@article {,
	title = {Economical Quasi-Newton Unitary Optimization of Electronic Orbitals},
	journal = {Physical Chemistry Chemical Physics},
	year = {2024},
	month = {2023-12},
	abstract = {We present an efficient quasi-Newton orbital solver optimized to reduce the number of gradient evaluations and other computational steps of comparable cost. The solver optimizes orthogonal orbitals by sequences of unitary rotations generated by the (preconditioned) limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm equipped with trust-region step restriction. The low-rank structure of the L-BFGS inverse Hessian is exploited when solving the trust-region problem. The efficiency of the proposed {\textquoteleft}{\textquoteleft}Quasi-Newton Unitary Optimization with Trust-Region{\textquoteright}{\textquoteright} (QUOTR) solver is compared to that of the standard Roothaan-Hall approach accelerated by the Direct Inversion of Iterative Subspace (DIIS), and other exact and approximate Newton solvers for mean-field (Hartree-Fock and Kohn-Sham) problems.},
	issn = {1463-9076},
	doi = {10.1039/D3CP05557D},
	url = {http://pubs.rsc.org/en/Content/ArticleLanding/2024/CP/D3CP05557},
	author = {Slattery, Samuel A and Surjuse, Kshitijkumar A and Peterson, Charles and Penchoff, Deborah A and Valeev, Edward}
}
@booklet {,
	title = {XaaS: Acceleration as a Service to Enable Productive High-Performance Cloud Computing},
	year = {2024},
	month = {2024-01},
	publisher = {arXiv},
	abstract = {HPC and Cloud have evolved independently, specializing their innovations into performance or productivity. Acceleration as a Service (XaaS) is a recipe to empower both fields with a shared execution platform that provides transparent access to computing resources, regardless of the underlying cloud or HPC service provider. Bridging HPC and cloud advancements, XaaS presents a unified architecture built on performance-portable containers. Our converged model concentrates on low-overhead, high-performance communication and computing, targeting resource-intensive workloads from climate simulations to machine learning. XaaS lifts the restricted allocation model of Function-as-a-Service (FaaS), allowing users to benefit from the flexibility and efficient resource utilization of serverless while supporting long-running and performance-sensitive workloads from HPC.},
	url = {https://arxiv.org/abs/2401.04552},
	author = {Torsten Hoefler and Marcin Copik and Pete Beckman and Andrew Jones and Ian Foster and Manish Parashar and Daniel Reed and Matthias Troyer and Thomas Schulthess and Dan Ernst and Jack Dongarra}
}
@conference {,
	title = {AI Benchmarking for Science: Efforts from the MLCommons Science Working Group},
	booktitle = {Lecture Notes in Computer Science},
	volume = {13387},
	year = {2023},
	month = {2023-01},
	pages = {47 - 64},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {With machine learning (ML) becoming a transformative tool for science, the scientific community needs a clear catalogue of ML techniques, and their relative benefits on various scientific problems, if they were to make significant advances in science using AI. Although this comes under the purview of benchmarking, conventional benchmarking initiatives are focused on performance, and as such, science, often becomes a secondary criteria.

In this paper, we describe a community effort from a working group, namely, MLCommons Science Working Group, in developing science-specific AI benchmarking for the international scientific community. Since the inception of the working group in 2020, the group has worked very collaboratively with a number of national laboratories, academic institutions and industries, across the world, and has developed four science-specific AI benchmarks. We will describe the overall process, the resulting benchmarks along with some initial results. We foresee that this initiative is likely to be very transformative for the AI for Science, and for performance-focused communities.},
	isbn = {978-3-031-23219-0},
	doi = {10.1007/978-3-031-23220-610.1007/978-3-031-23220-6_4},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-23220-6_4},
	author = {Thiyagalingam, Jeyan and von Laszewski, Gregor and Yin, Junqi and Emani, Murali and Papay, Juri and Barrett, Gregg and Luszczek, Piotr and Tsaris, Aristeidis and Kirkpatrick, Christine and Wang, Feiyi and Gibbs, Tom and Vishwanath, Venkatram and Shankar, Mallikarjun and Fox, Geoffrey and Hey, Tony},
	editor = {Anzt, Hartwig and Bienz, Amanda and Luszczek, Piotr and Baboulin, Marc}
}
@article {,
	title = {Direct Determination of Optimal Real-Space Orbitals for Correlated Electronic Structure of Molecules},
	journal = {Journal of Chemical Theory and Computation},
	volume = {19},
	year = {2023},
	month = {2023-10},
	pages = {7230 - 7241},
	abstract = {We demonstrate how to determine numerically nearly exact orthonormal orbitals that are optimal for the evaluation of the energy of arbitrary (correlated) states of atoms and molecules by minimization of the energy Lagrangian. Orbitals are expressed in real space using a multiresolution spectral element basis that is refined adaptively to achieve the user-specified target precision while avoiding the ill-conditioning issues that plague AO basis set expansions traditionally used for correlated models of molecular electronic structure. For light atoms, the orbital solver, in conjunction with a variational electronic structure model [selected Configuration Interaction (CI)] provides energies of comparable precision to a state-of-the-art atomic CI solver. The computed electronic energies of atoms and molecules are significantly more accurate than the counterparts obtained with the orbital sets of the same rank expanded in Gaussian AO bases, and can be determined even when linear dependence issues preclude the use of the AO bases. It is feasible to optimize more than 100 fully correlated numerical orbitals on a single computer node, and significant room exists for additional improvement. These findings suggest that real-space orbital representations might be the preferred alternative to AO representations for high-end models of correlated electronic states of molecules and materials.},
	issn = {1549-9618},
	doi = {10.1021/acs.jctc.3c00732},
	url = {https://pubs.acs.org/doi/10.1021/acs.jctc.3c00732},
	author = {Valeev, Edward F. and Harrison, Robert J. and Holmes, Adam A. and Peterson, Charles C. and Penchoff, Deborah A.}
}
@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 = {Preconditioners for Batched Iterative Linear Solvers on GPUs},
	booktitle = {Smoky Mountains Computational Sciences and Engineering Conference},
	volume = {169075},
	year = {2023},
	month = {2023-01},
	pages = {38 - 53},
	publisher = {Springer Nature Switzerland},
	organization = {Springer Nature Switzerland},
	abstract = {Batched iterative solvers can be an attractive alternative to batched direct solvers if the linear systems allow for fast convergence. In non-batched settings, iterative solvers are often enhanced with sophisticated preconditioners to improve convergence. In this paper, we develop preconditioners for batched iterative solvers that improve the iterative solver convergence without incurring detrimental resource overhead and preserving much of the iterative solver flexibility. We detail the design and implementation considerations, present a user-friendly interface to the batched preconditioners, and demonstrate the convergence and runtime benefits over non-preconditioned batched iterative solvers on state-of-the-art GPUs for a variety of benchmark problems from finite difference stencil matrices, the Suitesparse matrix collection and a computational chemistry application.},
	isbn = {978-3-031-23605-1},
	doi = {10.1007/978-3-031-23606-810.1007/978-3-031-23606-8_3},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-23606-8_3},
	author = {Aggarwal, Isha and Nayak, Pratik and Kashi, Aditya and Anzt, Hartwig},
	editor = {Doug, Kothe and Al, Geist and Pophale, Swaroop and Liu, Hong and Parete-Koon, Suzanne}
}
@article {,
	title = {Revisiting I/O bandwidth-sharing strategies for HPC applications},
	number = {RR-9502},
	year = {2023},
	month = {2023-03},
	institution = {INRIA},
	abstract = {This work revisits I/O bandwidth-sharing strategies for HPC applications. When several applications post concurrent I/O operations, well-known approaches include serializing these operations (First-Come First-Served) or fair-sharing the bandwidth across them (FairShare). Another recent approach, I/O-Sets, assigns priorities to the applications, which are classified into different sets based upon the average length of their iterations. We introduce several new bandwidth-sharing strategies, some of them simple greedy algorithms, and some of them more complicated to implement, and we compare them with existing ones. Our new strategies do not rely on any a-priori knowledge of the behavior of the applications, such as the length of work phases, the volume of I/O operations, or some expected periodicity. We introduce a rigorous framework, namely steady-state windows, which enables to derive bounds on the competitive ratio of all bandwidth-sharing strategies for three different objectives: minimum yield, platform utilization, and global efficiency. To the best of our knowledge, this work is the first to provide a quantitative assessment of the online competitiveness of any bandwidth-sharing strategy. This theory-oriented assessment is complemented by a comprehensive set of simulations, based upon both synthetic and realistic traces. The main conclusion is that our simple and low-complexity greedy strategies significantly outperform First-Come First-Served, FairShare and I/O-Sets, and we recommend that the I/O community implements them for further assessment.},
	keywords = {bandwidth sharing, HPC applications, I/O, scheduling strategy},
	url = {https://hal.inria.fr/hal-04038011},
	author = {Anne Benoit and Thomas Herault and Lucas Perotin and Yves Robert and Frederic Vivien}
}
@article {,
	title = {Accelerating Geostatistical Modeling and Prediction With Mixed-Precision Computations: A High-Productivity Approach With PaRSEC},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	volume = {33},
	year = {2022},
	month = {2022-04},
	pages = {964 - 976},
	abstract = {Geostatistical modeling, one of the prime motivating applications for exascale computing, is a technique for predicting desired quantities from geographically distributed data, based on statistical models and optimization of parameters. Spatial data are assumed to possess properties of stationarity or non-stationarity via a kernel fitted to a covariance matrix. A primary workhorse of stationary spatial statistics is Gaussian maximum log-likelihood estimation (MLE), whose central data structure is a dense, symmetric positive definite covariance matrix of the dimension of the number of correlated observations. Two essential operations in MLE are the application of the inverse and evaluation of the determinant of the covariance matrix. These can be rendered through the Cholesky decomposition and triangular solution. In this contribution, we reduce the precision of weakly correlated locations to single- or half- precision based on distance. We thus exploit mathematical structure to migrate MLE to a three-precision approximation that takes advantage of contemporary architectures offering BLAS3-like operations in a single instruction that are extremely fast for reduced precision. We illustrate application-expected accuracy worthy of double-precision from a majority half-precision computation, in a context where uniform single-precision is by itself insufficient. In tackling the complexity and imbalance caused by the mixing of three precisions, we deploy the PaRSEC runtime system. PaRSEC delivers on-demand casting of precisions while orchestrating tasks and data movement in a multi-GPU distributed-memory environment within a tile-based Cholesky factorization. Application-expected accuracy is maintained while achieving up to 1.59X by mixing FP64/FP32 operations on 1536 nodes of HAWK or 4096 nodes of Shaheen II , and up to 2.64X by mixing FP64/FP32/FP16 operations on 128 nodes of Summit , relative to FP64-only operations. This translates into up to 4.5, 4.7, ...},
	keywords = {Computational modeling, Covariance matrices, Data models, Maximum likelihood estimation, Predictive models, runtime, Task analysis},
	issn = {1045-9219},
	doi = {10.1109/TPDS.2021.3084071},
	url = {https://ieeexplore.ieee.org/document/9442267/https://ieeexplore.ieee.org/ielam/71/9575177/9442267-aam.pdfhttp://xplorestaging.ieee.org/ielx7/71/9575177/09442267.pdf?arnumber=9442267},
	author = {Abdulah, Sameh and Qinglei Cao and Pei, Yu and George Bosilca and Jack Dongarra and Genton, Marc G. and Keyes, David E. and Ltaief, Hatem and Sun, Ying}
}
@inbook {,
	title = {Batch QR Factorization on GPUs: Design, Optimization, and Tuning},
	booktitle = { Lecture Notes in Computer Science},
	volume = {13350},
	year = {2022},
	month = {2022-06},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {QR factorization of dense matrices is a ubiquitous tool in high performance computing (HPC). From solving linear systems and least squares problems to eigenvalue problems, and singular value decompositions, the impact of a high performance QR factorization is fundamental to computer simulations and many applications. More importantly, the QR factorization on a batch of relatively small matrices has acquired a lot of attention in sparse direct solvers and low-rank approximations for Hierarchical matrices. To address this interest and demand, we developed and present a high performance batch QR factorization for Graphics Processing Units (GPUs). We present a multi-level blocking strategy that adjusts various algorithmic designs to the size of the input matrices. We also show that following the LAPACK QR design convention, while still useful, is significantly outperformed by unconventional code structures that increase data reuse. The performance results show multi-fold speedups against the state of the art libraries on the latest GPU architectures from both NVIDIA and AMD.},
	keywords = {Batch linear algebra, GPU computing, QR factorization},
	isbn = {978-3-031-08750-9},
	doi = {10.1007/978-3-031-08751-6_5},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-08751-6_5},
	author = {Abdelfattah, Ahmad and Stanimire Tomov and Dongarra, Jack},
	editor = {Groen, Derek and de Mulatier, C{\'e}lia and Paszy{\'n}ski, Maciej and Krzhizhanovskaya, Valeria V. and Dongarra, Jack J. and Sloot, Peter M. A.}
}
@inproceedings {,
	title = {Checkpointing {\`a} la Young/Daly: An Overview},
	journal = {IC3-2022: Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing},
	year = {2022},
	month = {2022-08},
	pages = {701-710},
	publisher = {ACM Press},
	address = {Noida, India},
	abstract = {The Young/Daly formula provides an approximation of the optimal checkpoint period for a parallel application executing on a supercomputing platform. The Young/Daly formula was originally designed for preemptible tightly-coupled applications. We provide some background and survey various application scenarios to assess the usefulness and limitations of the formula.},
	isbn = {9781450396752},
	doi = {10.1145/3549206},
	url = {https://dl.acm.org/doi/fullHtml/10.1145/3549206.3549328},
	author = {Anne Benoit and Yishu Du and Thomas Herault and Loris Marchal and Guillaume Pallez and Lucas Perotin and Yves Robert and Hongyang Sun and Frederic Vivien}
}
@article {,
	title = {Computational science for a better future},
	journal = {Journal of Computational Science},
	volume = {62},
	year = {2022},
	month = {2022-07},
	pages = {101745},
	issn = {18777503},
	doi = {10.1016/j.jocs.2022.101745},
	url = {https://www.sciencedirect.com/science/article/pii/S1877750322001351},
	author = {Kovalchuk, Sergey V. and Krzhizhanovskaya, Valeria V. and Paszy{\'n}ski, Maciej and Kranzlm{\"u}ller, Dieter and Dongarra, Jack and Sloot, Peter M.A.}
}
@article {,
	title = {Evaluations of molecular modeling and machine learning for predictive capabilities in binding of lanthanum and actinium with carboxylic acids},
	journal = {Journal of Radioanalytical and Nuclear Chemistry},
	year = {2022},
	month = {2022-12},
	issn = {0236-5731},
	doi = {10.1007/s10967-022-08620-7},
	url = {https://rdcu.be/c2lGj},
	author = {Penchoff, Deborah A. and Peterson, Charles C. and Wrancher, Eleigha M. and George Bosilca and Harrison, Robert J. and Valeev, Edward F. and Benny, Paul D.}
}
@conference {,
	title = {A Framework to Exploit Data Sparsity in Tile Low-Rank Cholesky Factorization},
	booktitle = {IEEE International Parallel and Distributed Processing Symposium (IPDPS)},
	year = {2022},
	month = {2022-07},
	doi = {10.1109/IPDPS53621.2022.00047},
	url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=9820680\&isnumber=9820610},
	author = {Qinglei Cao and Rabab Alomairy and Yu Pei and George Bosilca and Hatem Ltaief and David Keyes and Jack Dongarra}
}
@article {,
	title = {OpenMP application experiences: Porting to accelerated nodes},
	journal = {Parallel Computing},
	volume = {109},
	year = {2022},
	month = {2022-03},
	abstract = {As recent enhancements to the OpenMP specification become available in its implementations, there is a need to share the results of experimentation in order to better understand the OpenMP implementation{\textquoteright}s behavior in practice, to identify pitfalls, and to learn how the implementations can be effectively deployed in scientific codes. We report on experiences gained and practices adopted when using OpenMP to port a variety of ECP applications, mini-apps and libraries based on different computational motifs to accelerator-based leadership-class high-performance supercomputer systems at the United States Department of Energy. Additionally, we identify important challenges and open problems related to the deployment of OpenMP. Through our report of experiences, we find that OpenMP implementations are successful on current supercomputing platforms and that OpenMP is a promising programming model to use for applications to be run on emerging and future platforms with accelerated nodes.},
	issn = {01678191},
	doi = {10.1016/j.parco.2021.102856},
	url = {https://www.sciencedirect.com/science/article/pii/S0167819121001009},
	author = {Bak, Seonmyeong and Bertoni, Colleen and Boehm, Swen and Budiardja, Reuben and Chapman, Barbara M. and Doerfert, Johannes and Eisenbach, Markus and Finkel, Hal and Hernandez, Oscar and Huber, Joseph and Iwasaki, Shintaro and Kale, Vivek and Kent, Paul R.C. and Kwack, JaeHyuk and Lin, Meifeng and Luszczek, Piotr and Luo, Ye and Pham, Buu and Pophale, Swaroop and Ravikumar, Kiran and Sarkar, Vivek and Scogland, Thomas and Tian, Shilei and Yeung, P.K.}
}
@article {,
	title = {Optimal Checkpointing Strategies for Iterative Applications},
	journal = {IEEE Transactions on Parallel Distributed Systems},
	volume = {33},
	year = {2022},
	month = {2022-03},
	pages = {507-522},
	doi = {10.1109/TPDS.2021.3099440},
	url = {https://ieeexplore.ieee.org/document/9495174},
	author = {Yishu Du and Guillaume Pallez and Loris Marchal and Yves Robert}
}
@inproceedings {,
	title = {Reshaping Geostatistical Modeling and Prediction for Extreme-Scale Environmental Applications},
	journal = {2022 International Conference for High Performance Computing, Networking, Storage and Analysis (SC22)},
	year = {2022},
	month = {2022-11},
	publisher = {IEEE Press},
	address = {Dallas, TX},
	abstract = {We extend the capability of space-time geostatistical modeling using algebraic approximations, illustrating application-expected accuracy worthy of double precision from majority low-precision computations and low-rank matrix approximations. We exploit the mathematical structure of the dense covariance matrix whose inverse action and determinant are repeatedly required in Gaussian log-likelihood optimization. Geostatistics augments first-principles modeling approaches for the prediction of environmental phenomena given the availability of measurements at a large number of locations; however, traditional Cholesky-based approaches grow cubically in complexity, gating practical extension to continental and global datasets now available. We combine the linear algebraic contributions of mixed-precision and low-rank computations within a tilebased Cholesky solver with on-demand casting of precisions and dynamic runtime support from PaRSEC to orchestrate tasks and data movement. Our adaptive approach scales on various systems and leverages the Fujitsu A64FX nodes of Fugaku to achieve up to 12X performance speedup against the highly optimized dense Cholesky implementation.},
	keywords = {climate/weather prediction, dynamic runtime systems, high performance computing., low- rank matrix approximations, mixed-precision computations, space-time geospatial statistics, Task-based programming models},
	isbn = {9784665454445},
	url = {https://dl.acm.org/doi/abs/10.5555/3571885.3571888},
	author = {Cao, Qinglei and Abdulah, Sameh and Rabab Alomairy and Pei, Yu and Pratik Nag and George Bosilca and Dongarra, Jack and Genton, Marc G. and Keyes, David and Ltaief, Hatem and Sun, Ying}
}
@article {,
	title = {20 years of computational science: Selected papers from 2020 International Conference on Computational Science},
	journal = {Journal of Computational Science},
	volume = {53},
	year = {2021},
	pages = {101395{\textendash}101395},
	abstract = {We thank the authors of the selected papers for their valuable contributions, the reviewers of this special section for their in-depth reviews and constructive comments, the ICCS program committee members, and workshop organizers for their diligent work ensuring the high standard of accepted ICCS papers. As always, we also thank Springer for publishing the conference proceedings and Elsevier for their continuous support and inspiration during the preparation and publishing of this virtual special issue.},
	doi = {10.1016/j.jocs.2021.101395},
	author = {Kovalchuk, Sergey V and Krzhizhanovskaya, Valeria V and Sloot, PMA and Z{\'a}vodszky, G{\'a}bor and Lees, Michael H and Paszy{\'n}ski, M and Jack Dongarra}
}
@inproceedings {,
	title = {Evaluating Task Dropping Strategies for Overloaded Real-Time Systems (Work-In-Progress)},
	journal = {42nd Real Time Systems Symposium (RTSS)},
	year = {2021},
	publisher = {IEEE Computer Society Press},
	author = {Yiqin Gao and Guillaume Pallez and Yves Robert and Frederic Vivien}
}
@inbook {,
	title = {An Introduction to High Performance Computing and Its Intersection with Advances in Modeling Rare Earth Elements and Actinides},
	booktitle = {Rare Earth Elements and Actinides: Progress in Computational Science Applications},
	volume = {1388},
	year = {2021},
	month = {2021-10},
	pages = {3-53},
	publisher = {American Chemical Society},
	organization = {American Chemical Society},
	chapter = {1},
	address = {Washington, DC},
	abstract = {Computationally driven solutions in nuclear and radiochemistry heavily depend on efficient modeling of Rare Earth Elements (REEs) and actinides. Accurate modeling of REEs and actinides faces challenges stemming from limitations from an imbalanced hardware-software ecosystem and its implications on inefficient use of High Performance Computing (HPC). This chapter provides a historical perspective on the evolution of HPC hardware, its intersectionality with domain sciences, the importance of benchmarks for performance, and an overview of challenges and advances in modeling REEs and actinides. This chapter intends to provide an introduction for researchers at the intersection of scientific computing, software development for HPC, and applied computational modeling of REEs and actinides. The chapter is structured in five sections. First, the Introduction includes subsections focusing on the Importance of REEs and Actinides (1.1), Hardware, Software, and the HPC Ecosystem (1.2), and Electronic Structure Modeling of REEs and Actinides (1.3). Second, a section in High Performance Computing focuses on the TOP500 (2.1), HPC Performance (2.2), HPC Benchmarks: Processing, Bandwidth, and Latency (2.3), and HPC Benchmarks and their Relationship to Chemical Modeling (2.4). Third, the Software Challenges and Advances focus on NWChem/NWChemEx (3.1), MADNESS (3.2), and MPQC (3.3). The fourth section provides a short overview of Artificial Intelligence in HPC applications relevant to nuclear and radiochemistry. The fifth section illustrates A Protocol to Evaluate Complexation Preferences in Separations of REEs and Actinides through Computational Modeling.},
	keywords = {actinide, Computational modeling, HPC, REE},
	isbn = {ISBN13: 9780841298255 eISBN: 9780841298248},
	doi = {10.1021/bk-2021-1388.ch001},
	url = {https://pubs.acs.org/doi/10.1021/bk-2021-1388.ch001},
	author = {Deborah A. Penchoff and Edward Valeev and Heike Jagode and Piotr Luszczek and Anthony Danalis and George Bosilca and Robert J. Harrison and Jack Dongarra and Theresa L. Windus}
}
@conference {,
	title = {Leveraging PaRSEC Runtime Support to Tackle Challenging 3D Data-Sparse Matrix Problems},
	booktitle = {35th IEEE International Parallel \& Distributed Processing Symposium (IPDPS 2021)},
	year = {2021},
	month = {2021-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Portland, OR},
	abstract = {The task-based programming model associated with dynamic runtime systems has gained popularity for challenging problems because of workload imbalance, heterogeneous resources, or extreme concurrency. During the last decade, lowrank matrix approximations, where the main idea consists of exploiting data sparsity typically by compressing off-diagonal tiles up to an application-specific accuracy threshold, have been adopted to address the curse of dimensionality at extreme scale. In this paper, we create a bridge between the runtime and the linear algebra by communicating knowledge of the data sparsity to the runtime. We design and implement this synergistic approach with high user productivity in mind, in the context of the PaRSEC runtime system and the HiCMA numerical library. This requires to extend PaRSEC with new features to integrate rank information into the dataflow so that proper decisions can be taken at runtime. We focus on the tile low-rank (TLR) Cholesky factorization for solving 3D data-sparse covariance matrix problems arising in environmental applications. In particular, we employ the 3D exponential model of Matern matrix kernel, which exhibits challenging nonuniform {\textasciiacute}high ranks in off-diagonal tiles. We first provide a dynamic data structure management driven by a performance model to reduce extra floating-point operations. Next, we optimize the memory footprint of the application by relying on a dynamic memory allocator, and supported by a rank-aware data distribution to cope with the workload imbalance. Finally, we expose further parallelism using kernel recursive formulations to shorten the critical path. Our resulting high-performance implementation outperforms existing data-sparse TLR Cholesky factorization by up to 7-fold on a large-scale distributed-memory system, while minimizing the memory footprint up to a 44-fold factor. This multidisciplinary work highlights the need to empower runtime systems beyond their original duty of task scheduling for servicing next-generation low-rank matrix algebra libraries.},
	keywords = {asynchronous executions and load balancing, dynamic runtime system, environmental applications, High-performance computing, low-rank matrix computations, task-based programming model, user productivity},
	author = {Qinglei Cao and Yu Pei and Kadir Akbudak and George Bosilca and Hatem Ltaief and David Keyes and Jack Dongarra}
}
@article {,
	title = {libCEED: Fast algebra for high-order element-based discretizations},
	journal = {Journal of Open Source Software},
	volume = {6},
	number = {63},
	year = {2021},
	pages = {2945},
	abstract = {Finite element methods are widely used to solve partial differential equations (PDE) in science and engineering, but their standard implementation (Arndt et al., 2020; Kirk et al., 2006; Logg et al., 2012) relies on assembling sparse matrices. Sparse matrix multiplication and triangular operations perform a scalar multiply and add for each nonzero entry, just 2 floating point operations (flops) per scalar that must be loaded from memory (Williams et al., 2009). Modern hardware is capable of nearly 100 flops per scalar streamed from memory (Rupp, 2020) so sparse matrix operations cannot achieve more than about 2\% utilization of arithmetic units.
Matrix assembly becomes even more problematic when the polynomial degree p of the basis functions is increased, resulting in O(pd) storage and O(p2d) compute per degree of freedom (DoF) in d dimensions. Methods pioneered by the spectral element community (Deville et al., 2002; Orszag, 1980) exploit problem structure to reduce costs to O(1) storage and O(p)
compute per DoF, with very high utilization of modern CPUs and GPUs. Unfortunately, highquality implementations have been relegated to applications and intrusive frameworks that are often difficult to extend to new problems or incorporate into legacy applications, especially when strong preconditioners are required.
libCEED, the Code for Efficient Extensible Discretization (Abdelfattah et al., 2021), is a lightweight library that provides a purely algebraic interface for linear and nonlinear operators and preconditioners with element-based discretizations. libCEED provides portable performance via run-time selection of implementations optimized for CPUs and GPUs, including
support for just-in-time (JIT) compilation. It is designed for convenient use in new and legacy software, and offers interfaces in C99 (International Standards Organisation, 1999), Fortran77 (ANSI, 1978), Python (Python, 2021), Julia (Bezanson et al., 2017), and Rust (Rust, 2021). Users and library developers can integrate libCEED at a low level into existing applications in
place of existing matrix-vector products without significant refactoring of their own discretization infrastructure. Alternatively, users can utilize integrated libCEED support in MFEM (Anderson et al., 2020; MFEM, 2021).
In addition to supporting applications and discretization libraries, libCEED provides a platform for performance engineering and co-design, as well as an algebraic interface for solvers research like adaptive p-multigrid, much like how sparse matrix libraries enable development and deployment of algebraic multigrid solvers},
	keywords = {finite elements, high-order methods, High-performance computing, matrix-free, spectral elements},
	doi = {10.21105/joss.02945},
	url = {https://doi.org/10.21105/joss.02945},
	author = {Jed Brown and Ahmad Abdelfattah and Valeria Barra and Natalie Beams and Jean-Sylvain Camier and Veselin Dobrev and Yohann Dudouit and Leila Ghaffari and Tzanio Kolev and David Medina and Will Pazner and Thilina Ratnayaka and Jeremy Thompson and Stanimire Tomov}
}
@book {,
	title = {Rare Earth Elements and Actinides: Progress in Computational Science Applications},
	series = {ACS Symposium Series},
	volume = {1388},
	year = {2021},
	month = {2021-10},
	publisher = {American Chemical Society},
	organization = {American Chemical Society},
	address = {Washington, DC},
	abstract = {Rare earth elements (REEs) and actinides are critical to electronics, communication, military applications, and green energy systems. They also play a large role in nuclear waste challenges with critical national importance. Actinides are still among some of the least studied elements in the periodic table, due to their short half-lives and radioactivity, which demand expert facilities for research. Computational modeling greatly aids in understanding REEs and actinides; however, electronic structure modeling of these elements presents limitations. High Performance Computing (HPC) has had a direct impact not only on technical advances and access to information on a global scale but also on investigations of REEs and actinides. This work discusses recent advances in molecular and data driven modeling that are essential to the study of REEs and actinides, effects of computational science in nuclear and radiochemical applications, and advances and challenges in the exascale era of supercomputing.},
	keywords = {actinides, HPC, REEs},
	isbn = {ISBN13: ‍9780841298255 eISBN: ‍9780841298248},
	doi = {DOI: 10.1021/bk-2021-1388},
	url = {https://pubs.acs.org/doi/book/10.1021/bk-2021-1388},
	author = {Deborah A. Penchoff and Theresa L. Windus and Charles C. Peterson}
}
@inbook {,
	title = {Rare Earth Elements and Critical Materials: Uses and Availability},
	booktitle = {Rare Earth Elements and Actinides: Progress in Computational Science Applications},
	volume = {1388},
	year = {2021},
	month = {2021-10},
	pages = {63-74},
	publisher = {American Chemical Society},
	organization = {American Chemical Society},
	chapter = {3},
	address = {Washington, DC},
	abstract = {Rare Earth Elements (REEs) are essential elements in critical materials for the economy and national security. This chapter explores REEs{\textquoteright} classifications, importance, abundance, resources and challenges, and the need for solutions and alternatives. REEs{\textquoteright} production, reserves, world markets, and net import reliance is discussed. Applications of REEs to clean energy, electronics, and defense are highlighted.},
	keywords = {critical materials, REE},
	isbn = {ISBN13: 9780841298255 eISBN: 9780841298248},
	doi = {10.1021/bk-2021-1388.ch003},
	url = {https://pubs.acs.org/doi/10.1021/bk-2021-1388.ch003},
	author = {Deborah A. Penchoff and Charles B. Sims and Theresa L. Windus}
}
@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 {1478,
	title = {Communication Avoiding 2D Stencil Implementations over PaRSEC Task-Based Runtime},
	booktitle = {2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
	year = {2020},
	month = {2020-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {New Orleans, LA},
	abstract = {Stencil computation or general sparse matrix-vector product (SpMV) are key components in many algorithms like geometric multigrid or Krylov solvers. But their low arithmetic intensity means that memory bandwidth and network latency will be the performance limiting factors. The current architectural trend favors computations over bandwidth, worsening the already unfavorable imbalance. Previous work approached stencil kernel optimization either by improving memory bandwidth usage or by providing a Communication Avoiding (CA) scheme to minimize network latency in repeated sparse vector multiplication by replicating remote work in order to delay communications on the critical path. Focusing on minimizing communication bottleneck in distributed stencil computation, in this study we combine a CA scheme with the computation and communication overlapping that is inherent in a dataflow task-based runtime system such as PaRSEC to demonstrate their combined benefits. We implemented the 2D five point stencil (Jacobi iteration) in PETSc, and over PaRSEC in two flavors, full communications (base-PaRSEC) and CA-PaRSEC which operate directly on a 2D compute grid. Our results running on two clusters, NaCL and Stampede2 indicate that we can achieve 2{\texttimes} speedup over the standard SpMV solution implemented in PETSc, and in certain cases when kernel execution is not dominating the execution time, the CA-PaRSEC version achieved up to 57\% and 33\% speedup over base-PaRSEC implementation on NaCL and Stampede2 respectively.},
	doi = {https://doi.org/10.1109/IPDPSW50202.2020.00127},
	author = {Yu Pei and Qinglei Cao and George Bosilca and Piotr Luszczek and Victor Eijkhout and Jack Dongarra}
}
@conference {,
	title = {Extreme-Scale Task-Based Cholesky Factorization Toward Climate and Weather Prediction Applications},
	booktitle = {Platform for Advanced Scientific Computing Conference (PASC20)},
	year = {2020},
	month = {2020-06},
	publisher = {ACM},
	organization = {ACM},
	address = {Geneva, Switzerland},
	abstract = {Climate and weather can be predicted statistically via geospatial Maximum Likelihood Estimates (MLE), as an alternative to running large ensembles of forward models. The MLE-based iterative optimization procedure requires the solving of large-scale linear systems that performs a Cholesky factorization on a symmetric positive-definite covariance matrix---a demanding dense factorization in terms of memory footprint and computation. We propose a novel solution to this problem: at the mathematical level, we reduce the computational requirement by exploiting the data sparsity structure of the matrix off-diagonal tiles by means of low-rank approximations; and, at the programming-paradigm level, we integrate PaRSEC, a dynamic, task-based runtime to reach unparalleled levels of efficiency for solving extreme-scale linear algebra matrix operations. The resulting solution leverages fine-grained computations to facilitate asynchronous execution while providing a flexible data distribution to mitigate load imbalance. Performance results are reported using 3D synthetic datasets up to 42M geospatial locations on 130, 000 cores, which represent a cornerstone toward fast and accurate predictions of environmental applications.},
	doi = {https://doi.org/10.1145/3394277.3401846},
	author = {Qinglei Cao and Yu Pei and Kadir Akbudak and Aleksandr Mikhalev and George Bosilca and Hatem Ltaief and David Keyes and Jack Dongarra}
}
@conference {,
	title = {HAN: A Hierarchical AutotuNed Collective Communication Framework},
	booktitle = {IEEE Cluster Conference},
	year = {2020},
	month = {2020-09},
	publisher = {Best Paper Award, IEEE Computer Society Press},
	organization = {Best Paper Award, IEEE Computer Society Press},
	address = {Kobe, Japan},
	abstract = {High-performance computing (HPC) systems keep growing in scale and heterogeneity to satisfy the increasing computational need, and this brings new challenges to the design of MPI libraries, especially with regard to collective operations. To address these challenges, we present "HAN," a new hierarchical autotuned collective communication framework in Open MPI, which selects suitable homogeneous collective communication modules as submodules for each hardware level, uses collective operations from the submodules as tasks, and organizes these tasks to perform efficient hierarchical collective operations. With a task-based design, HAN can easily swap out submodules, while keeping tasks intact, to adapt to new hardware. This makes HAN suitable for the current platform and provides a strong and flexible support for future HPC systems. To provide a fast and accurate autotuning mechanism, we present a novel cost model based on benchmarking the tasks instead of a whole collective operation. This method drastically reduces tuning time, as the cost of tasks can be reused across different message sizes, and is more accurate than existing cost models. Our cost analysis suggests the autotuning component can find the optimal configuration in most cases. The evaluation of the HAN framework suggests our design significantly improves the default Open MPI and achieves decent speedups against state-of-the-art MPI implementations on tested applications.},
	author = {Xi Luo and Wei Wu and George Bosilca and Yu Pei and Qinglei Cao and Thananon Patinyasakdikul and Dong Zhong and Jack Dongarra}
}
@conference {,
	title = {Reservation and Checkpointing Strategies for Stochastic Jobs},
	booktitle = {34th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2020)},
	year = {2020},
	month = {2020-05},
	publisher = {IEEE Computer Society Press},
	organization = {IEEE Computer Society Press},
	address = {New Orleans, LA},
	author = {Ana Gainaru and Brice Goglin and Valentin Honor{\'e} and Padma Raghavan and Guillaume Pallez and Padma Raghavan and Yves Robert and Hongyang Sun}
}
@conference {,
	title = {Robustness of the Young/Daly Formula for Stochastic Iterative Applications},
	booktitle = {49th International Conference on Parallel Processing (ICPP 2020)},
	year = {2020},
	month = {2020-08},
	publisher = {ACM Press},
	organization = {ACM Press},
	address = {Edmonton, AB, Canada},
	author = {Yishu Du and Loris Marchal and Guillaume Pallez and Yves Robert}
}
@techreport {,
	title = {A Survey of Numerical Methods Utilizing Mixed Precision Arithmetic},
	journal = {SLATE Working Notes},
	number = {15, ICL-UT-20-08},
	year = {2020},
	month = {2020-07},
	publisher = {University of Tennessee},
	type = {SLATE Working Notes},
	author = {Ahmad Abdelfattah and Hartwig Anzt and Erik Boman and Erin Carson and Terry Cojean and Jack Dongarra and Mark Gates and Thomas Gruetzmacher and Nicholas J. Higham and Sherry Li and Neil Lindquist and Yang Liu and Jennifer Loe and Piotr Luszczek and Pratik Nayak and Sri Pranesh and Siva Rajamanickam and Tobias Ribizel and Barry Smith and Kasia Swirydowicz and Stephen Thomas and Stanimire Tomov and Yaohung Tsai and Ichitaro Yamazaki and Urike Meier Yang}
}
@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 {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 {1313,
	title = {Co-Scheduling HPC Workloads on Cache-Partitioned CMP Platforms},
	journal = {International Journal of High Performance Computing Applications},
	volume = {33},
	year = {2019},
	month = {2019-11},
	pages = {1221-1239},
	abstract = {With the recent advent of many-core architectures such as chip multiprocessors (CMPs), the number of processing units accessing a global shared memory is constantly increasing. Co-scheduling techniques are used to improve application throughput on such architectures, but sharing resources often generates critical interferences. In this article, we focus on the interferences in the last level of cache (LLC) and use the Cache Allocation Technology (CAT) recently provided by Intel to partition the LLC and give each co-scheduled application their own cache area. We consider m iterative HPC applications running concurrently and answer to the following questions: (i) How to precisely model the behavior of these applications on the cache-partitioned platform? and (ii) how many cores and cache fractions should be assigned to each application to maximize the platform efficiency? Here, platform efficiency is defined as maximizing the performance either globally, or as guaranteeing a fixed ratio of iterations per second for each application. Through extensive experiments using CAT, we demonstrate the impact of cache partitioning when multiple HPC applications are co-scheduled onto CMP platforms.},
	keywords = {cache partitioning, chip multiprocessor, co-scheduling, HPC application},
	doi = {https://doi.org/10.1177/1094342019846956},
	author = {Guillaume Aupy and Anne Benoit and Brice Goglin and Lo{\"\i}c Pottier and Yves Robert}
}
@conference {1451,
	title = {Evaluation of Programming Models to Address Load Imbalance on Distributed Multi-Core CPUs: A Case Study with Block Low-Rank Factorization},
	booktitle = {PAW-ATM Workshop at SC19},
	year = {2019},
	month = {2019-11},
	publisher = {ACM},
	organization = {ACM},
	address = {Denver, CO},
	author = {Yu Pei and George Bosilca and Ichitaro Yamazaki and Akihiro Ida and Jack Dongarra}
}
@conference {1395,
	title = {Give MPI Threading a Fair Chance: A Study of Multithreaded MPI Designs},
	booktitle = {IEEE Cluster},
	year = {2019},
	month = {2019-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Albuquerque, NM},
	abstract = {The Message Passing Interface (MPI) has been one of the most prominent programming paradigms in highperformance computing (HPC) for the past decade. Lately, with changes in modern hardware leading to a drastic increase in the number of processor cores, developers of parallel applications are moving toward more integrated parallel programming paradigms, where MPI is used along with other, possibly node-level, programming paradigms, or MPI+X. MPI+threads emerged as one of the favorite choices in HPC community, according to a survey of the HPC community. However, threading support in MPI comes with many compromises to the overall performance delivered, and, therefore, its adoption is compromised. This paper studies in depth the MPI multi-threaded implementation design in one of the leading MPI implementations, Open MPI, and expose some of the shortcomings of the current design. We propose, implement, and evaluate a new design of the internal handling of communication progress which allows for a significant boost in multi-threading performance, increasing the viability of MPI in the MPI+X programming paradigm.},
	keywords = {communication contention, MPI, thread},
	author = {Thananon Patinyasakdikul and David Eberius and George Bosilca and Nathan Hjelm}
}
@conference {1452,
	title = {Performance Analysis of Tile Low-Rank Cholesky Factorization Using PaRSEC Instrumentation Tools},
	booktitle = {Workshop on Programming and Performance Visualization Tools (ProTools 19) at SC19},
	year = {2019},
	month = {2019-11},
	publisher = {ACM},
	organization = {ACM},
	address = {Denver, CO},
	author = {Qinglei Cao and Yu Pei and Thomas Herault and Kadir Akbudak and Aleksandr Mikhalev and George Bosilca and Hatem Ltaief and David Keyes and Jack Dongarra}
}
@conference {1205,
	title = {ADAPT: An Event-Based Adaptive Collective Communication Framework},
	booktitle = {The 27th International Symposium on High-Performance Parallel and Distributed Computing (HPDC {\textquoteright}18)},
	year = {2018},
	month = {2018-06},
	publisher = {ACM Press},
	organization = {ACM Press},
	address = {Tempe, Arizona},
	abstract = {The increase in scale and heterogeneity of high-performance computing (HPC) systems predispose the performance of Message Passing Interface (MPI) collective communications to be susceptible to noise, and to adapt to a complex mix of hardware capabilities. The designs of state of the art MPI collectives heavily rely on synchronizations; these designs magnify noise across the participating processes, resulting in significant performance slowdown. Therefore, such design philosophy must be reconsidered to efficiently and robustly run on the large-scale heterogeneous platforms. In this paper, we present ADAPT, a new collective communication framework in Open MPI, using event-driven techniques to morph collective algorithms to heterogeneous environments. The core concept of ADAPT is to relax synchronizations, while mamtaining the minimal data dependencies of MPI collectives. To fully exploit the different bandwidths of data movement lanes in heterogeneous systems, we extend the ADAPT collective framework with a topology-aware communication tree. This removes the boundaries of different hardware topologies while maximizing the speed of data movements. We evaluate our framework with two popular collective operations: broadcast and reduce on both CPU and GPU clusters. Our results demonstrate drastic performance improvements and a strong resistance against noise compared to other state of the art MPI libraries. In particular, we demonstrate at least 1.3X and 1.5X speedup for CPU data and 2X and 10X speedup for GPU data using ADAPT event-based broadcast and reduce operations.},
	isbn = {9781450357852},
	doi = {10.1145/3208040.3208054},
	author = {Xi Luo and Wei Wu and George Bosilca and Thananon Patinyasakdikul and Linnan Wang and Jack Dongarra}
}
@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 {1198,
	title = {Co-Scheduling Amdhal Applications on Cache-Partitioned Systems},
	journal = {International Journal of High Performance Computing Applications},
	volume = {32},
	year = {2018},
	month = {2018-01},
	pages = {123{\textendash}138},
	abstract = {Cache-partitioned architectures allow subsections of the shared last-level cache (LLC) to be exclusively reserved for some applications. This technique dramatically limits interactions between applications that are concurrently executing on a multicore machine. Consider n applications that execute concurrently, with the objective to minimize the makespan, defined as the maximum completion time of the n applications. Key scheduling questions are as follows: (i) which proportion of cache and (ii) how many processors should be given to each application? In this article, we provide answers to (i) and (ii) for Amdahl applications. Even though the problem is shown to be NP-complete, we give key elements to determine the subset of applications that should share the LLC (while remaining ones only use their smaller private cache). Building upon these results, we design efficient heuristics for Amdahl applications. Extensive simulations demonstrate the usefulness of co-scheduling when our efficient cache partitioning strategies are deployed.},
	keywords = {cache partitioning, co-scheduling, complexity results},
	doi = {https://doi.org/10.1177/1094342017710806},
	author = {Guillaume Aupy and Anne Benoit and Sicheng Dai and Lo{\"\i}c Pottier and Padma Raghavan and Yves Robert and Manu Shantharam}
}
@conference {1217,
	title = {Co-Scheduling HPC Workloads on Cache-Partitioned CMP Platforms},
	booktitle = {Cluster 2018},
	year = {2018},
	month = {2018-09},
	publisher = {IEEE Computer Society Press},
	organization = {IEEE Computer Society Press},
	address = {Belfast, UK},
	author = {Guillaume Aupy and Anne Benoit and Brice Goglin and Lo{\"\i}c Pottier and Yves Robert}
}
@inproceedings {1259,
	title = {The Design of Fast and Energy-Efficient Linear Solvers: On the Potential of Half-Precision Arithmetic and Iterative Refinement Techniques},
	journal = {International Conference on Computational Science (ICCS 2018)},
	volume = {10860},
	year = {2018},
	month = {2018-06},
	pages = {586{\textendash}600},
	publisher = {Springer},
	address = {Wuxi, China},
	abstract = {As parallel computers approach exascale, power efficiency in high-performance computing (HPC) systems is of increasing concern. Exploiting both the hardware features and algorithms is an effective solution to achieve power efficiency, and to address the energy constraints in modern and future HPC systems. In this work, we present a novel design and implementation of an energy-efficient solution for dense linear systems of equations, which are at the heart of large-scale HPC applications. The proposed energy-efficient linear system solvers are based on two main components: (1) iterative refinement techniques, and (2) reduced-precision computing features in modern accelerators and coprocessors. While most of the energy efficiency approaches aim to reduce the consumption with a minimal performance penalty, our method improves both the performance and the energy efficiency. Compared to highly-optimized linear system solvers, our kernels deliver the same accuracy solution up to   2{\texttimes}  faster and reduce the energy consumption up to half on Intel Knights Landing (KNL) architectures. By efficiently using the Tensor Cores available in the NVIDIA V100 PCIe GPUs, the speedups can be up to   4{\texttimes} , with more than 80\% reduction in the energy consumption.},
	doi = {https://doi.org/10.1007/978-3-319-93698-7_45},
	url = {https://rdcu.be/bcKSC},
	author = {Azzam Haidar and Ahmad Abdelfattah and Mawussi Zounon and Panruo Wu and Srikara Pranesh and Stanimire Tomov and Jack Dongarra}
}
@inproceedings {1306,
	title = {Evaluating Contexts in OpenSHMEM-X Reference Implementation},
	journal = {OpenSHMEM and Related Technologies. Big Compute and Big Data Convergence},
	year = {2018},
	pages = {50{\textendash}62},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {Many-core processors are now ubiquitous in supercomputing. This evolution pushes toward the adoption of mixed models in which cores are exploited with threading models (and related programming abstractions, such as OpenMP), while communication between distributed memory domains employ a communication Application Programming Interface (API). OpenSHMEM is a partitioned global address space communication specification that exposes one-sided and synchronization operations. As the threaded semantics of OpenSHMEM are being fleshed out by its standardization committee, it is important to assess the soundness of the proposed concepts. This paper implements and evaluate the {\textquoteleft}{\textquoteleft}context{\textquoteright}{\textquoteright} extension in relation to threaded operations. We discuss the implementation challenges of the context and the associated API in OpenSHMEM-X. We then evaluate its performance in threaded situations on the Infiniband network using micro-benchmarks and the Random Access benchmark and see that adding communication contexts significantly improves message rate achievable by the executing multi-threaded PEs.},
	isbn = {978-3-319-73814-7},
	doi = {https://doi.org/10.1007/978-3-319-73814-7_4},
	author = {Aurelien Bouteiller and Pophale, Swaroop and Swen Boehm and Baker, Matthew B. and Manjunath Gorentla Venkata},
	editor = {Manjunath Gorentla Venkata and Imam, Neena and Pophale, Swaroop}
}
@techreport {1207,
	title = {Initial Integration and Evaluation of SLATE Parallel BLAS in LATTE},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-18-07},
	year = {2018},
	month = {2018-06},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	author = {Asim YarKhan and Gerald Ragghianti and Jack Dongarra and Marc Cawkwell and Danny Perez and Arthur Voter}
}
@article {1219,
	title = {Optimization and Performance Evaluation of the IDR Iterative Krylov Solver on GPUs},
	journal = {The International Journal of High Performance Computing Applications},
	volume = {32},
	number = {2},
	year = {2018},
	month = {2018-03},
	pages = {220{\textendash}230},
	abstract = {In this paper, we present an optimized GPU implementation for the induced dimension reduction algorithm. We improve data locality, combine it with an efficient sparse matrix vector kernel, and investigate the potential of overlapping computation with communication as well as the possibility of concurrent kernel execution. A comprehensive performance evaluation is conducted using a suitable performance model. The analysis reveals efficiency of up to 90\%, which indicates that the implementation achieves performance close to the theoretically attainable bound.},
	keywords = {co-design, gpu, Induced dimension reduction (IDR), kernel fusion, kernel overlap, roofline performance model},
	doi = {https://doi.org/10.1177/1094342016646844},
	author = {Hartwig Anzt and Moritz Kreutzer and Eduardo Ponce and Gregory D. Peterson and Gerhard Wellein and Jack Dongarra}
}
@conference {1216,
	title = {A Performance Model to Execute Workflows on High-Bandwidth Memory Architectures},
	booktitle = {The 47th International Conference on Parallel Processing (ICPP 2018)},
	year = {2018},
	month = {2018-08},
	publisher = {IEEE Computer Society Press},
	organization = {IEEE Computer Society Press},
	address = {Eugene, OR},
	abstract = {This work presents a realistic performance model to execute scientific workflows on high-bandwidth memory architectures such as the Intel Knights Landing. We provide a detailed analysis of the execution time on such platforms, taking into account transfers from both fast and slow memory and their overlap with computations. We discuss several scheduling and mapping strategies: not only tasks must be assigned to computing resource, but also one has to decide which fraction of input and output data will reside in fast memory, and which will have to stay in slow memory. Extensive simulations allow us to assess the impact of the mapping strategies on performance. We also conduct actual experiments for a simple 1D Gauss-Seidel kernel, which assess the accuracy of the model and further demonstrate the importance of a tuned memory management. Altogether, our model and results lay the foundations for further studies and experiments on dual-memory systems.},
	author = {Anne Benoit and Swann Perarnau and Lo{\"\i}c Pottier and Yves Robert}
}
@article {1231,
	title = {A Survey of MPI Usage in the US Exascale Computing Project},
	journal = {Concurrency Computation: Practice and Experience},
	year = {2018},
	month = {2018-09},
	type = {Special Issue},
	abstract = {The Exascale Computing Project (ECP) is currently the primary effort in theUnited States focused on developing {\textquotedblleft}exascale{\textquotedblright} levels of computing capabilities, including hardware, software, and applications. In order to obtain amore thorough understanding of how the software projects under the ECPare using, and planning to use theMessagePassing Interface (MPI), and help guide the work of our own project within the ECP, we created a survey.Of the 97 ECP projects active at the time the survey was distributed, we received 77 responses, 56 of which reported that their projects were usingMPI. This paper reports the results of that survey for the benefit of the broader community of MPI developers.},
	keywords = {exascale, MPI},
	doi = {https://doi.org/10.1002/cpe.4851},
	author = {David E. Bernholdt and Swen Boehm and George Bosilca and Manjunath Gorentla Venkata and Ryan E. Grant and Thomas Naughton and Howard P. Pritchard and Martin Schulz and Geoffroy R. Vallee}
}
@techreport {1280,
	title = {Tensor Contraction on Distributed Hybrid Architectures using a Task-Based Runtime System},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-18-13},
	year = {2018},
	month = {2018-12},
	publisher = {University of Tennessee},
	abstract = {The needs for predictive simulation of electronic structure in chemistry and materials science calls for fast/reduced-scaling formulations of quantum n-body methods that replace the traditional dense tensors with element-, block-, rank-, and block-rank-sparse (data-sparse) tensors. The resulting, highly irregular data structures are a poor match to imperative, bulk-synchronous parallel programming style due to the dynamic nature of the problem and to the lack of clear domain decomposition to guarantee a fair load-balance. TESSE runtime and the associated programming model aim to support performance-portable composition of applications involving irregular and dynamically changing data. In this paper we report an implementation of irregular dense tensor contraction in a paradigmatic electronic structure application based on the TESSE extension of PaRSEC, a distributed hybrid task runtime system, and analyze the resulting performance on a distributed memory cluster of multi-GPU nodes. Unprecedented strong scaling and promising efficiency indicate a viable future for task-based programming of complete production-quality reduced scaling models of electronic structure.},
	author = {George Bosilca and Damien Genet and Robert Harrison and Thomas Herault and Mohammad Mahdi Javanmard and Chong Peng and Edward Valeev}
}
@conference {1169,
	title = {Autotuning Batch Cholesky Factorization in CUDA with Interleaved Layout of Matrices},
	booktitle = {Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
	year = {2017},
	month = {2017-06},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Orlando, FL},
	abstract = {Batch matrix operations address the case of solving the same linear algebra problem for a very large number of very small matrices. In this paper, we focus on implementing the batch Cholesky factorization in CUDA, in single precision arithmetic, for NVIDIA GPUs. Specifically, we look into the benefits of using noncanonical data layouts, where consecutive memory locations store elements with the same row and column index in a set of consecutive matrices. We discuss a number of different implementation options and tuning parameters. We demonstrate superior performance to traditional implementations for the case of very small matrices.},
	keywords = {batch computation, Cholesky Factorization, data layout, GPU computing, numerical linear algebra},
	doi = {10.1109/IPDPSW.2017.18},
	author = {Mark Gates and Jakub Kurzak and Piotr Luszczek and Yu Pei and Jack Dongarra}
}
@conference {1094,
	title = {Co-Scheduling Algorithms for Cache-Partitioned Systems},
	booktitle = {19th Workshop on Advances in Parallel and Distributed Computational Models},
	year = {2017},
	month = {2017-05},
	publisher = {IEEE Computer Society Press},
	organization = {IEEE Computer Society Press},
	address = {Orlando, FL},
	abstract = {Cache-partitioned architectures allow subsections of the shared last-level cache (LLC) to be exclusively reserved for some applications. This technique dramatically limits interactions between applications that are concurrently executing on a multicore machine. Consider n applications that execute concurrently, with the objective to minimize the makespan, defined as the maximum completion time of the n applications. Key scheduling questions are: (i) which proportion of cache and (ii) how many processors should be given to each application? Here, we assign rational numbers of processors to each application, since they can be shared across applications through multi-threading. In this paper, we provide answers to (i) and (ii) for perfectly parallel applications. Even though the problem is shown to be NP-complete, we give key elements to determine the subset of applications that should share the LLC (while remaining ones only use their smaller private cache). Building upon these results, we design efficient heuristics for general applications. Extensive simulations demonstrate the usefulness of co-scheduling when our efficient cache partitioning strategies are deployed.},
	keywords = {Computational modeling, Degradation, Interference, Mathematical model, Program processors, Supercomputers, Throughput},
	doi = {10.1109/IPDPSW.2017.60},
	author = {Guillaume Aupy and Anne Benoit and Lo{\"\i}c Pottier and Padma Raghavan and Yves Robert and Manu Shantharam}
}
@inbook {1384,
	title = {Performance Analysis and Debugging Tools at Scale},
	booktitle = {Exascale Scientific Applications: Scalability and Performance Portability},
	year = {2017},
	month = {2017-11},
	pages = {17-50},
	publisher = {Chapman \& Hall / CRC Press},
	organization = {Chapman \& Hall / CRC Press},
	chapter = {2},
	abstract = {This chapter explores present-day challenges and those likely to arise as new hardware and software technologies are introduced on the path to exascale. It covers some of the underlying hardware, software, and techniques that enable tools and debuggers. Performance tools and debuggers are critical components that enable computational scientists to fully exploit the computing power of While high-performance computing systems. Instrumentation is the insertion of code to perform measurement in a program. It is vital step in performance analysis, especially for parallel programs. The essence of a debugging tool is enabling observation, exploration, and control of program state, such that a developer can, for example, verify that what is currently occurring correlates to what is intended. The increased complexity and volume of performance and debugging data likely to be seen on exascale systems risks overwhelming tool users. Tools and debuggers may need to develop advanced techniques such as automated filtering and analysis to reduce the complexity seen by the user.},
	isbn = {9781315277400},
	doi = {https://doi.org/10.1201/b21930},
	author = {Scott Parker and John Mellor-Crummey and Dong H. Ahn and Heike Jagode and Holger Brunst and Sameer Shende and Allen D. Malony and David DelSignore and Ronny Tschuter and Ralph Castain and Kevin Harms and Philip Carns and Ray Loy and Kalyan Kumaran}
}
@article {1091,
	title = {Resilient Co-Scheduling of Malleable Applications},
	journal = {International Journal of High Performance Computing Applications (IJHPCA)},
	year = {2017},
	month = {2017-05},
	abstract = {Recently, the benefits of co-scheduling several applications have been demonstrated in a fault-free context, both in terms of performance and energy savings. However, large-scale computer systems are confronted by frequent failures, and resilience techniques must be employed for large applications to execute efficiently. Indeed, failures may create severe imbalance between applications and significantly degrade performance. In this article, we aim at minimizing the expected completion time of a set of co-scheduled applications. We propose to redistribute the resources assigned to each application upon the occurrence of failures, and upon the completion of some applications, in order to achieve this goal. First, we introduce a formal model and establish complexity results. The problem is NP-complete for malleable applications, even in a fault-free context. Therefore, we design polynomial-time heuristics that perform redistributions and account for processor failures. A fault simulator is used to perform extensive simulations that demonstrate the usefulness of redistribution and the performance of the proposed heuristics.},
	keywords = {co-scheduling, complexity results, heuristics, Redistribution, resilience, simulations},
	doi = {10.1177/1094342017704979},
	author = {Anne Benoit and Lo{\"\i}c Pottier and Yves Robert}
}
@conference {1127,
	title = {Using Software-Based Performance Counters to Expose Low-Level Open MPI Performance Information},
	booktitle = {EuroMPI},
	year = {2017},
	month = {2017-09},
	publisher = {ACM},
	organization = {ACM},
	address = {Chicago, IL},
	abstract = {This paper details the implementation and usage of software-based performance counters to understand the performance of a particular implementation of the MPI standard, Open MPI.  Such counters can expose intrinsic features of the software stack that are not available otherwise in a generic and portable way. The PMPI-interface is useful for instrumenting MPI applications at a user level, however it is insufficient for providing meaningful internal MPI performance details.  While the Peruse interface provides more detailed information on state changes within Open MPI, it has not seen widespread adoption.  We introduce a simple low-level approach that instruments the Open MPI code at key locations to provide fine-grained MPI performance metrics.  We evaluate the overhead associated with adding these counters to Open MPI as well as their use in determining bottlenecks and areas for improvement both in user code and the MPI implementation itself.},
	keywords = {MPI, Performance Counters, Profiling, Tools},
	isbn = {978-1-4503-4849-2/17/09},
	doi = {https://doi.org/10.1145/3127024.3127039},
	url = {https://dl.acm.org/citation.cfm?id=3127024},
	author = {David Eberius and Thananon Patinyasakdikul and George Bosilca}
}
@conference {939,
	title = {Heterogeneous Streaming},
	booktitle = {The Sixth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), IPDPS 2016},
	year = {2016},
	month = {2016-05},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Chicago, IL},
	abstract = {This paper introduces a new heterogeneous streaming library called hetero Streams (hStreams). We show how a simple FIFO streaming model can be applied to heterogeneous systems that include manycore coprocessors and multicore CPUs. This model supports concurrency across nodes, among tasks within a node, and between data transfers and computation. We give examples for different approaches, show how the implementation can be layered, analyze overheads among layers, and apply those models to parallelize applications using simple, intuitive interfaces. We compare the features and versatility of hStreams, OpenMP, CUDA Streams1 and OmpSs. We show how the use of hStreams makes it easier for scientists to identify tasks and easily expose concurrency among them, and how it enables tuning experts and runtime systems to tailor execution for different heterogeneous targets. Practical application examples are taken from the field of numerical linear algebra, commercial structural simulation software, and a seismic processing application.},
	keywords = {plasma},
	author = {Chris J. Newburn and Gaurav Bansal and Michael Wood and Luis Crivelli and Judit Planas and Alejandro Duran and Paulo Souza and Leonardo Borges and Piotr Luszczek and Stanimire Tomov and Jack Dongarra and Hartwig Anzt and Mark Gates and Azzam Haidar and Yulu Jia and Khairul Kabir and Ichitaro Yamazaki and Jesus Labarta}
}
@inproceedings {1308,
	title = {Surviving Errors with OpenSHMEM},
	journal = {OpenSHMEM and Related Technologies. Enhancing OpenSHMEM for Hybrid Environments},
	year = {2016},
	pages = {66{\textendash}81},
	publisher = {Springer International Publishing},
	address = {Baltimore, MD, USA},
	abstract = {Unexpected error conditions stem from a variety of underlying causes, including resource exhaustion, network failures, hardware failures, or program errors. As the scale of HPC systems continues to grow, so does the probability of encountering a condition that causes a failure; meanwhile, error recovery and run-through failure management are becoming mature, and interoperable HPC programming paradigms are beginning to feature advanced error management. As a result from these developments, it becomes increasingly desirable to gracefully handle error conditions in OpenSHMEM. In this paper, we present the design and rationale behind an extension of the OpenSHMEM API that can (1) notify user code of unexpected erroneous conditions, (2) permit customized user response to errors without incurring overhead on an error-free execution path, (3) propagate the occurence of an error condition to all Processing Elements, and (4) consistently close the erroneous epoch in order to resume the application.},
	isbn = {978-3-319-50995-2},
	author = {Aurelien Bouteiller and George Bosilca and Manjunath Gorentla Venkata},
	editor = {Manjunath Gorentla Venkata and Imam, Neena and Pophale, Swaroop and Mintz, Tiffany M.}
}
@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 {913,
	title = {GPU-accelerated Co-design of Induced Dimension Reduction: Algorithmic Fusion and Kernel Overlap},
	booktitle = {2nd International Workshop on Hardware-Software Co-Design for High Performance Computing},
	year = {2015},
	month = {2015-11},
	publisher = {ACM},
	organization = {ACM},
	address = {Austin, TX},
	abstract = {In this paper we present an optimized GPU co-design of the Induced Dimension Reduction (IDR) algorithm for solving linear systems. Starting from a baseline implementation based on the generic BLAS routines from the MAGMA software library, we apply optimizations
that are based on kernel fusion and kernel overlap. Runtime experiments are used to investigate the benefit of the distinct optimization techniques for different variants of the IDR algorithm. A comparison to the reference implementation reveals that the interplay between them can succeed in cutting the overall runtime by up to about one third.},
	author = {Hartwig Anzt and Eduardo Ponce and Gregory D. Peterson and Jack Dongarra}
}
@techreport {872,
	title = {Practical Scalable Consensus for Pseudo-Synchronous Distributed Systems: Formal Proof},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-15-01},
	year = {2015},
	month = {2015-04},
	author = {Thomas Herault and Aurelien Bouteiller and George Bosilca and Marc Gamell and Keita Teranishi and Manish Parashar and Jack Dongarra}
}
@conference {889,
	title = {Practical Scalable Consensus for Pseudo-Synchronous Distributed Systems},
	booktitle = {The International Conference for High Performance Computing, Networking, Storage and Analysis (SC15)},
	year = {2015},
	month = {2015-11},
	publisher = {ACM},
	organization = {ACM},
	address = {Austin, TX},
	abstract = {The ability to consistently handle faults in a distributed environment requires, among a small set of basic routines, an agreement algorithm allowing surviving entities to reach a consensual decision between a bounded set of volatile resources. This paper presents an algorithm that implements an Early Returning Agreement (ERA) in pseudo-synchronous systems, which optimistically allows a process to resume its activity while guaranteeing strong progress. We prove the correctness of our ERA algorithm, and expose its logarithmic behavior, which is an extremely desirable property for any algorithm which targets future exascale platforms. We detail a practical implementation of this consensus algorithm in the context of an MPI library, and evaluate both its efficiency and scalability through a set of benchmarks and two fault tolerant scientific applications.},
	author = {Thomas Herault and Aurelien Bouteiller and George Bosilca and Marc Gamell and Keita Teranishi and Manish Parashar and Jack Dongarra}
}
@inproceedings {1309,
	title = {UCX: An Open Source Framework for HPC Network APIs and Beyond},
	journal = {2015 IEEE 23rd Annual Symposium on High-Performance Interconnects},
	year = {2015},
	month = {Aug},
	pages = {40-43},
	publisher = {IEEE},
	address = {Santa Clara, CA, USA},
	abstract = {This paper presents Unified Communication X (UCX), a set of network APIs and their implementations for high throughput computing. UCX comes from the combined effort of national laboratories, industry, and academia to design and implement a high-performing and highly-scalable network stack for next generation applications and systems. UCX design provides the ability to tailor its APIs and network functionality to suit a wide variety of application domains and hardware. We envision these APIs to satisfy the networking needs of many programming models such as Message Passing Interface (MPI), OpenSHMEM, Partitioned Global Address Space (PGAS) languages, task-based paradigms and I/O bound applications. To evaluate the design we implement the APIs and protocols, and measure the performance of overhead-critical network primitives fundamental for implementing many parallel programming models and system libraries. Our results show that the latency, bandwidth, and message rate achieved by the portable UCX prototype is very close to that of the underlying driver. With UCX, we achieved a message exchange latency of 0.89 us, a bandwidth of 6138.5 MB/s, and a message rate of 14 million messages per second. As far as we know, this is the highest bandwidth and message rate achieved by any network stack (publicly known) on this hardware.},
	keywords = {application program interfaces, Bandwidth, Electronics packaging, Hardware, high throughput computing, highly-scalable network stack, HPC, HPC network APIs, I/O bound applications, Infiniband, input-output programs, Libraries, Memory management, message passing, message passing interface, Middleware, MPI, open source framework, OpenSHMEM, parallel programming, parallel programming models, partitioned global address space languages, PGAS, PGAS languages, Programming, protocols, public domain software, RDMA, system libraries, task-based paradigms, UCX, Unified Communication X},
	isbn = {978-1-4673-9160-3},
	doi = {10.1109/HOTI.2015.13},
	author = {P. Shamis and Manjunath Gorentla Venkata and M. Graham Lopez and M. B. Baker and O. Hernandez and Y. Itigin and M. Dubman and G. Shainer and R. L. Graham and L. Liss and Y. Shahar and S. Potluri and D. Rossetti and D. Becker and D. Poole and C. Lamb and S. Kumar and C. Stunkel and George Bosilca and Aurelien Bouteiller}
}
@article {815,
	title = {Communication-Avoiding Symmetric-Indefinite Factorization},
	journal = {SIAM Journal on Matrix Analysis and Application},
	volume = {35},
	year = {2014},
	month = {2014-07},
	pages = {1364-1406},
	abstract = {We describe and analyze a novel symmetric triangular factorization algorithm. The algorithm is essentially a block version of Aasen{\textquoteright}s triangular tridiagonalization. It factors a dense symmetric matrix A as the product A = P LT L T P T where P is a permutation matrix, L is lower triangular, and T is block tridiagonal and banded. The algorithm is the first symmetric-indefinite communication-avoiding factorization: it performs an asymptotically optimal amount of communication in a two-level memory hierarchy for almost any cache-line size. Adaptations of the algorithm to parallel computers are likely to be communication efficient as well; one such adaptation has been recently published. The current paper describes the algorithm, proves that it is numerically stable, and proves that it is communication optimal.},
	keywords = {plasma},
	author = {Grey Ballard and Dulceneia Becker and James Demmel and Jack Dongarra and Alex Druinsky and I Peled and Oded Schwartz and Sivan Toledo and Ichitaro Yamazaki}
}
@article {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}
}
@article {icl:691,
	title = {Implementing a Blocked Aasen{\textquoteright}s Algorithm with a Dynamic Scheduler on Multicore Architectures},
	journal = {IPDPS 2013 (submitted)},
	year = {2013},
	month = {2013-00},
	address = {Boston, MA},
	abstract = {Factorization of a dense symmetric indefinite matrix is a key computational kernel in many scientific and engineering simulations. However, there is no scalable factorization algorithm that takes advantage of the symmetry and guarantees numerical stability through pivoting at the same time. This is because such an algorithm exhibits many of the fundamental challenges in parallel programming like irregular data accesses and irregular task dependencies. In this paper, we address these challenges in a tiled implementation of a blocked Aasen{\textquoteright}s algorithm using a dynamic scheduler. To fully exploit the limited parallelism in this left-looking algorithm, we study several performance enhancing techniques; e.g., parallel reduction to update a panel, tall-skinny LU factorization algorithms to factorize the panel, and a parallel implementation of symmetric pivoting. Our performance results on up to 48 AMD Opteron processors demonstrate that our implementation obtains speedups of up to 2.8 over MKL, while losing only one or two digits in the computed residual norms.},
	author = {Ichitaro Yamazaki and Dulceneia Becker and Jack Dongarra and Alex Druinsky and I. Peled and Sivan Toledo and Grey Ballard and James Demmel and Oded Schwartz}
}
@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}
}
@inproceedings {icl:679,
	title = {A Checkpoint-on-Failure Protocol for Algorithm-Based Recovery in Standard MPI},
	journal = {18th International European Conference on Parallel and Distributed Computing (Euro-Par 2012) (Best Paper Award)},
	year = {2012},
	month = {2012-08},
	publisher = {Springer-Verlag},
	address = {Rhodes, Greece},
	author = {Wesley Bland and Peng Du and Aurelien Bouteiller and Thomas Herault and George Bosilca and Jack Dongarra},
	editor = {Christos Kaklamanis and Theodore Papatheodorou and Paul Spirakis}
}
@article {icl:725,
	title = {From CUDA to OpenCL: Towards a Performance-portable Solution for Multi-platform GPU Programming},
	journal = {Parallel Computing},
	volume = {38},
	number = {8},
	year = {2012},
	month = {2012-08},
	pages = {391-407},
	author = {Peng Du and Rick Weber and Piotr Luszczek and Stanimire Tomov and Gregory D. Peterson and Jack Dongarra}
}
@article {icl:705,
	title = {Looking Back at Dense Linear Algebra Software},
	journal = {Perspectives on Parallel and Distributed Processing: Looking Back and What{\textquoteright}s Ahead (to appear)},
	year = {2012},
	month = {2012-00},
	author = {Piotr Luszczek and Jakub Kurzak and Jack Dongarra},
	editor = {Viktor K. Prasanna and Yves Robert and Per Stenstr{\"o}m}
}
@article {icl:686,
	title = {Power Aware Computing on GPUs},
	journal = {SAAHPC {\textquoteright}12 (Best Paper Award)},
	year = {2012},
	month = {2012-07},
	address = {Argonne, IL},
	keywords = {magma},
	author = {Kiran Kasichayanula and Dan Terpstra and Piotr Luszczek and Stanimire Tomov and Shirley Moore and Gregory D. Peterson}
}
@article {icl:587,
	title = {Changes in Dense Linear Algebra Kernels - Decades Long Perspective},
	journal = {in Solving the Schrodinger Equation: Has everything been tried? (to appear)},
	year = {2011},
	month = {2011-00},
	publisher = {Imperial College Press},
	author = {Piotr Luszczek and Jakub Kurzak and Jack Dongarra},
	editor = {P. Popular}
}
@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}
}
@conference {icl:633,
	title = {Parallel Performance Measurement of Heterogeneous Parallel Systems with GPUs},
	booktitle = {International Conference on Parallel Processing (ICPP{\textquoteright}11)},
	year = {2011},
	month = {2011-09},
	publisher = {ACM},
	organization = {ACM},
	address = {Taipei, Taiwan},
	abstract = {The power of GPUs is giving rise to heterogeneous parallel computing, with new demands on programming environments, runtime systems, and tools to deliver high-performing applications. This paper studies the problems associated with performance measurement of heterogeneous machines with GPUs. A heterogeneous computation model and alternative host-GPU measurement approaches are discussed to set the stage for reporting new capabilities for heterogeneous parallel performance measurement in three leading HPC tools: PAPI, Vampir, and the TAU Performance System. Our work leverages the new CUPTI tool support in NVIDIA{\textquoteright}s CUDA device library. Heterogeneous benchmarks from the SHOC suite are used to demonstrate the measurement methods and tool support.},
	keywords = {magma, mumi, papi},
	isbn = {978-0-7695-4510-3},
	doi = {10.1109/ICPP.2011.71},
	author = {Allen D. Malony and Scott Biersdorff and Sameer Shende and Heike Jagode and Stanimire Tomov and Guido Juckeland and Robert Dietrich and Duncan Poole and Christopher Lamb}
}
@article {icl:677,
	title = {QCG-OMPI: MPI Applications on Grids.},
	journal = {Future Generation Computer Systems},
	volume = {27},
	number = {4},
	year = {2011},
	month = {2011-01},
	pages = {435-369},
	author = {Emmanuel Agullo and Camille Coti and Thomas Herault and Julien Langou and Sylvain Peyronnet and A. Rezmerita and Franck Cappello and Jack Dongarra}
}
@article {icl:555,
	title = {Constructing Resiliant Communication Infrastructure for Runtime Environments in Advances in Parallel Computing},
	journal = {Advances in Parallel Computing - Parallel Computing: From Multicores and GPU{\textquoteright}s to Petascale},
	volume = {19},
	year = {2010},
	pages = {441-451},
	doi = {10.3233/978-1-60750-530-3-441},
	author = {George Bosilca and Camille Coti and Thomas Herault and Pierre Lemariner and Jack Dongarra},
	editor = {Barbara Chapman and Frederic Desprez and Gerhard R. Joubert and Alain Lichnewsky and Frans Peters and T. Priol}
}
@article {icl:574,
	title = {QCG-OMPI: MPI Applications on Grids},
	journal = {Future Generation Computer Systems},
	volume = {27},
	number = {4},
	year = {2010},
	month = {2010-03},
	pages = {357-369},
	author = {Emmanuel Agullo and Camille Coti and Thomas Herault and Julien Langou and Sylvain Peyronnet and A. Rezmerita and Franck Cappello and Jack Dongarra}
}
@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:544,
	title = {Using multiple levels of parallelism to enhance the performance of domain decomposition solvers},
	journal = {Parallel Computing},
	volume = {36},
	number = {5-6},
	year = {2010},
	month = {2010-00},
	pages = {285-296},
	publisher = {Elsevier journals},
	author = {Luc Giraud and Azzam Haidar and Stephane Pralet},
	editor = {Costas Bekas and Pascua D{\textquoteright}Ambra and Ananth Grama and Yousef Saad and Petko Yanev}
}
@article {icl:480,
	title = {Impact of Quad-core Cray XT4 System and Software Stack on Scientific Computation},
	journal = {Euro-Par 2009, Lecture Notes in Computer Science},
	volume = {5704/2009},
	year = {2009},
	month = {2009-08},
	pages = {334-344},
	publisher = {Springer Berlin / Heidelberg},
	address = {Delft, The Netherlands},
	keywords = {test},
	author = {Sadaf Alam and Richard F. Barrett and Heike Jagode and J. A. Kuehn and Steve W. Poole and R. Sankaran}
}
@inproceedings {icl:499,
	title = {Making Performance Analysis and Tuning Part of the Software Development Cycle},
	journal = {Proceedings of DoD HPCMP UGC 2009},
	year = {2009},
	month = {2009-06},
	publisher = {IEEE},
	address = {San Diego, CA},
	author = {Ricardo Portillo and Patricia J. Teller and David Cronk and Shirley Moore}
}
@inproceedings {icl:503,
	title = {MPI-aware Compiler Optimizations for Improving Communication-Computation Overlap},
	journal = {Proceedings of the 23rd annual International Conference on Supercomputing (ICS {\textquoteright}09)},
	year = {2009},
	month = {2009-06},
	pages = {316-325},
	publisher = {ACM},
	address = {Yorktown Heights, NY, USA},
	author = {Anthony Danalis and Lori Pollock and Martin Swany and John Cavazos}
}
@inproceedings {icl:440,
	title = {Exploring New Architectures in Accelerating CFD for Air Force Applications},
	journal = {Proceedings of the DoD HPCMP User Group Conference},
	year = {2008},
	month = {2008-01},
	address = {Seattle, Washington},
	keywords = {magma},
	author = {Jack Dongarra and Shirley Moore and Gregory D. Peterson and Stanimire Tomov and Jeff Allred and Vincent Natoli and David Richie}
}
@article {icl:409,
	title = {High Performance GridRPC Middleware},
	journal = {Recent developments in Grid Technology and Applications},
	year = {2008},
	month = {2008-00},
	publisher = {Nova Science Publishers},
	keywords = {netsolve},
	author = {Yves Caniou and Eddy Caron and Frederic Desprez and Hidemoto Nakada and Yoshio Tanaka and Keith Seymour},
	editor = {George A. Gravvanis and John P. Morrison and Hamid R. Arabnia and D. A. Power}
}
@article {icl:436,
	title = {The LINPACK Benchmark: Past, Present, and Future},
	journal = {Concurrency: Practice and Experience},
	volume = {15},
	year = {2008},
	month = {2008-00},
	pages = {803-820},
	keywords = {hpl},
	author = {Jack Dongarra and Piotr Luszczek and Antoine Petitet}
}
@inproceedings {icl:455,
	title = {Matrix Product on Heterogeneous Master Worker Platforms},
	journal = {2008 PPoPP Conference},
	year = {2008},
	month = {2008-01},
	address = {Salt Lake City, Utah},
	author = {Jack Dongarra and Jean-Francois Pineau and Yves Robert and Frederic Vivien}
}
@article {icl:504,
	title = {Revisiting Matrix Product on Master-Worker Platforms},
	journal = {International Journal of Foundations of Computer Science (IJFCS)},
	volume = {19},
	number = {6},
	year = {2008},
	month = {2008-12},
	pages = {1317-1336},
	author = {Jack Dongarra and Jean-Francois Pineau and Yves Robert and Zhiao Shi and Frederic Vivien}
}
@inproceedings {icl:412,
	title = {Usage of the Scalasca Toolset for Scalable Performance Analysis of Large-scale Parallel Applications},
	journal = {Proceedings of the 2nd International Workshop on Tools for High Performance Computing},
	year = {2008},
	month = {2008-01},
	pages = {157-167},
	publisher = {Springer},
	address = {Stuttgart, Germany},
	keywords = {point},
	author = {Felix Wolf and Brian Wylie and Erika Abraham and Wolfgang Frings and Karl F{\"u}rlinger and Markus Geimer and Marc-Andre Hermanns and Bernd Mohr and Shirley Moore and Matthias Pfeifer},
	editor = {Michael Resch and Rainer Keller and Valentin Himmler and Bettina Krammer and A Schulz}
}
@article {icl:360,
	title = {A Comparison of Application Performance Using Open MPI and Cray MPI},
	journal = {Cray User Group, CUG 2007},
	year = {2007},
	month = {2007-05},
	author = {Richard L. Graham and George Bosilca and Jelena Pjesivac{\textendash}Grbovic}
}
@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 {icl:359,
	title = {An Evaluation of Open MPI{\textquoteright}s Matching Transport Layer on the Cray XT},
	journal = {EuroPVM/MPI 2007},
	year = {2007},
	month = {2007-09},
	author = {Richard L. Graham and Ron Brightwell and Brian Barrett and George Bosilca and Jelena Pjesivac{\textendash}Grbovic}
}
@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}
}
@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}
}
@article {icl:371,
	title = {Revisiting Matrix Product on Master-Worker Platforms},
	journal = {International Journal of Foundations of Computer Science (IJFCS) (accepted)},
	year = {2007},
	month = {2007-00},
	author = {Jack Dongarra and Jean-Francois Pineau and Yves Robert and Zhiao Shi and Frederic Vivien}
}
@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}
}
@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:370,
	title = {Prospectus for the Next LAPACK and ScaLAPACK Libraries},
	journal = {PARA 2006},
	year = {2006},
	month = {2006-06},
	address = {Umea, Sweden},
	author = {James Demmel and Jack Dongarra and B. Parlett and William Kahan and Ming Gu and David Bindel and Yozo Hida and Xiaoye Li and Osni Marques and Jason E. Riedy and Christof Voemel and Julien Langou and Piotr Luszczek and Jakub Kurzak and Alfredo Buttari and Julien Langou and Stanimire Tomov}
}
@article {icl: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:278,
	title = {New Grid Scheduling and Rescheduling Methods in the GrADS Project},
	journal = {International Journal of Parallel Programming},
	volume = {33},
	number = {2},
	year = {2005},
	month = {2005-06},
	pages = {209-229},
	publisher = {Springer},
	keywords = {grads},
	author = {Francine Berman and Henri Casanova and Andrew Chien and Keith Cooper and Holly Dail and Anshuman Dasgupta and Wei Deng and Jack Dongarra and Lennart Johnsson and Ken Kennedy and Charles Koelbel and Bo Liu and Xu Liu and Anirban Mandal and Gabriel Marin and Mark Mazina and John Mellor-Crummey and Celso Mendes and A. Olugbile and Jignesh M. Patel and Dan Reed and Zhiao Shi and Otto Sievert and H. Xia and Asim YarKhan}
}
@inproceedings {icl: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}
}
@conference {icl:298,
	title = {Performance Profiling and Analysis of DoD Applications using PAPI and TAU},
	booktitle = {Proceedings of DoD HPCMP UGC 2005},
	year = {2005},
	month = {2005-06},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Nashville, TN},
	keywords = {papi},
	author = {Shirley Moore and David Cronk and Felix Wolf and Avi Purkayastha and Patricia J. Teller and Robert Araiza and Gabriela Aguilera and Jamie Nava}
}
@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}
}
@article {icl:244,
	title = {Self Adaptivity in Grid Computing},
	journal = {Concurrency and Computation: Practice and Experience, Special Issue: Grid Performance},
	volume = {17},
	number = {2-4},
	year = {2005},
	month = {2005-00},
	pages = {235-257},
	keywords = {netsolve, sans},
	author = {Sathish Vadhiyar and Jack Dongarra},
	editor = {John Gurd and Anthony Hey and Juri Papay and Graham Riley}
}
@inproceedings {icl:206,
	title = {Active Logistical State Management in the GridSolve/L},
	journal = {4th International Symposium on Cluster Computing and the Grid (CCGrid 2004)(submitted)},
	year = {2004},
	month = {2004-01},
	address = {Chicago, Illinois},
	keywords = {netsolve},
	author = {Micah Beck and Jack Dongarra and Jian Huang and Terry Moore and James Plank}
}
@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}
}
@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: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:168,
	title = {High Performance Computing for Computational Science},
	journal = {Lecture Notes in Computer Science},
	volume = {2565},
	year = {2003},
	month = {2003-01},
	publisher = {Springer-Verlag, Berlin},
	address = {VECPAR 2002, 5th International Conference June 26-28, 2002},
	author = {Jose Palma and Jack Dongarra and Vicente Hern{\'a}ndez},
	editor = {Antonio Augusto Sousa}
}
@inproceedings {icl:171,
	title = {Optimizing Performance and Reliability in Distributed Computing Systems Through Wide Spectrum Storage},
	journal = {Proceedings of the IPDPS 2003, NGS Workshop},
	year = {2003},
	month = {2003-01},
	pages = {209},
	address = {Nice, France},
	author = {James Plank and Micah Beck and Jack Dongarra and Rich Wolski and Henri Casanova}
}
@inproceedings {icl:158,
	title = {Scalable, Trustworthy Network Computing Using Untrusted Intermediaries: A Position Paper},
	journal = {DOE/NSF Workshop on New Directions in Cyber-Security in Large-Scale Networks: Development Obstacles},
	year = {2003},
	month = {2003-03},
	address = {National Conference Center - Landsdowne, Virginia},
	keywords = {netsolve},
	author = {Micah Beck and Jack Dongarra and Victor Eijkhout and Mike Langston and Terry Moore and James Plank}
}
@article {icl:119,
	title = {Adaptive Scheduling for Task Farming with Grid Middleware},
	journal = {International Journal of Supercomputer Applications and High-Performance Computing},
	volume = {13},
	number = {3},
	year = {2002},
	month = {2002-10},
	pages = {231-240},
	author = {Henri Casanova and Myung Ho Kim and James Plank and Jack Dongarra}
}
@article {icl:121,
	title = {Algorithmic Redistribution Methods for Block Cyclic Decompositions},
	journal = {IEEE Transactions on Parallel and Distributed Computing},
	volume = {10},
	number = {12},
	year = {2002},
	month = {2002-10},
	pages = {201-220},
	author = {Antoine Petitet and Jack Dongarra}
}
@inproceedings {icl:93,
	title = {Deploying Parallel Numerical Library Routines to Cluster Computing in a Self Adapting Fashion},
	journal = {Parallel Computing: Advances and Current Issues:Proceedings of the International Conference ParCo2001},
	year = {2002},
	month = {2002-01},
	publisher = {Imperial College Press},
	address = {London, England},
	keywords = {lfc, sans},
	author = {Kenneth Roche and Jack Dongarra},
	editor = {Gerhard R. Joubert and Almerica Murli and Frans Peters and Marco Vanneschi}
}
@inproceedings {icl:98,
	title = {Experiments with Scheduling Using Simulated Annealing in a Grid Environment},
	journal = {Grid Computing - GRID 2002, Third International Workshop},
	volume = {2536},
	year = {2002},
	month = {2002-11},
	pages = {232-242},
	publisher = {Springer},
	address = {Baltimore, MD},
	keywords = {grads},
	author = {Asim YarKhan and Jack Dongarra},
	editor = {Manish Parashar}
}
@inproceedings {icl:122,
	title = {The Internet BackPlane Protocol: A Study in Resource Sharing},
	journal = {Proceedings of the second IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID 2002)},
	year = {2002},
	month = {2002-10},
	address = {Berlin, Germany},
	keywords = {ftmpi},
	author = {Alessandro Bassi and Micah Beck and Graham Fagg and Terry Moore and James Plank and Martin Swany and Rich Wolski}
}
@article {icl:101,
	title = {Middleware for the Use of Storage in Communication},
	journal = {Parallel Computing},
	volume = {28},
	number = {12},
	year = {2002},
	month = {2002-08},
	pages = {1773-1788},
	keywords = {netsolve},
	author = {Micah Beck and Dorian Arnold and Alessandro Bassi and Francine Berman and Henri Casanova and Jack Dongarra and Terry Moore and Graziano Obertelli and James Plank and Martin Swany and Sathish Vadhiyar and Rich Wolski}
}
@inproceedings {icl:187,
	title = {Overview of GridRPC: A Remote Procedure Call API for Grid Computing},
	journal = {Proceedings of the Third International Workshop on Grid Computing},
	year = {2002},
	month = {2002-01},
	pages = {274-278},
	author = {Keith Seymour and Hidemoto Nakada and Satoshi Matsuoka and Jack Dongarra and Craig Lee and Henri Casanova},
	editor = {Manish Parashar}
}
@article {icl:125,
	title = {An Updated Set of Basic Linear Algebra Subprograms (BLAS)},
	journal = {ACM Transactions on Mathematical Software},
	volume = {28},
	number = {2},
	year = {2002},
	month = {2002-12},
	pages = {135-151},
	doi = {10.1145/567806.567807},
	author = {Susan Blackford and James Demmel and Jack Dongarra and Iain Duff and Sven Hammarling and Greg Henry and Michael Heroux and Linda Kaufman and Andrew Lumsdaine and Antoine Petitet and Roldan Pozo and Karin Remington and Clint Whaley}
}
@article {icl: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}
}
@article {icl:85,
	title = {Automated Empirical Optimization of Software and the ATLAS Project},
	journal = {Parallel Computing},
	volume = {27},
	number = {1-2},
	year = {2001},
	month = {2001-01},
	pages = {3-25},
	keywords = {atlas},
	author = {Clint Whaley and Antoine Petitet and Jack Dongarra}
}
@article {icl:6,
	title = {Basic Linear Algebra Subprograms (BLAS)},
	journal = {(an update), submitted to ACM TOMS},
	year = {2001},
	month = {2001-02},
	author = {Susan Blackford and James Demmel and Jack Dongarra and Iain Duff and Sven Hammarling and Greg Henry and Michael Heroux and Linda Kaufman and Andrew Lumsdaine and Antoine Petitet and Roldan Pozo and Karin Remington and Clint Whaley}
}
@techreport {icl:2,
	title = {Internet Backplane Protocol: API 1.0},
	journal = {University of Tennessee Computer Science Technical Report},
	number = {UT-CS-01-464},
	year = {2001},
	month = {2001-01},
	author = {Alessandro Bassi and Micah Beck and James Plank and Rich Wolski}
}
@article {icl:4,
	title = {Logistical Computing and Internetworking: Middleware for the Use of Storage in Communication},
	journal = {submitted to SC2001},
	year = {2001},
	month = {2001-11},
	address = {Denver, Colorado},
	keywords = {netsolve},
	author = {Micah Beck and Dorian Arnold and Alessandro Bassi and Francine Berman and Henri Casanova and Jack Dongarra and Terry Moore and Graziano Obertelli and James Plank and Martin Swany and Sathish Vadhiyar and Rich Wolski}
}
@article {icl: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}
}
@article {icl:23,
	title = {Overview of High Performance Computers},
	journal = {Handbook of Massive Data Sets},
	year = {2001},
	month = {2001-01},
	pages = {791-852},
	publisher = {Kluwer Academic Publishers},
	author = {Aad J. van der Steen and Jack Dongarra},
	editor = {James Abello and Panos Pardalos and Mauricio Resende}
}
@techreport {icl:49,
	title = {Automated Empirical Optimizations of Software and the ATLAS Project (LAPACK Working Note 147)},
	journal = {University of Tennessee Computer Science Department Technical Report,},
	number = {UT-CS-00-448},
	year = {2000},
	month = {2000-09},
	keywords = {atlas},
	author = {Clint Whaley and Antoine Petitet and Jack Dongarra}
}
@article {icl:29,
	title = {Logistical Networking: Sharing More Than the Wires},
	journal = {In Active Middleware Services, Ed. Salim Hariri, Craig A. Lee, Cauligi S. Raghavendra (2000), Kluwer Academic},
	year = {2000},
	month = {2000-01},
	address = {Norwell, MA},
	author = {Micah Beck and Terry Moore and James Plank and Martin Swany}
}
@inproceedings {icl:39,
	title = {Recent Advances in Parallel Virtual Machine and Message Passing Interface},
	journal = {Lecture Notes in Computer Science: Proceedings of 7th European PVM/MPI Users{\textquoteright} Group Meeting 2000},
	year = {2000},
	month = {2000-01},
	pages = {V1908},
	address = {(Hungary: Springer Verlag)},
	keywords = {ftmpi},
	author = {Jack Dongarra and Peter Kacsuk and N. Podhorszki}
}
@article {icl:33,
	title = {Deploying Fault-tolerance and Task Migration with NetSolve},
	journal = {Future Generation Computer Systems},
	volume = {15},
	number = {5-6},
	year = {1999},
	month = {1999-10},
	pages = {745-755},
	publisher = {Elsevier},
	keywords = {netsolve},
	author = {Henri Casanova and James Plank and Micah Beck 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}
}
@techreport {icl:72,
	title = {IBP - Internet Backplane Protocol: Infrastructure for Distributed Storage (V O.2)},
	journal = {University of Tennessee Computer Science Department Technical Report},
	number = {UT-CS-99-430},
	year = {1999},
	month = {1999-02},
	author = {Wael Elwasif and Micah Beck and James Plank}
}
@article {icl:53,
	title = {Logistical Quality of Service in NetSolve},
	journal = {Computer Communications},
	volume = {22},
	number = {11},
	year = {1999},
	month = {1999-01},
	pages = {1034-1044},
	keywords = {netsolve},
	author = {Micah Beck and Henri Casanova and Jack Dongarra and Terry Moore and James Plank and Francine Berman and Rich Wolski}
}
@article {icl:229,
	title = {A Numerical Linear Algebra Problem Solving Environment Designer{\textquoteright}s Perspective (LAPACK Working Note 139)},
	journal = {SIAM Annual Meeting},
	year = {1999},
	month = {1999-05},
	address = {Atlanta, GA},
	author = {Antoine Petitet and Henri Casanova and Clint Whaley and Jack Dongarra and Yves Robert}
}
@article {icl:75,
	title = {Parallel and Distributed Scientific Computing: A Numerical Linear Algebra Problem Solving Environment Designer{\textquoteright}s Perspective},
	journal = {Handbook on Parallel and Distributed Processing},
	year = {1999},
	month = {1999-01},
	author = {Antoine Petitet and Henri Casanova and Jack Dongarra and Yves Robert and Clint Whaley}
}
@article {icl:63,
	title = {Stochastic Performance Prediction for Iterative Algorithms in Distributed Environments},
	journal = {Journal of Parallel and Distributed Computing},
	volume = {98},
	number = {1},
	year = {1999},
	month = {1999-01},
	pages = {68-91},
	author = {Henri Casanova and Myung Ho Kim and James Plank and Jack Dongarra}
}
@article {1467,
	title = {ScaLAPACK: A Portable Linear Algebra Library for Distributed Memory Computers - Design Issues and Performance},
	journal = {Computer Physics Communications},
	volume = {97},
	year = {1996},
	month = {1996-08},
	pages = {1-15},
	abstract = {This paper outlines the content and performance of ScaLAPACK, a collection of mathematical software for linear algebra computations on distributed memory computers. The importance of developing standards for computational and message passing interfaces is discussed. We present the different components and building blocks of ScaLAPACK. This paper outlines the difficulties inherent in producing correct codes for networks of heterogeneous processors. We define a theoretical model of parallel computers dedicated to linear algebra applications: the Distributed Linear Algebra Machine (DLAM). This model provides a convenient framework for developing parallel algorithms and investigating their scalability, performance and programmability. Extensive performance results on various platforms are presented and analyzed with the help of the DLAM. Finally, this paper briefly describes future directions for the ScaLAPACK library and concludes by suggesting alternative approaches to mathematical libraries, explaining how ScaLAPACK could be integrated into efficient and user-friendly distributed systems.},
	doi = {https://doi.org/10.1016/0010-4655(96)00017-3},
	author = {Jaeyoung Choi and Jim Demmel and Inderjit Dhillon and Jack Dongarra and Susan Ostrouchov and Antoine Petitet and Kendall Stanley and David Walker and Clint Whaley}
}
