Publications
Batched One-Sided Factorizations of Tiny Matrices Using GPUs: Challenges and Countermeasures,”
Journal of Computational Science, vol. 26, pp. 226–236, May 2018.
(3.73 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.
(3.4 MB)
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The Design of Fast and Energy-Efficient Linear Solvers: On the Potential of Half-Precision Arithmetic and Iterative Refinement Techniques,”
International Conference on Computational Science (ICCS 2018), vol. 10860, Wuxi, China, Springer, pp. 586–600, June 2018.
(487.88 KB)
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Evaluation and Design of FFT for Distributed Accelerated Systems,”
ECP WBS 2.3.3.09 Milestone Report, no. FFT-ECP ST-MS-10-1216: Innovative Computing Laboratory, University of Tennessee, October 2018.
(7.53 MB)
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A Guide for Achieving High Performance with Very Small Matrices on GPUs: A Case Study of Batched LU and Cholesky Factorizations,”
IEEE Transactions on Parallel and Distributed Systems, vol. 29, issue 5, pp. 973–984, May 2018.
(832.92 KB)
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Harnessing GPU Tensor Cores for Fast FP16 Arithmetic to Speed up Mixed-Precision Iterative Refinement Solvers,”
The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18), Dallas, TX, IEEE, November 2018.
(642.51 KB)
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Harnessing GPU's Tensor Cores Fast FP16 Arithmetic to Speedup Mixed-Precision Iterative Refinement Solvers and Achieve 74 Gflops/Watt on Nvidia V100
, San Jose, CA, GPU Technology Conference (GTC), Poster, March 2018.
(2.96 MB)
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Investigating Power Capping toward Energy-Efficient Scientific Applications,”
Concurrency Computation: Practice and Experience, vol. 2018, issue e4485, pp. 1-14, April 2018.
(1.2 MB)
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MATEDOR: MAtrix, TEnsor, and Deep-learning Optimized Routines
, Dallas, TX, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18), Research Poster, November 2018.
(2.55 MB)
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MAtrix, TEnsor, and Deep-learning Optimized Routines (MATEDOR)
, Washington, DC, NSF PI Meeting, Poster, April 2018.
(2.4 MB)
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Optimizing GPU Kernels for Irregular Batch Workloads: A Case Study for Cholesky Factorization,”
IEEE High Performance Extreme Computing Conference (HPEC’18), Waltham, MA, IEEE, September 2018.
(729.87 KB)
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The Singular Value Decomposition: Anatomy of Optimizing an Algorithm for Extreme Scale,”
SIAM Review, vol. 60, issue 4, pp. 808–865, November 2018.
(2.5 MB)
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Tensor Contractions using Optimized Batch GEMM Routines
, San Jose, CA, GPU Technology Conference (GTC), Poster, March 2018.
(1.64 MB)
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Using GPU FP16 Tensor Cores Arithmetic to Accelerate Mixed-Precision Iterative Refinement Solvers and Reduce Energy Consumption
, Frankfurt, Germany, ISC High Performance (ISC18), Best Poster Award, June 2018.
(3.01 MB)
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Using GPU FP16 Tensor Cores Arithmetic to Accelerate Mixed-Precision Iterative Refinement Solvers and Reduce Energy Consumption,”
ISC High Performance (ISC'18), Best Poster, Frankfurt, Germany, June 2018.
(3.01 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.
(3.27 MB)
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Design and Implementation for FFT-ECP on Distributed Accelerated Systems,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-19-05: University of Tennessee, April 2019.
(3.19 MB)
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Evaluation of Directive-Based Performance Portable Programming Models,”
International Journal of High Performance Computing and Networking, vol. 14, issue 2, pp. 165-182.
(1.12 MB)
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FFT-ECP Fast Fourier Transform
, Houston, TX, 2019 ECP Annual Meeting (Research Poster), January 2019.
(1.51 MB)
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FFT-ECP Implementation Optimizations and Features Phase,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-19-12: University of Tennessee, October 2019.
(4.14 MB)
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GPUDirect MPI Communications and Optimizations to Accelerate FFTs on Exascale Systems,”
EuroMPI'19 Posters, Zurich, Switzerland, no. icl-ut-19-06: ICL, September 2019.
(2.25 MB)
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Impacts of Multi-GPU MPI Collective Communications on Large FFT Computation,”
Workshop on Exascale MPI (ExaMPI) at SC19, Denver, CO, November 2019.
(1.6 MB)
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MagmaDNN 0.2 High-Performance Data Analytics for Manycore GPUs and CPUs
: University of Tennessee, January 2019.
(7.84 MB)
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PLASMA: Parallel Linear Algebra Software for Multicore Using OpenMP,”
ACM Transactions on Mathematical Software, vol. 45, issue 2, June 2019.
(7.5 MB)
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FFT-ECP API and High-Performance Library Prototype for 2-D and 3-D FFTs on Large-Scale Heterogeneous Systems with GPUs,”
ECP Milestone Report, no. FFT-ECP STML13-27: Innovative Computing Laboratory, University of Tennessee, January 2020.
(9.71 MB)
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heFFTe: Highly Efficient FFT for Exascale,”
International Conference on Computational Science (ICCS 2020), Amsterdam, Netherlands, June 2020.
(2.62 MB)
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heFFTe: Highly Efficient FFT for Exascale (Poster)
: NVIDIA GPU Technology Conference (GTC2020), October 2020.
(866.88 KB)
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heFFTe: Highly Efficient FFT for Exascale (Poster)
, Seattle, WA, SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP20), February 2020.
(1.54 MB)
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heFFTe: Highly Efficient FFT for Exascale (Poster)
, Houston, TX, 2020 Exascale Computing Project Annual Meeting, February 2020.
(6.2 MB)
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MAGMA Templates for Scalable Linear Algebra on Emerging Architectures,”
The International Journal of High Performance Computing Applications, vol. 34, issue 6, pp. 645-658, November 2020.
“Mixed-Precision Iterative Refinement using Tensor Cores on GPUs to Accelerate Solution of Linear Systems,”
Proceedings of the Royal Society A, vol. 476, issue 2243, November 2020.
(2.24 MB)
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Mixed-Precision Solution of Linear Systems Using Accelerator-Based Computing,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-20-05: University of Tennessee, May 2020.
(1.03 MB)
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A Set of Batched Basic Linear Algebra Subprograms,”
ACM Transactions on Mathematical Software, October 2020.
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