On the performance and energy efficiency of sparse linear algebra on GPUs
Author:
Affiliation:
1. University of Tennessee, Knoxville, USA
2. Oak Ridge National Laboratory, USA
3. University of Manchester, UK
Abstract
Funder
Division of Computing and Communication Foundations
Advanced Scientific Computing Research
Nvidia
Russian Scientific Fund
Publisher
SAGE Publications
Subject
Hardware and Architecture,Theoretical Computer Science,Software
Link
http://journals.sagepub.com/doi/pdf/10.1177/1094342016672081
Reference44 articles.
1. QR Factorization on a Multicore Node Enhanced with Multiple GPU Accelerators
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3. LAPACK Users' Guide
4. Anderson M, Ballard G, Demmel J, Keutzer K (2010) Communication-avoiding QR decomposition for GPUs. Technical Report UCB/EECS-2010-131, EECS Department, University of California, Berkeley.
5. Anzt H, Tomov S, Dongarra J (2014) Implementing a sparse matrix vector product for the SELL-C/SELL-C-σ formats on NVIDIA GPUs. Technical Report ut-eecs-14-727, University of Tennessee.
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