Publications
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)

Autotuning Dense Linear Algebra Libraries on GPUs
, Basel, Switzerland, Sixth International Workshop on Parallel Matrix Algorithms and Applications (PMAA 2010), June 2010.
(579.44 KB)

Does your tool support PAPI SDEs yet?
, Tahoe City, CA, 13th Scalable Tools Workshop, July 2019.
(3.09 MB)

Flexible Batched Sparse Matrix Vector Product on GPUs
, Denver, Colorado, ScalA'17: 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, November 2017.
(16.8 MB)

The Future of Computing: Software Libraries
, Savannah, GA, DOD CREATE Developers' Review, Keynote Presentation, February 2012.
(6.76 MB)

Integrating Deep Learning in Domain Science at Exascale (MagmaDNN)
, virtual, DOD HPCMP seminar, December 2020.
(11.12 MB)

An Introduction to the MAGMA project - Acceleration of Dense Linear Algebra
: NVIDIA Webinar, June 2010.
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 - LAPACK for HPC on Heterogeneous Architectures
, Oak Ridge, TN, Titan Summit at Oak Ridge National Laboratory, Presentation, August 2011.
(20.43 MB)

MAGMA MIC: Linear Algebra Library for Intel Xeon Phi Coprocessors
, Salt Lake City, UT, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC12), November 2012.
(6.4 MB)

MAGMA MIC: Optimizing Linear Algebra for Intel Xeon Phi
, Frankfurt, Germany, ISC High Performance (ISC15), Intel Booth Presentation, June 2015.
(2.03 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)

Memory Traffic and Complete Application Profiling with PAPI Multi-Component Measurements
, St. Petersburg, FL, 28th HIPS Workshop, May 2023.
(3.99 MB)

PAPI: Counting outside the Box
, Barcelona, Spain, 8th JLESC Meeting, April 2018.
PAPI's New Software-Defined Events for In-Depth Performance Analysis
, Lyon, France, CCDSC 2018: Workshop on Clusters, Clouds, and Data for Scientific Computing, September 2018.
PAPI's new Software-Defined Events for in-depth Performance Analysis
, Dresden, Germany, 13th Parallel Tools Workshop, September 2019.
(3.14 MB)

Power-Aware HPC on Intel Xeon Phi KNL Processors
, Frankfurt, Germany, ISC High Performance (ISC17), Intel Booth Presentation, June 2017.
(5.87 MB)

SLATE: Design of a Modern Distributed and Accelerated Linear Algebra Library
, Denver, CO, International Conference for High Performance Computing, Networking, Storage and Analysis (SC19), November 2019.
(16.19 MB)

SLATE Tutorial
, Houston, TX, 2020 ECP Annual Meeting, February 2020.
(12.14 MB)

Software-Defined Events through PAPI for In-Depth Analysis of Application Performance
, Basel, Switzerland, 5th Platform for Advanced Scientific Computing Conference (PASC18), July 2018.
Understanding Native Event Semantics
, Knoxville, TN, 9th JLESC Workshop, April 2019.
(2.33 MB)

What it Takes to keep PAPI Instrumental for the HPC Community
, Collegeville, MN, The 2019 Collegeville Workshop on Sustainable Scientific Software (CW3S19), July 2019.
(3.29 MB)

Is your scheduling good? How would you know?
, Bordeaux, France, 14th Scheduling for Large Scale Systems Workshop, June 2019.
(2.5 MB)

Batched BLAS (Basic Linear Algebra Subprograms) 2018 Specification
, July 2018.
(483.05 KB)

Reinventing High Performance Computing: Challenges and Opportunities,”
ICL Technical Report, no. ICL-UT-22-03, March 2022.
(1.36 MB)
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2016 Dense Linear Algebra Software Packages Survey,”
University of Tennessee Computer Science Technical Report, no. UT-EECS-16-744 / LAWN 290: University of Tennessee, September 2016.
(366.43 KB)
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Accelerating the Reduction to Upper Hessenberg Form through Hybrid GPU-Based Computing,”
University of Tennessee Computer Science Technical Report, UT-CS-09-642 (also LAPACK Working Note 219), May 2009.
(2.37 MB)
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Achieving Numerical Accuracy and High Performance using Recursive Tile LU Factorization,”
University of Tennessee Computer Science Technical Report (also as a LAWN), no. ICL-UT-11-08, September 2011.
(618.53 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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Analysis of the Communication and Computation Cost of FFT Libraries towards Exascale,”
ICL Technical Report, no. ICL-UT-22-07: Innovative Computing Laboratory, July 2022.
(5.91 MB)
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ASCR@40: Four Decades of Department of Energy Leadership in Advanced Scientific Computing Research
: Advanced Scientific Computing Advisory Committee (ASCAC), US Department of Energy, August 2020.
ASCR@40: Highlights and Impacts of ASCR’s Programs
: US Department of Energy’s Office of Advanced Scientific Computing Research, June 2020.
DOI: 10.2172/1631812
Asynchronous SGD for DNN Training on Shared-Memory Parallel Architectures,”
Innovative Computing Laboratory Technical Report, no. ICL-UT-20-04: University of Tennessee, Knoxville, March 2020.
(188.51 KB)
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