Publications
Towards a High-Performance Tensor Algebra Package for Accelerators
, Gatlinburg, TN, moky Mountains Computational Sciences and Engineering Conference (SMC15), September 2015.
(1.76 MB)
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)
Accelerating Linear Algebra with MAGMA
, Knoxville, TN, ECP Annual Meeting 2018, Tutorial, February 2018.
(35.27 MB)
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)
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)
MAGMA: A Breakthrough in Solvers for Eigenvalue Problems
, San Jose, CA, GPU Technology Conference (GTC12), Presentation, May 2012.
(9.23 MB)
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)
MAGMA MIC: Optimizing Linear Algebra for Intel Xeon Phi
, Frankfurt, Germany, ISC High Performance (ISC15), Intel Booth Presentation, June 2015.
(2.03 MB)
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)
MagmaDNN 0.2 High-Performance Data Analytics for Manycore GPUs and CPUs
: University of Tennessee, January 2019.
DOI: 10.13140/RG.2.2.14906.64961 (7.84 MB)
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)
Power-Aware HPC on Intel Xeon Phi KNL Processors
, Frankfurt, Germany, ISC High Performance (ISC17), Intel Booth Presentation, June 2017.
(5.87 MB)
Batched BLAS (Basic Linear Algebra Subprograms) 2018 Specification
, July 2018.
(483.05 KB)
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)
“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)
“C++ API for Batch BLAS,”
SLATE Working Notes, no. 04, ICL-UT-17-12: University of Tennessee, December 2017.
(1.89 MB)
“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)
“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)
“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)
“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)
“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)
“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)
“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)
“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)
“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)
“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)
“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)
“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)
“PLASMA 17 Performance Report,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-17-11: University of Tennessee, June 2017.
(7.57 MB)
“PLASMA 17.1 Functionality Report,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-17-10: University of Tennessee, June 2017.
(1.8 MB)
“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)
“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)
“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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