Publications
Analysis of Dynamically Scheduled Tile Algorithms for Dense Linear Algebra on Multicore Architectures,”
Submitted to Concurrency and Computations: Practice and Experience, November 2010.
(1.65 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)
“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)
“Flexible Development of Dense Linear Algebra Algorithms on Massively Parallel Architectures with DPLASMA,”
Proceedings of the Workshops of the 25th IEEE International Symposium on Parallel and Distributed Processing (IPDPS 2011 Workshops), Anchorage, Alaska, USA, IEEE, pp. 1432-1441, May 2011.
(1.26 MB)
“Parallel Reduction to Condensed Forms for Symmetric Eigenvalue Problems using Aggregated Fine-Grained and Memory-Aware Kernels,”
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC11), Seattle, WA, November 2011.
(636.01 KB)
“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)
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)
“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)