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.
(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.
(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.
(1.03 MB)
“The International Exascale Software Project Roadmap,”
International Journal of High Performance Computing, vol. 25, no. 1, pp. 3-60, January 2011.
(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.
(3.21 MB)
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