@techreport {icl:645,
	title = {Hierarchical QR Factorization Algorithms for Multi-Core Cluster Systems},
	journal = {University of Tennessee Computer Science Technical Report (also Lawn 257)},
	number = {UT-CS-11-684},
	year = {2011},
	month = {2011-10},
	keywords = {magma, plasma},
	author = {Jack Dongarra and Mathieu Faverge and Thomas Herault and Julien Langou and Yves Robert}
}
@inproceedings {icl:658,
	title = {High Performance Matrix Inversion Based on LU Factorization for Multicore Architectures},
	journal = {Proceedings of MTAGS11},
	year = {2011},
	month = {2011-11},
	address = {Seattle, WA},
	author = {Jack Dongarra and Mathieu Faverge and Hatem Ltaeif and Piotr Luszczek}
}
@article {icl:643,
	title = {The International Exascale Software Project Roadmap},
	journal = {International Journal of High Performance Computing},
	volume = {25},
	number = {1},
	year = {2011},
	month = {2011-01},
	pages = {3-60},
	abstract = {Over the last 20 years, the open-source community has provided more and more software on which the world{\textquoteright}s high-performance computing systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. However, although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual petascale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/ exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and graphics processing units. This report describes the work of the community to prepare for the challenges of exascale computing, ultimately combing their efforts in a coordinated International Exascale Software Project.},
	doi = {https://doi.org/10.1177/1094342010391989},
	author = {Jack Dongarra and Pete Beckman and Terry Moore and Patrick Aerts and Giovanni Aloisio and Jean-Claude Andre and David Barkai and Jean-Yves Berthou and Taisuke Boku and Bertrand Braunschweig and Franck Cappello and Barbara Chapman and Xuebin Chi and Alok Choudhary and Sudip Dosanjh and Thom Dunning and Sandro Fiore and Al Geist and Bill Gropp and Robert Harrison and Mark Hereld and Michael Heroux and Adolfy Hoisie and Koh Hotta and Zhong Jin and Yutaka Ishikawa and Fred Johnson and Sanjay Kale and Richard Kenway and David Keyes and Bill Kramer and Jesus Labarta and Alain Lichnewsky and Thomas Lippert and Bob Lucas and Barney MacCabe and Satoshi Matsuoka and Paul Messina and Peter Michielse and Bernd Mohr and Matthias S. Mueller and Wolfgang E. Nagel and Hiroshi Nakashima and Michael E. Papka and Dan Reed and Mitsuhisa Sato and Ed Seidel and John Shalf and David Skinner and Marc Snir and Thomas Sterling and Rick Stevens and Fred Streitz and Bob Sugar and Shinji Sumimoto and William Tang and John Taylor and Rajeev Thakur and Anne Trefethen and Mateo Valero and Aad van der Steen and Jeffrey Vetter and Peg Williams and Robert Wisniewski and Kathy Yelick}
}
@article {icl:599,
	title = {LU Factorization for Accelerator-Based Systems},
	journal = {IEEE/ACS AICCSA 2011},
	year = {2011},
	month = {2011-12},
	address = {Sharm-El-Sheikh, Egypt},
	keywords = {magma, morse},
	author = {Emmanuel Agullo and Cedric Augonnet and Jack Dongarra and Mathieu Faverge and Julien Langou and Hatem Ltaeif and Stanimire Tomov}
}
@inproceedings {icl:659,
	title = {An open-source tool-chain for performance analysis},
	journal = {Parallel Tools Workshop},
	year = {2011},
	month = {2011-09},
	address = {Dresden, Germany},
	author = {Kevin Coulomb and Augustin Degomme and Mathieu Faverge and Francois Trahay}
}
@techreport {icl:610,
	title = {Towards a Parallel Tile LDL Factorization for Multicore Architectures},
	journal = {ICL Technical Report},
	number = {ICL-UT-11-03},
	year = {2011},
	month = {2011-04},
	address = {Seattle, WA},
	keywords = {plasma, quark},
	author = {Dulceneia Becker and Mathieu Faverge and Jack Dongarra}
}
@techreport {icl:563,
	title = {Distributed Dense Numerical Linear Algebra Algorithms on Massively Parallel Architectures: DPLASMA},
	journal = {University of Tennessee Computer Science Technical Report, UT-CS-10-660},
	year = {2010},
	month = {2010-09},
	keywords = {dague, dplasma, parsec, plasma},
	author = {George Bosilca and Aurelien Bouteiller and Anthony Danalis and Mathieu Faverge and Azzam Haidar and Thomas Herault and Jakub Kurzak and Julien Langou and Pierre Lemariner and Hatem Ltaeif and Piotr Luszczek and Asim YarKhan and Jack Dongarra}
}
@techreport {icl:529,
	title = {Distributed-Memory Task Execution and Dependence Tracking within DAGuE and the DPLASMA Project},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-10-02},
	year = {2010},
	month = {2010-00},
	keywords = {dague, plasma},
	author = {George Bosilca and Aurelien Bouteiller and Anthony Danalis and Mathieu Faverge and Azzam Haidar and Thomas Herault and Jakub Kurzak and Julien Langou and Pierre Lemariner and Hatem Ltaeif and Piotr Luszczek and Asim YarKhan and Jack Dongarra}
}
@techreport {icl:600,
	title = {EZTrace: a generic framework for performance analysis},
	journal = {ICL Technical Report},
	number = {ICL-UT-11-01},
	year = {2010},
	month = {2010-12},
	author = {Jack Dongarra and Mathieu Faverge and Yutaka Ishikawa and Raymond Namyst and Fran{\c c}ois Rue and Francois Trahay}
}
@inproceedings {icl:577,
	title = {QR Factorization on a Multicore Node Enhanced with Multiple GPU Accelerators},
	journal = {Proceedings of IPDPS 2011},
	number = {ICL-UT-10-04},
	year = {2010},
	month = {2010-10},
	address = {Anchorage, AK},
	keywords = {magma, morse, plasma},
	author = {Emmanuel Agullo and Cedric Augonnet and Jack Dongarra and Mathieu Faverge and Hatem Ltaeif and Samuel Thibault and Stanimire Tomov}
}
@article {icl:567,
	title = {Self-Healing Network for Scalable Fault-Tolerant Runtime Environments},
	journal = {Future Generation Computer Systems},
	volume = {26},
	number = {3},
	year = {2010},
	month = {2010-03},
	pages = {479-485},
	author = {Thara Angskun and Graham Fagg and George Bosilca and Jelena Pjesivac{\textendash}Grbovic and Jack Dongarra}
}
@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:620,
	title = {Using MAGMA with PGI Fortran},
	journal = {PGI Insider},
	year = {2010},
	month = {2010-11},
	keywords = {magma},
	author = {Stanimire Tomov and Mathieu Faverge and Piotr Luszczek and Jack Dongarra}
}
@article {icl:568,
	title = {Accelerating Time-To-Solution for Computational Science and Engineering},
	journal = {SciDAC Review},
	year = {2009},
	month = {2009-00},
	author = {James Demmel and Jack Dongarra and Armando Fox and Sam Williams and Vasily Volkov and Katherine Yelick}
}
@article {icl:596,
	title = {Capturing and Analyzing the Execution Control Flow of OpenMP Applications},
	journal = {International Journal of Parallel Programming},
	volume = {37},
	number = {3},
	year = {2009},
	month = {2009-00},
	pages = {266-276},
	author = {Karl F{\"u}rlinger and Shirley Moore}
}
@article {icl:595,
	title = {Recording the Control Flow of Parallel Applications to Determine Iterative and Phase-Based Behavior},
	journal = {Future Generation Computing Systems},
	volume = {26},
	year = {2009},
	month = {2009-00},
	pages = {162-166},
	author = {Karl F{\"u}rlinger and Shirley Moore}
}
@inproceedings {icl:429,
	title = {Detection and Analysis of Iterative Behavior in Parallel Applications},
	journal = {Proceedings of the 2008 International Conference on Computational Science (ICCS 2008)},
	volume = {5103},
	year = {2008},
	month = {2008-01},
	pages = {261-267},
	address = {Krakow, Poland},
	keywords = {point},
	author = {Karl F{\"u}rlinger and Shirley Moore}
}
@inproceedings {icl:459,
	title = {OpenMP-centric Performance Analysis of Hybrid Applications},
	journal = {Proc. 2008 IEEE International Conference on Cluster Computing (CLUSTER 2008)},
	year = {2008},
	month = {2008-01},
	address = {Tsukuba, Japan},
	author = {Karl F{\"u}rlinger and Shirley Moore}
}
@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}
}
@inproceedings {icl:460,
	title = {Visualizing the Program Execution Control Flow of OpenMP Applications},
	journal = {Proc. 4th International Workshop on OpenMP (IWOMP 2008)},
	year = {2008},
	month = {2008-01},
	pages = {181-190},
	publisher = {Lecture Notes in Computer Science 5004},
	address = {West Lafayette, Indiana},
	author = {Karl F{\"u}rlinger and Shirley Moore}
}
@inproceedings {icl:383,
	title = {Continuous Runtime Profiling of OpenMP Applications},
	journal = {Proceedings of the 2007 Conference on Parallel Computing (PARCO 2007)},
	year = {2007},
	month = {2007-01},
	address = {Juelich and Aachen, Germany},
	keywords = {kojak},
	author = {Karl F{\"u}rlinger and Shirley Moore}
}
@article {icl:357,
	title = {Decision Trees and MPI Collective Algorithm Selection Problem},
	journal = {Euro-Par 2007},
	year = {2007},
	month = {2007-08},
	pages = {105{\textendash}115},
	publisher = {Springer},
	address = {Rennes, France},
	keywords = {ftmpi},
	author = {Jelena Pjesivac{\textendash}Grbovic and George Bosilca and Graham Fagg and Thara Angskun and Jack Dongarra}
}
@article {,
	title = {The Impact of Multicore on Computational Science Software},
	journal = {CTWatch Quarterly},
	volume = {3},
	year = {2007},
	month = {2007-02},
	author = {Jack Dongarra and Dennis Gannon and Geoffrey Fox and Ken Kennedy}
}
@article {icl:356,
	title = {MPI Collective Algorithm Selection and Quadtree Encoding},
	journal = {Parallel Computing (Special Edition: EuroPVM/MPI 2006)},
	year = {2007},
	month = {2007-00},
	publisher = {Elsevier},
	keywords = {ftmpi},
	author = {Jelena Pjesivac{\textendash}Grbovic and George Bosilca and Graham Fagg and Thara Angskun and Jack Dongarra}
}
@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}
}
@article {icl:358,
	title = {Performance Analysis of MPI Collective Operations},
	journal = {Cluster computing},
	volume = {10},
	number = {2},
	year = {2007},
	month = {2007-06},
	pages = {127-143},
	publisher = {Springer Netherlands},
	keywords = {ftmpi},
	author = {Jelena Pjesivac{\textendash}Grbovic and Thara Angskun and George Bosilca and Graham Fagg and Edgar Gabriel and Jack Dongarra}
}
@inproceedings {icl:354,
	title = {Reliability Analysis of Self-Healing Network using Discrete-Event Simulation},
	journal = {Proceedings of Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid {\textquoteright}07)},
	year = {2007},
	month = {2007-05},
	pages = {437-444},
	publisher = {IEEE Computer Society},
	keywords = {ftmpi},
	author = {Thara Angskun and George Bosilca and Graham Fagg and Jelena Pjesivac{\textendash}Grbovic and Jack Dongarra}
}
@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}
}
@inproceedings {icl:393,
	title = {Self Adapting Application Level Fault Tolerance for Parallel and Distributed Computing},
	journal = {Proceedings of Workshop on Self Adapting Application Level Fault Tolerance for Parallel and Distributed Computing at IPDPS},
	year = {2007},
	month = {2007-03},
	pages = {1-8},
	author = {Zizhong Chen and Ming Yang and Guillermo Francia III and Jack Dongarra}
}
@article {icl:385,
	title = {Specification and detection of performance problems with ASL},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {19},
	number = {11},
	year = {2007},
	month = {2007-01},
	pages = {1451-1464},
	publisher = {John Wiley and Sons Ltd.},
	author = {Michael Gerndt and Karl F{\"u}rlinger}
}
@inproceedings {icl:382,
	title = {On Using Incremental Profiling for the Performance Analysis of Shared Memory Parallel Applications},
	journal = {Proceedings of the 13th International Euro-Par Conference on Parallel Processing (Euro-Par {\textquoteright}07)},
	year = {2007},
	month = {2007-01},
	publisher = {Springer LNCS},
	address = {Rennes, France},
	keywords = {kojak},
	author = {Karl F{\"u}rlinger and Jack Dongarra and Michael Gerndt}
}
@article {icl:333,
	title = {Application of Machine Learning to the Selection of Sparse Linear Solvers},
	journal = {International Journal of High Performance Computing Applications (submitted)},
	year = {2006},
	month = {2006-00},
	keywords = {salsa, sans},
	author = {Sanjukta Bhowmick and Victor Eijkhout and Yoav Freund and Erika Fuentes and David Keyes}
}
@article {icl:315,
	title = {Flexible collective communication tuning architecture applied to Open MPI},
	journal = {2006 Euro PVM/MPI (submitted)},
	year = {2006},
	month = {2006-01},
	address = {Bonn, Germany},
	keywords = {ftmpi},
	author = {Graham Fagg and Jelena Pjesivac{\textendash}Grbovic and George Bosilca and Thara Angskun and Jack Dongarra}
}
@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:650,
	title = {Implementation and Usage of the PERUSE-Interface in Open MPI},
	journal = {Euro PVM/MPI 2006},
	year = {2006},
	month = {2006-09},
	address = {Bonn, Germany},
	author = {Rainer Keller and George Bosilca and Graham Fagg and Michael Resch and Jack Dongarra}
}
@inproceedings {icl:309,
	title = {Large Event Traces in Parallel Performance Analysis},
	journal = {8th Workshop {\textquoteright}Parallel Systems and Algorithms{\textquoteright} (PASA), Lecture Notes in Informatics},
	number = {ICL-UT-06-08},
	year = {2006},
	month = {2006-03},
	publisher = {Gesellschaft f{\"u}r Informatik},
	address = {Frankfurt/Main, Germany},
	keywords = {kojak},
	author = {Felix Wolf and Felix Freitag and Bernd Mohr and Shirley Moore and Brian Wylie}
}
@techreport {icl:314,
	title = {MPI Collective Algorithm Selection and Quadtree Encoding},
	journal = {ICL Technical Report},
	number = {ICL-UT-06-11},
	year = {2006},
	month = {2006-00},
	keywords = {ftmpi},
	author = {Jelena Pjesivac{\textendash}Grbovic and Graham Fagg and Thara Angskun and George Bosilca and Jack Dongarra}
}
@article {icl:323,
	title = {MPI Collective Algorithm Selection and Quadtree Encoding},
	journal = {Lecture Notes in Computer Science},
	volume = {4192},
	number = {ICL-UT-06-13},
	year = {2006},
	month = {2006-09},
	pages = {40-48},
	publisher = {Springer Berlin / Heidelberg},
	keywords = {ftmpi},
	author = {Jelena Pjesivac{\textendash}Grbovic and Graham Fagg and Thara Angskun and George Bosilca and Jack Dongarra}
}
@article {icl:327,
	title = {Predicting the electronic properties of 3D, million-atom semiconductor nanostructure architectures},
	journal = {J. Phys.: Conf. Ser. 46},
	volume = {:101088/1742-6596/46/1/040},
	year = {2006},
	month = {2006-01},
	pages = {292-298},
	keywords = {DOE_NANO},
	author = {Alex Zunger and Alberto Franceschetti and Gabriel Bester and Wesley B. Jones and Kwiseon Kim and Peter A. Graf and Lin-Wang Wang and Andrew Canning and Osni Marques and Christof Voemel and Jack Dongarra and Julien Langou and Stanimire Tomov}
}
@inproceedings {icl:310,
	title = {Proposal of MPI operation level Checkpoint/Rollback and one implementation},
	journal = {Proceedings of IEEE CCGrid 2006},
	year = {2006},
	month = {2006-01},
	publisher = {IEEE Computer Society},
	keywords = {HARNESS/FT-PI},
	author = {Yuan Tang and Graham Fagg and Jack Dongarra}
}
@article {icl:316,
	title = {Scalable Fault Tolerant Protocol for Parallel Runtime Environments},
	journal = {2006 Euro PVM/MPI},
	number = {ICL-UT-06-12},
	year = {2006},
	month = {2006-00},
	address = {Bonn, Germany},
	keywords = {ftmpi},
	author = {Thara Angskun and Graham Fagg and George Bosilca and Jelena Pjesivac{\textendash}Grbovic and Jack Dongarra}
}
@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:330,
	title = {Self-Healing Network for Scalable Fault Tolerant Runtime Environments},
	journal = {DAPSYS 2006, 6th Austrian-Hungarian Workshop on Distributed and Parallel Systems},
	year = {2006},
	month = {2006-01},
	address = {Innsbruck, Austria},
	author = {Thara Angskun and Graham Fagg and George Bosilca and Jelena Pjesivac{\textendash}Grbovic and Jack Dongarra}
}
@article {icl:277,
	title = {The Component Structure of a Self-Adapting Numerical Software System},
	journal = {International Journal of Parallel Programming},
	volume = {33},
	number = {2},
	year = {2005},
	month = {2005-06},
	keywords = {salsa, sans},
	author = {Victor Eijkhout and Erika Fuentes and Thomas Eidson and Jack Dongarra}
}
@inproceedings {icl:293,
	title = {Dynamic Process Management for Pipelined Applications},
	journal = {Proceedings of DoD HPCMP UGC 2005 (to appear)},
	year = {2005},
	month = {2005-01},
	publisher = {IEEE},
	address = {Nashville, TN},
	author = {David Cronk and Graham Fagg and Susan Emeny and Scott Tucker}
}
@inproceedings {icl:265,
	title = {Fault Tolerant High Performance Computing by a Coding Approach},
	journal = {Proceedings of ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (to appear)},
	year = {2005},
	month = {2005-01},
	address = {Chicago, Illinois},
	keywords = {ftmpi, grads, lacsi, sans},
	author = {Zizhong Chen and Graham Fagg and Edgar Gabriel and Julien Langou and Thara Angskun and George Bosilca and Jack Dongarra}
}
@inproceedings {icl:280,
	title = {Hash Functions for Datatype Signatures in MPI},
	journal = {Proceedings of 12th European Parallel Virtual Machine and Message Passing Interface Conference - Euro PVM/MPI},
	volume = {3666},
	year = {2005},
	month = {2005-09},
	pages = {76-83},
	publisher = {Springer-Verlag Berlin},
	address = {Sorrento (Naples), Italy},
	keywords = {ftmpi},
	author = {George Bosilca and Jack Dongarra and Graham Fagg and Julien Langou},
	editor = {Beniamino Di Martino}
}
@article {,
	title = {NanoPSE: A Nanoscience Problem Solving Environment for Atomistic Electronic Structure of Semiconductor Nanostructures},
	journal = {Journal of Physics: Conference Series},
	year = {2005},
	month = {2005-06},
	pages = {277-282},
	abstract = {Researchers at the National Renewable Energy Laboratory and their collaborators have developed over the past ~10 years a set of algorithms for an atomistic description of the electronic structure of nanostructures, based on plane-wave pseudopotentials and configuration interaction. The present contribution describes the first step in assembling these various codes into a single, portable, integrated set of software packages. This package is part of an ongoing research project in the development stage. Components of NanoPSE include codes for atomistic nanostructure generation and passivation, valence force field model for atomic relaxation, code for potential field generation, empirical pseudopotential method solver, strained linear combination of bulk bands method solver, configuration interaction solver for excited states, selection of linear algebra methods, and several inverse band structure solvers. Although not available for general distribution at this time as it is being developed and tested, the design goal of the NanoPSE software is to provide a software context for collaboration. The software package is enabled by fcdev, an integrated collection of best practice GNU software for open source development and distribution augmented to better support FORTRAN.},
	doi = {https://doi.org/10.1088/1742-6596/16/1/038},
	url = {https://iopscience.iop.org/article/10.1088/1742-6596/16/1/038/meta},
	author = {Wesley B. Jones and Gabriel Bester and Andrew Canning and Alberto Franceschetti and Peter A. Graf and Kwiseon Kim and Julien Langou and Lin-Wang Wang and Jack Dongarra and Alex Zunger}
}
@inproceedings {icl:249,
	title = {Performance Analysis of MPI Collective Operations},
	journal = {4th International Workshop on Performance Modeling, Evaluation, and Optmization of Parallel and Distributed Systems (PMEO-PDS {\textquoteright}05)},
	year = {2005},
	month = {2005-04},
	address = {Denver, Colorado},
	keywords = {ftmpi},
	author = {Jelena Pjesivac{\textendash}Grbovic and Thara Angskun and George Bosilca and Graham Fagg and Edgar Gabriel and Jack Dongarra}
}
@article {icl:306,
	title = {Performance Analysis of MPI Collective Operations},
	journal = {Cluster Computing Journal (to appear)},
	year = {2005},
	month = {2005-01},
	keywords = {ftmpi},
	author = {Jelena Pjesivac{\textendash}Grbovic and Thara Angskun and George Bosilca and Graham Fagg and Edgar Gabriel and Jack Dongarra}
}
@inproceedings {icl:279,
	title = {Scalable Fault Tolerant MPI: Extending the Recovery Algorithm},
	journal = {Proceedings of 12th European Parallel Virtual Machine and Message Passing Interface Conference - Euro PVM/MPI},
	volume = {3666},
	year = {2005},
	month = {2005-09},
	pages = {67},
	publisher = {Springer-Verlag Berlin},
	address = {Sorrento (Naples) , Italy},
	keywords = {ftmpi},
	author = {Graham Fagg and Thara Angskun and George Bosilca and Jelena Pjesivac{\textendash}Grbovic and Jack Dongarra},
	editor = {Beniamino Di Martino}
}
@techreport {icl:261,
	title = {Towards an Accurate Model for Collective Communications},
	journal = {ICL Technical Report},
	number = {ICL-UT-05-03},
	year = {2005},
	month = {2005-01},
	author = {Sathish Vadhiyar and Graham Fagg and Jack Dongarra}
}
@techreport {icl:231,
	title = {An Asynchronous Algorithm on NetSolve Global Computing System},
	journal = {PRiSM - Laboratoire de recherche en informatique, Universit{\'e} de Versailles St-Quentin Technical Report},
	year = {2004},
	month = {2004-03},
	keywords = {netsolve},
	author = {Nahid Emad and S. A. Shahzadeh Fazeli and Jack Dongarra}
}
@article {icl:241,
	title = {Building and using a Fault Tolerant MPI implementation},
	journal = {International Journal of High Performance Applications and Supercomputing (to appear)},
	year = {2004},
	month = {2004-00},
	keywords = {ftmpi, lacsi, sans},
	author = {Graham Fagg and Jack Dongarra}
}
@article {icl:236,
	title = {Cray X1 Evaluation Status Report},
	journal = {Oak Ridge National Laboratory Report},
	volume = {/-2004/13},
	year = {2004},
	month = {2004-01},
	author = {Pratul Agarwal and R. A. Alexander and E. Apra and Satish Balay and Arthur S. Bland and James Colgan and Eduardo D{\textquoteright}Azevedo and Jack Dongarra and Tom Dunigan and Mark Fahey and Al Geist and M. Gordon and Robert Harrison and Dinesh Kaushik and M. Krishnakumar and Piotr Luszczek and Tony Mezzacapa and Jeff Nichols and Jarek Nieplocha and Leonid Oliker and T. Packwood and M. Pindzola and Thomas C. Schulthess and Jeffrey Vetter and James B White and T. Windus and Patrick H. Worley and Thomas Zacharia}
}
@inproceedings {icl:230,
	title = {Extending the MPI Specification for Process Fault Tolerance on High Performance Computing Systems},
	journal = {Proceedings of ISC2004 (to appear)},
	year = {2004},
	month = {2004-06},
	address = {Heidelberg, Germany},
	keywords = {ftmpi, lacsi},
	author = {Graham Fagg and Edgar Gabriel and George Bosilca and Thara Angskun and Zizhong Chen and Jelena Pjesivac{\textendash}Grbovic and Kevin London and Jack Dongarra}
}
@techreport {icl:252,
	title = {Performance Optimization and Modeling of Blocked Sparse Kernels},
	journal = {ICL Technical Report},
	number = {ICL-UT-04-05},
	year = {2004},
	month = {2004-00},
	keywords = {sans},
	author = {Alfredo Buttari and Victor Eijkhout and Julien Langou and Salvatore Filippone}
}
@article {icl:240,
	title = {Process Fault-Tolerance: Semantics, Design and Applications for High Performance Computing},
	journal = {International Journal for High Performance Applications and Supercomputing (to appear)},
	year = {2004},
	month = {2004-04},
	keywords = {ftmpi, lacsi},
	author = {Graham Fagg and Edgar Gabriel and Zizhong Chen and Thara Angskun and George Bosilca and Jelena Pjesivac{\textendash}Grbovic and Jack Dongarra}
}
@inproceedings {icl:238,
	title = {Self Adapting Linear Algebra Algorithms and Software},
	journal = {IEEE Proceedings (to appear)},
	year = {2004},
	month = {2004-00},
	keywords = {salsa, sans},
	author = {James Demmel and Jack Dongarra and Victor Eijkhout and Erika Fuentes and Antoine Petitet and Rich Vuduc and Clint Whaley and Katherine Yelick}
}
@article {icl:167,
	title = {Towards an Accurate Model for Collective Communications},
	journal = {International Journal of High Performance Applications, Special Issue: Automatic Performance Tuning},
	volume = {18},
	number = {1},
	year = {2004},
	month = {2004-01},
	pages = {159-167},
	keywords = {lacsi},
	author = {Sathish Vadhiyar and Graham Fagg 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:145,
	title = {Evaluating The Performance Of MPI-2 Dynamic Communicators And One-Sided Communication},
	journal = {Lecture Notes in Computer Science, Recent Advances in Parallel Virtual Machine and Message Passing Interface, 10th European PVM/MPI User{\textquoteright}s Group Meeting},
	volume = {2840},
	year = {2003},
	month = {2003-09},
	pages = {88-97},
	publisher = {Springer-Verlag, Berlin},
	address = {Venice, Italy},
	keywords = {ftmpi},
	author = {Edgar Gabriel and Graham Fagg and Jack Dongarra}
}
@inproceedings {icl:153,
	title = {Fault Tolerant Communication Library and Applications for High Performance Computing},
	journal = {Los Alamos Computer Science Institute (LACSI) Symposium 2003 (presented)},
	year = {2003},
	month = {2003-10},
	address = {Santa Fe, NM},
	keywords = {ftmpi, lacsi},
	author = {Graham Fagg and Edgar Gabriel and Zizhong Chen and Thara Angskun and George Bosilca and Antonin Bukovsky and Jack Dongarra}
}
@inproceedings {icl:144,
	title = {A Fault-Tolerant Communication Library for Grid Environments},
	journal = {17th Annual ACM International Conference on Supercomputing (ICS{\textquoteright}03) International Workshop on Grid Computing and e-Science},
	year = {2003},
	month = {2003-06},
	address = {San Francisco},
	keywords = {ftmpi, lacsi},
	author = {Edgar Gabriel and Graham Fagg and Antonin Bukovsky and Thara Angskun 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}
}
@techreport {icl:189,
	title = {A Proposed Standard for Matrix Metadata},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-03-02},
	year = {2003},
	month = {2003-11},
	address = {Submitted to ACM TOMS},
	keywords = {salsa, sans},
	author = {Victor Eijkhout and Erika Fuentes}
}
@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}
}
@article {icl:213,
	title = {HARNESS Fault Tolerant MPI Design, Usage and Performance Issues},
	journal = {Future Generation Computer Systems},
	volume = {18},
	number = {8},
	year = {2002},
	month = {2002-01},
	pages = {1127-1142},
	author = {Graham Fagg and Jack Dongarra}
}
@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}
}
@inproceedings {icl:79,
	title = {Toward a Framework for Preparing and Executing Adaptive Grid Programs},
	journal = {International Parallel and Distributed Processing Symposium: IPDPS 2002 Workshops},
	year = {2002},
	month = {2002-04},
	pages = {0171},
	address = {Fort Lauderdale, FL},
	keywords = {grads},
	author = {Ken Kennedy and John Mellor-Crummey and Keith Cooper and Linda Torczon and Francine Berman and Andrew Chien and Dave Angulo and Ian Foster and Dennis Gannon and Lennart Johnsson and Carl Kesselman and Jack Dongarra and Sathish Vadhiyar}
}
@article {icl: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:203,
	title = {Fault Tolerant MPI for the HARNESS Meta-Computing System},
	journal = {Proceedings of International Conference of Computational Science - ICCS 2001, Lecture Notes in Computer Science},
	volume = {2073},
	year = {2001},
	month = {2001-00},
	pages = {355-366},
	publisher = {Springer Verlag},
	address = {Berlin},
	keywords = {ftmpi, harness},
	doi = {10.1007/3-540-45545-0_44},
	author = {Graham Fagg and Antonin Bukovsky and Jack Dongarra},
	editor = {Benjoe A. Juliano and R. Renner and K. Tan}
}
@article {icl:90,
	title = {The GrADS Project: Software Support for High-Level Grid Application Development},
	journal = {International Journal of High Performance Applications and Supercomputing},
	volume = {15},
	number = {4},
	year = {2001},
	month = {2001-01},
	pages = {327-344},
	keywords = {grads},
	author = {Francine Berman and Andrew Chien and Keith Cooper and Jack Dongarra and Ian Foster and Dennis Gannon and Lennart Johnsson and Ken Kennedy and Carl Kesselman and John Mellor-Crummey and Dan Reed and Linda Torczon and Rich Wolski}
}
@article {icl:86,
	title = {HARNESS and Fault Tolerant MPI},
	journal = {Parallel Computing},
	volume = {27},
	number = {11},
	year = {2001},
	month = {2001-01},
	pages = {1479-1496},
	author = {Graham Fagg and Antonin Bukovsky and Jack Dongarra}
}
@article {icl:89,
	title = {Numerical Libraries and The Grid},
	journal = {International Journal of High Performance Applications and Supercomputing},
	volume = {15},
	number = {4},
	year = {2001},
	month = {2001-01},
	pages = {359-374},
	keywords = {grads},
	author = {Antoine Petitet and Susan Blackford and Jack Dongarra and Brett Ellis and Graham Fagg and Kenneth Roche and Sathish Vadhiyar}
}
@techreport {icl:21,
	title = {Numerical Libraries and The Grid: The Grads Experiments with ScaLAPACK},
	journal = {University of Tennessee Computer Science Technical Report},
	number = {UT-CS-01-460},
	year = {2001},
	month = {2001-01},
	keywords = {grads, scalapack},
	author = {Antoine Petitet and Susan Blackford and Jack Dongarra and Brett Ellis and Graham Fagg and Kenneth Roche and Sathish Vadhiyar}
}
@inproceedings {icl:8,
	title = {Parallel I/O for EQM Applications},
	journal = {Department of Defense Users{\textquoteright} Group Conference Proceedings (to appear),},
	year = {2001},
	month = {2001-06},
	address = {Biloxi, Mississippi},
	keywords = {ftmpi},
	author = {David Cronk and Graham Fagg and Shirley Moore}
}
@article {icl:14,
	title = {Parallel IO Support for Meta-Computing Applications: MPI_Connect IO Applied to PACX-MPI},
	journal = {8th European PVM/MPI User{\textquoteright}s Group Meeting, Lecture Notes in Computer Science},
	volume = {2131},
	year = {2001},
	month = {2001-09},
	publisher = {Springer Verlag, Berlin},
	address = {Greece},
	keywords = {ftmpi},
	author = {Graham Fagg and Edgar Gabriel and Michael Resch}
}
@inproceedings {icl:78,
	title = {Performance Modeling for Self Adapting Collective Communications for MPI},
	journal = {LACSI Symposium 2001},
	year = {2001},
	month = {2001-10},
	address = {Santa Fe, NM},
	keywords = {ftmpi},
	author = {Sathish Vadhiyar and Graham Fagg and Jack Dongarra}
}
@article {icl:82,
	title = {Telescoping Languages: A Strategy for Automatic Generation of Scientific Problem-Solving Systems from Annotated Libraries},
	journal = {Journal of Parallel and Distributed Computing},
	volume = {61},
	number = {12},
	year = {2001},
	month = {2001-12},
	pages = {1803-1826},
	author = {Ken Kennedy and Bradley Broom and Keith Cooper and Jack Dongarra and Rob Fowler and Dennis Gannon and Lennart Johnsson and John Mellor-Crummey and Linda Torczon}
}
@inproceedings {icl:48,
	title = {Automatically Tuned Collective Communications},
	journal = {Proceedings of SuperComputing 2000 (SC{\textquoteright}2000)},
	year = {2000},
	month = {2000-11},
	address = {Dallas, TX},
	keywords = {ftmpi},
	author = {Sathish Vadhiyar and Graham Fagg and Jack Dongarra}
}
@inproceedings {icl:43,
	title = {FT-MPI: Fault Tolerant MPI, Supporting Dynamic Applications in a Dynamic World},
	journal = {Lecture Notes in Computer Science: Proceedings of EuroPVM-MPI 2000},
	year = {2000},
	month = {2000-01},
	pages = {V1908,346-353},
	address = {(Hungary: Springer Verlag, 2000)},
	keywords = {ftmpi},
	author = {Graham Fagg and Jack Dongarra}
}
@techreport {icl:30,
	title = {The GrADS Project: Software Support for High-Level Grid Application Development},
	journal = {Technical Report},
	year = {2000},
	month = {2000-02},
	keywords = {grads},
	author = {Francine Berman and Andrew Chien and Keith Cooper and Jack Dongarra and Ian Foster and Dennis Gannon and Lennart Johnsson and Ken Kennedy and Carl Kesselman and Dan Reed and Linda Torczon and Rich Wolski}
}
@article {icl:118,
	title = {Message Passing Software Systems},
	journal = {Encyclopedia of Electrical and Engineering, Supplement 1},
	year = {2000},
	month = {2000-00},
	publisher = {John Wiley \& Sons, Inc.},
	keywords = {ftmpi},
	author = {Jack Dongarra and Graham Fagg and Rolf Hempel and David W. Walker},
	editor = {J. Webster}
}
@techreport {icl:34,
	title = {Metacomputing: An Evaluation of Emerging Systems},
	journal = {University of Tennessee Computer Science Department Technical Report},
	number = {UT-CS-00-445},
	year = {2000},
	month = {2000-07},
	author = {David Cronk and Brett Ellis and Graham Fagg}
}
@techreport {icl:228,
	title = {Secure Remote Access to Numerical Software and Computation Hardware},
	journal = {University of Tennessee Computer Science Technical Report, UT-CS-00-446},
	year = {2000},
	month = {2000-07},
	author = {Dorian Arnold and Shirley Browne and Jack Dongarra and Graham Fagg and Keith Moore}
}
@inproceedings {icl:26,
	title = {Secure Remote Access to Numerical Software and Computational Hardware},
	journal = {Proceedings of the DoD HPC Users Group Conference (HPCUG) 2000},
	year = {2000},
	month = {2000-06},
	address = {Albuquerque, NM},
	keywords = {netsolve},
	author = {Dorian Arnold and Shirley Browne and Jack Dongarra and Graham Fagg and Keith Moore}
}
@article {icl:74,
	title = {Experiences with Windows 95/NT as a Cluster Computing Platform for Parallel Computing},
	journal = {Parallel and Distributed Computing Practices, Special Issue: Cluster Computing},
	volume = {2},
	number = {2},
	year = {1999},
	month = {1999-02},
	pages = {119-128},
	publisher = {Nova Science Publishers, USA},
	author = {Markus Fischer and Jack Dongarra}
}
@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:73,
	title = {Scalable Networked Information Processing Environment (SNIPE)},
	journal = {Journal on Future Generation Computer Systems},
	volume = {15},
	number = {5/6},
	year = {1999},
	month = {1999-01},
	pages = {595-605},
	keywords = {harness},
	author = {Graham Fagg and Keith Moore and Jack Dongarra}
}
