Publications
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Filters: Author is Mark Gates [Clear All Filters]
Least Squares Solvers for Distributed-Memory Machines with GPU Accelerators,”
ACM International Conference on Supercomputing (ICS '19), Phoenix, Arizona, ACM, pp. 117–126, June 2019.
DOI: https://dl.acm.org/doi/abs/10.1145/3330345.3330356 (1.63 MB)
“Least Squares Performance Report,”
SLATE Working Notes, no. 09, ICL-UT-18-10: Innovative Computing Laboratory, University of Tennessee, December 2018.
(1.76 MB)
“Implementation of the C++ API for Batch BLAS,”
SLATE Working Notes, no. 07, ICL-UT-18-04: Innovative Computing Laboratory, University of Tennessee, June 2018.
(1.07 MB)
“Heterogeneous Streaming,”
The Sixth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), IPDPS 2016, Chicago, IL, IEEE, May 2016.
(2.73 MB)
“Designing SLATE: Software for Linear Algebra Targeting Exascale,”
SLATE Working Notes, no. 03, ICL-UT-17-06: Innovative Computing Laboratory, University of Tennessee, October 2017.
(2.8 MB)
“Computational Benefit of GPU Optimization for Atmospheric Chemistry Modeling,”
Journal of Advances in Modeling Earth Systems, vol. 10, issue 8, pp. 1952–1969, August 2018.
DOI: 10.1029/2018MS001276 (3.4 MB)
“Communication Avoiding LU with Tournament Pivoting in SLATE,”
SLATE Working Notes, no. 18, ICL-UT-22-01, January 2022.
(3.74 MB)
“Clover: Computational Libraries Optimized via Exascale Research
, Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.
(872 KB)
C++ API for BLAS and LAPACK,”
SLATE Working Notes, no. 02, ICL-UT-17-03: Innovative Computing Laboratory, University of Tennessee, June 2017.
(1.12 MB)
“C++ API for Batch BLAS,”
SLATE Working Notes, no. 04, ICL-UT-17-12: University of Tennessee, December 2017.
(1.89 MB)
“Bringing High Performance Computing to Big Data Algorithms,”
Handbook of Big Data Technologies: Springer, 2017.
DOI: 10.1007/978-3-319-49340-4 (1.22 MB)
“Block-asynchronous Multigrid Smoothers for GPU-accelerated Systems,”
ICCS 2012, Omaha, NE, June 2012.
(608.95 KB)
“Batched BLAS (Basic Linear Algebra Subprograms) 2018 Specification
, July 2018.
(483.05 KB)
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)
“Autotuning Batch Cholesky Factorization in CUDA with Interleaved Layout of Matrices,”
Parallel and Distributed Processing Symposium Workshops (IPDPSW), Orlando, FL, IEEE, June 2017.
DOI: 10.1109/IPDPSW.2017.18
“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)
“Accelerating Linear Algebra with MAGMA
, Knoxville, TN, ECP Annual Meeting 2018, Tutorial, February 2018.
(35.27 MB)