Detail publikace

Fast Linear Algebra on GPU

POLOK, L. SMRŽ, P.

Originální název

Fast Linear Algebra on GPU

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

GPU; parallel reduction; linear algebra; BLAS; OpenCL; CUDA

Autoři

POLOK, L.; SMRŽ, P.

Rok RIV

2012

Vydáno

25. 6. 2012

Nakladatel

IEEE Computer Society

Místo

Liverpool

ISBN

978-0-7695-4749-7

Kniha

IEEE conference proceedings

Strany od

1

Strany do

6

Strany počet

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/"
}