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POLOK, L. SMRŽ, P.
Original Title
Fast Linear Algebra on GPU
Type
article in a collection out of WoS and Scopus
Language
English
Original Abstract
GPUs have been successfully used for acceleration of many mathematical functions and libraries. A common limitation of those libraries is the minimal size of primitives being handled, in order to achieve a significant speedup compared to their CPU versions. The minimal size requirement can prove prohibitive for many applications. It can be loosened by batching operations in order to have sufficient amount of data to perform the calculation maximally efficiently on the GPU. A fast OpenCL implementation of two basic vector functions - vector reduction and vector scaling - is described in this paper. Its performance is analyzed by running benchmarks on two of the most common GPUs in use - Tesla and Fermi GPUs from NVIDIA. Reported experimental results show that our implementation significantly outperforms the current state-of-the-art GPU-based basic linear algebra library CUBLAS.
Keywords
GPU; parallel reduction; linear algebra; BLAS; OpenCL; CUDA
Authors
POLOK, L.; SMRŽ, P.
RIV year
2012
Released
25. 6. 2012
Publisher
IEEE Computer Society
Location
Liverpool
ISBN
978-0-7695-4749-7
Book
IEEE conference proceedings
Pages from
1
Pages to
6
Pages count
URL
https://www.fit.vut.cz/research/publication/10039/
BibTex
@inproceedings{BUT96982, author="Lukáš {Polok} and Pavel {Smrž}", title="Fast Linear Algebra on GPU", booktitle="IEEE conference proceedings", year="2012", pages="1--6", publisher="IEEE Computer Society", address="Liverpool", isbn="978-0-7695-4749-7", url="https://www.fit.vut.cz/research/publication/10039/" }