Abstract
A challenging class of problems arising in many GPU applications, called batched problems, involves linear algebra operations on many small-sized matrices. We designed batched BLAS (Basic Linear Algebra Subroutines) routines, and in particular the Level-2 BLAS GEMV and the Level-3 BLAS GEMM routines, to solve them. We proposed device functions and big-tile settings in our batched BLAS design. We adopted auto-tuning to optimize different instances of GEMV routines. We illustrated our batched BLAS approach to optimize batched bi-diagonalization progressively on a K40c GPU. The optimization techniques in this paper are applicable to the other two-sided factorizations as well.
Original language | English |
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Pages (from-to) | 1008-1018 |
Number of pages | 11 |
Journal | Procedia Computer Science |
Volume | 108 |
DOIs | |
State | Published - 2017 |
Event | International Conference on Computational Science ICCS 2017 - Zurich, Switzerland Duration: Jun 12 2017 → Jun 14 2017 |
Funding
This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. The work was also partially supported by Nvidia and NSF under grant #1514406.
Keywords
- Hardware accelerators
- Singular Value Problems
- batched
- two-sided factorization algorithms