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)
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Least Squares Performance Report,”
SLATE Working Notes, no. 09, ICL-UT-18-10: Innovative Computing Laboratory, University of Tennessee, December 2018.
(1.76 MB)
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Interface for Sparse Linear Algebra Operations
, November 2024.
DOI: 10.48550/arXiv.2411.13259
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)
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Heterogeneous Streaming,”
The Sixth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), IPDPS 2016, Chicago, IL, IEEE, May 2016.
(2.73 MB)
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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)
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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)
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Communication Avoiding LU with Tournament Pivoting in SLATE,”
SLATE Working Notes, no. 18, ICL-UT-22-01, January 2022.
(3.74 MB)
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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)
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C++ API for Batch BLAS,”
SLATE Working Notes, no. 04, ICL-UT-17-12: University of Tennessee, December 2017.
(1.89 MB)
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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)
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Block-asynchronous Multigrid Smoothers for GPU-accelerated Systems,”
ICCS 2012, Omaha, NE, June 2012.
(608.95 KB)
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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)
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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)
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Accelerating Linear Algebra with MAGMA
, Knoxville, TN, ECP Annual Meeting 2018, Tutorial, February 2018.
(35.27 MB)
