Publications
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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A Standard for Batched BLAS Routines
, Paris, France, 17th SIAM Conference on Parallel Processing for Scientific Computing (SIAM PP16), April 2016.
(1.93 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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Towards a High-Performance Tensor Algebra Package for Accelerators
, Gatlinburg, TN, moky Mountains Computational Sciences and Engineering Conference (SMC15), September 2015.
(1.76 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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Accelerating Linear Algebra with MAGMA
, Knoxville, TN, ECP Annual Meeting 2018, Tutorial, February 2018.
(35.27 MB)
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Accelerating Tensor Contractions in High-Order FEM with MAGMA Batched
, Atlanta, GA, SIAM Conference on Computer Science and Engineering (SIAM CSE17), Presentation, March 2017.
(9.29 MB)
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On the Design, Autotuning, and Optimization of GPU Kernels for Kinetic Network Simulations Using Fast Explicit Integration and GPU Batched Computation
, Oak Ridge, TN, Joint Institute for Computational Sciences Seminar Series, Presentation, September 2015.
(17.25 MB)
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MAGMA: A Breakthrough in Solvers for Eigenvalue Problems
, San Jose, CA, GPU Technology Conference (GTC12), Presentation, May 2012.
(9.23 MB)
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MAGMA: A New Generation of Linear Algebra Library for GPU and Multicore Architectures
, Salt Lake City, UT, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC12), Presentation, November 2012.
(4.69 MB)
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MAGMA MIC: Optimizing Linear Algebra for Intel Xeon Phi
, Frankfurt, Germany, ISC High Performance (ISC15), Intel Booth Presentation, June 2015.
(2.03 MB)
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MAGMA Tensors and Batched Computing for Accelerating Applications on GPUs
, San Jose, CA, GPU Technology Conference (GTC17), Presentation in Session S7728, May 2017.
(11.12 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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MagmaDNN – High-Performance Data Analytics for Manycore GPUs and CPUs
, Knoxville, TN, 2017 Summer Research Experiences for Undergraduate (REU), Presentation, December 2017.
(5.06 MB)
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Power-Aware HPC on Intel Xeon Phi KNL Processors
, Frankfurt, Germany, ISC High Performance (ISC17), Intel Booth Presentation, June 2017.
(5.87 MB)
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Batched BLAS (Basic Linear Algebra Subprograms) 2018 Specification
, July 2018.
(483.05 KB)
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Algorithms and Optimization Techniques for High-Performance Matrix-Matrix Multiplications of Very Small Matrices,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-18-09: Innovative Computing Laboratory, University of Tennessee, September 2018.
(3.74 MB)
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Analysis of Dynamically Scheduled Tile Algorithms for Dense Linear Algebra on Multicore Architectures,”
University of Tennessee Computer Science Technical Report, UT-CS-11-666, (also Lawn 243), March 2011.
(1.65 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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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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Distributed Dense Numerical Linear Algebra Algorithms on Massively Parallel Architectures: DPLASMA,”
University of Tennessee Computer Science Technical Report, UT-CS-10-660, September 2010.
(366.26 KB)
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Distributed-Memory Task Execution and Dependence Tracking within DAGuE and the DPLASMA Project,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-10-02, 00 2010.
(400.75 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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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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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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High-Performance Tensor Contractions for GPUs,”
University of Tennessee Computer Science Technical Report, no. UT-EECS-16-738: University of Tennessee, January 2016.
(2.36 MB)
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An Improved Parallel Singular Value Algorithm and Its Implementation for Multicore Hardware,”
University of Tennessee Computer Science Technical Report (also LAWN 283), no. ut-eecs-13-720: University of Tennessee, October 2013.
(1.23 MB)
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MAGMA Batched: A Batched BLAS Approach for Small Matrix Factorizations and Applications on GPUs,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-16-02: University of Tennessee, August 2016.
(929.79 KB)
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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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Parallel Reduction to Condensed Forms for Symmetric Eigenvalue Problems using Aggregated Fine-Grained and Memory-Aware Kernels,”
University of Tennessee Computer Science Technical Report, UT-CS-11-677, (also Lawn254), August 2011.
(636.01 KB)
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Performance, Design, and Autotuning of Batched GEMM for GPUs,”
University of Tennessee Computer Science Technical Report, no. UT-EECS-16-739: University of Tennessee, February 2016.
(1.27 MB)
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PLASMA 17 Performance Report,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-17-11: University of Tennessee, June 2017.
(7.57 MB)
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PLASMA 17.1 Functionality Report,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-17-10: University of Tennessee, June 2017.
(1.8 MB)
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POMPEI: Programming with OpenMP4 for Exascale Investigations,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-17-09: University of Tennessee, December 2017.
(1.1 MB)
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Roadmap for the Development of a Linear Algebra Library for Exascale Computing: SLATE: Software for Linear Algebra Targeting Exascale,”
SLATE Working Notes, no. 01, ICL-UT-17-02: Innovative Computing Laboratory, University of Tennessee, June 2017.
(2.8 MB)
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Small Tensor Operations on Advanced Architectures for High-Order Applications,”
University of Tennessee Computer Science Technical Report, no. UT-EECS-17-749: Innovative Computing Laboratory, University of Tennessee, April 2017.
(1.09 MB)
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