Publications
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Filters: Author is David Keyes [Clear All Filters]
Application of Machine Learning to the Selection of Sparse Linear Solvers,”
International Journal of High Performance Computing Applications (submitted), 00 2006.
(392.96 KB)
“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)
“Extreme-Scale Task-Based Cholesky Factorization Toward Climate and Weather Prediction Applications,”
Platform for Advanced Scientific Computing Conference (PASC20), Geneva, Switzerland, ACM, June 2020.
DOI: 10.1145/3394277.3401846 (2.71 MB)
“A Framework to Exploit Data Sparsity in Tile Low-Rank Cholesky Factorization,”
IEEE International Parallel and Distributed Processing Symposium (IPDPS), July 2022.
DOI: 10.1109/IPDPS53621.2022.00047 (1.03 MB)
“The International Exascale Software Project Roadmap,”
International Journal of High Performance Computing, vol. 25, no. 1, pp. 3-60, January 2011.
DOI: 10.1177/1094342010391989 (719.74 KB)
“Leveraging PaRSEC Runtime Support to Tackle Challenging 3D Data-Sparse Matrix Problems,”
35th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2021), Portland, OR, IEEE, May 2021.
(1.08 MB)
“Optimizing Memory-Bound Numerical Kernels on GPU Hardware Accelerators,”
VECPAR 2012, Kobe, Japan, July 2012.
(737.28 KB)
“Performance Analysis of Tile Low-Rank Cholesky Factorization Using PaRSEC Instrumentation Tools,”
Workshop on Programming and Performance Visualization Tools (ProTools 19) at SC19, Denver, CO, ACM, November 2019.
(429.55 KB)
“Performance optimization of Sparse Matrix-Vector Multiplication for multi-component PDE-based applications using GPUs,”
Concurrency and Computation: Practice and Experience, vol. 28, issue 12, pp. 3447 - 3465, May 2016.
DOI: 10.1002/cpe.v28.1210.1002/cpe.3874 (3.21 MB)
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