Publication detail

Fast Linear Algebra on GPU

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

6

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/"
}