Publications
Block-asynchronous Multigrid Smoothers for GPU-accelerated Systems
, no. UT-CS-11-689, December 2011.
(608.95 KB)

Block-asynchronous Multigrid Smoothers for GPU-accelerated Systems,”
ICCS 2012, Omaha, NE, June 2012.
(608.95 KB)
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BlackjackBench: Portable Hardware Characterization with Automated Results Analysis,”
The Computer Journal, March 2013.
DOI: 10.1093/comjnl/bxt057
(408.45 KB)
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Biological Sequence Alignment on the Computational Grid Using the GrADS Framework,”
Future Generation Computing Systems, vol. 21, no. 6: Elsevier, pp. 980-986, June 2005.
(147.29 KB)
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Big Data and Extreme-Scale Computing: Pathways to Convergence - Toward a Shaping Strategy for a Future Software and Data Ecosystem for Scientific Inquiry,”
The International Journal of High Performance Computing Applications, vol. 32, issue 4, pp. 435–479, July 2018.
DOI: 10.1177/1094342018778123
(1.29 MB)
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Biannual Top-500 Computer Lists Track Changing Environments for Scientific Computing,”
SIAM News, vol. 34, no. 9, October 2002.
(2.62 MB)
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Batched One-Sided Factorizations of Tiny Matrices Using GPUs: Challenges and Countermeasures,”
Journal of Computational Science, vol. 26, pp. 226–236, May 2018.
DOI: 10.1016/j.jocs.2018.01.005
(3.73 MB)
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Batched matrix computations on hardware accelerators based on GPUs,”
International Journal of High Performance Computing Applications, February 2015.
DOI: 10.1177/1094342014567546
(2.16 MB)
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Basic Linear Algebra Subprograms (BLAS),”
(an update), submitted to ACM TOMS, February 2001.
(228.33 KB)
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Autotuning Techniques for Performance-Portable Point Set Registration in 3D,”
Supercomputing Frontiers and Innovations, vol. 5, no. 4, December 2018.
DOI: 10.14529/jsfi180404
(720.15 KB)
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Autotuning Numerical Dense Linear Algebra for Batched Computation With GPU Hardware Accelerators,”
Proceedings of the IEEE, vol. 106, issue 11, pp. 2040–2055, November 2018.
DOI: 10.1109/JPROC.2018.2868961
(2.53 MB)
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Autotuning in High-Performance Computing Applications,”
Proceedings of the IEEE, vol. 106, issue 11, pp. 2068–2083, November 2018.
DOI: 10.1109/JPROC.2018.2841200
(2.5 MB)
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Autotuning GEMM Kernels for the Fermi GPU,”
IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 11, November 2012.
DOI: 10.1109/TPDS.2011.311
(742.5 KB)
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Automatic Translation of Fortran to JVM Bytecode,”
Concurrency and Computation: Practice and Experience, vol. 15, no. 3-5, pp. 202-207, 00 2003.
(185.8 KB)
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Automatic Optimisation of Parallel Linear Algebra Routines in Systems with Variable Load,”
EuroPar 2002, Paderborn, Germany, August 2002.
(92.59 KB)
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Automatic analysis of inefficiency patterns in parallel applications,”
Concurrency and Computation: Practice and Experience, Special issue "Automatic Performance Analysis" (submitted), 00 2005.
(233.31 KB)
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Automatic Analysis of Inefficiency Patterns in Parallel Applications,”
Concurrency and Computation: Practice and Experience, vol. 19, no. 11, pp. 1481-1496, August 2007.
(233.31 KB)
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Automated Empirical Optimization of Software and the ATLAS Project,”
Parallel Computing, vol. 27, no. 1-2, pp. 3-25, January 2001.
(370.71 KB)
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Atlanta Organizers Put Mathematics to Work For the Math Sciences Community,”
SIAM News, vol. 32, no. 6, January 1999.
(45.98 KB)
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Assessing the Cost of Redistribution followed by a Computational Kernel: Complexity and Performance Results,”
Parallel Computing, vol. 52, pp. 22-41, February 2016.
DOI: doi:10.1016/j.parco.2015.09.005
(2.06 MB)
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Analytical Modeling and Optimization for Affinity Based Thread Scheduling on Multicore Systems,”
IEEE Cluster 2009, New Orleans, August 2009.
(395.53 KB)
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Analysis of Dynamically Scheduled Tile Algorithms for Dense Linear Algebra on Multicore Architectures,”
Submitted to Concurrency and Computations: Practice and Experience, November 2010.
(1.65 MB)
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Analysis and Design Techniques towards High-Performance and Energy-Efficient Dense Linear Solvers on GPUs,”
IEEE Transactions on Parallel and Distributed Systems, vol. 29, issue 12, pp. 2700–2712, December 2018.
DOI: 10.1109/TPDS.2018.2842785
(2.53 MB)
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Algorithms and Optimization Techniques for High-Performance Matrix-Matrix Multiplications of Very Small Matrices,”
Parallel Computing, vol. 81, pp. 1–21, January 2019.
DOI: 10.1016/j.parco.2018.10.003
(3.27 MB)
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Algorithmic Redistribution Methods for Block Cyclic Decompositions,”
IEEE Transactions on Parallel and Distributed Computing, vol. 10, no. 12, pp. 201-220, October 2002.
(524.82 KB)
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Algorithmic Issues on Heterogeneous Computing Platforms,”
Parallel Processing Letters, vol. 9, no. 2, pp. 197-213, January 1999.
(301.17 KB)
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Algorithmic Based Fault Tolerance Applied to High Performance Computing,”
Journal of Parallel and Distributed Computing, vol. 69, pp. 410-416, 00 2009.
(313.55 KB)
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Algorithm-Based Fault Tolerance for Fail-Stop Failures,”
IEEE Transactions on Parallel and Distributed Systems, vol. 19, no. 12, January 2008.
(340.49 KB)
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Algorithm-based Fault Tolerance for Dense Matrix Factorizations, Multiple Failures, and Accuracy,”
ACM Transactions on Parallel Computing, vol. 1, issue 2, no. 10, pp. 10:1-10:28, January 2015.
DOI: 10.1145/2686892
(1.14 MB)
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Adaptive Scheduling for Task Farming with Grid Middleware,”
International Journal of Supercomputer Applications and High-Performance Computing, vol. 13, no. 3, pp. 231-240, October 2002.
(461.08 KB)
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Adaptive Precision in Block-Jacobi Preconditioning for Iterative Sparse Linear System Solvers,”
Concurrency and Computation: Practice and Experience, vol. 31, no. 6, pp. e4460, March 2019.
DOI: 10.1002/cpe.4460
(341.54 KB)
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Active Netlib: An Active Mathematical Software Collection for Inquiry-based Computational Science and Engineering Education,”
Journal of Digital Information special issue on Interactivity in Digital Libraries, vol. 2, no. 4, 00 2002.
(182.59 KB)
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Achieving numerical accuracy and high performance using recursive tile LU factorization with partial pivoting,”
Concurrency and Computation: Practice and Experience, vol. 26, issue 7, pp. 1408-1431, May 2014.
DOI: 10.1002/cpe.3110
(1.96 MB)
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Acceleration of GPU-based Krylov solvers via Data Transfer Reduction,”
International Journal of High Performance Computing Applications, 2015.
“Accelerating Time-To-Solution for Computational Science and Engineering,”
SciDAC Review, 00 2009.
(739.11 KB)
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Accelerating the SVD Two Stage Bidiagonal Reduction and Divide and Conquer Using GPUs,”
Parallel Computing, vol. 74, pp. 3–18, May 2018.
DOI: 10.1016/j.parco.2017.10.004
(1.34 MB)
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Accelerating the SVD Bi-Diagonalization of a Batch of Small Matrices using GPUs,”
Journal of Computational Science, vol. 26, pp. 237–245, May 2018.
DOI: 10.1016/j.jocs.2018.01.007
(2.18 MB)
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Accelerating the Reduction to Upper Hessenberg, Tridiagonal, and Bidiagonal Forms through Hybrid GPU-Based Computing,”
Parallel Computing, vol. 36, no. 12, pp. 645-654, 00 2010.
(1.39 MB)
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Accelerating Scientific Computations with Mixed Precision Algorithms,”
Computer Physics Communications, vol. 180, issue 12, pp. 2526-2533, December 2009.
DOI: 10.1016/j.cpc.2008.11.005
(402.69 KB)
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Accelerating NWChem Coupled Cluster through dataflow-based Execution,”
The International Journal of High Performance Computing Applications, vol. 32, issue 4, pp. 540--551, July 2018.
DOI: 10.1177/1094342016672543
(1.68 MB)
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Accelerating NWChem Coupled Cluster through Dataflow-Based Execution,”
The International Journal of High Performance Computing Applications, pp. 1–13, January 2017.
DOI: 10.1177/1094342016672543
(4.07 MB)
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Accelerating Linear System Solutions Using Randomization Techniques,”
ACM Transactions on Mathematical Software (also LAWN 246), vol. 39, issue 2, February 2013.
DOI: 10.1145/2427023.2427025
(358.79 KB)
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Accelerating Linear System Solutions Using Randomization Techniques,”
INRIA RR-7616 / LAWN #246 (presented at International AMMCS’11), Waterloo, Ontario, Canada, July 2011.
(358.79 KB)
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Accelerating GPU Kernels for Dense Linear Algebra,”
Proc. of VECPAR'10, Berkeley, CA, June 2010.
(615.07 KB)
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The 30th Anniversary of the Supercomputing Conference: Bringing the Future Closer—Supercomputing History and the Immortality of Now,”
Computer, vol. 51, issue 10, pp. 74–85, November 2018.
DOI: 10.1109/MC.2018.3971352
(1.73 MB)
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Weighted Dynamic Scheduling with Many Parallelism Grains for Offloading of Numerical Workloads to Multiple Varied Accelerators,”
Proceedings of the 6th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA'15), vol. No. 5, Austin, TX, ACM, November 2015.
(347.6 KB)
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Weighted Block-Asynchronous Iteration on GPU-Accelerated Systems,”
Tenth International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms (Best Paper), Rhodes Island, Greece, August 2012.
(764.02 KB)
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Variable-Size Batched Gauss-Huard for Block-Jacobi Preconditioning,”
International Conference on Computational Science (ICCS 2017), vol. 108, Zurich, Switzerland, Procedia Computer Science, pp. 1783-1792, June 2017.
DOI: 10.1016/j.procs.2017.05.186
(512.57 KB)
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