id: 06571975
dt: a
an: 06571975
au: Stpiczyński, Przemysław
ti: Semiautomatic acceleration of sparse matrix-vector product using openacc.
so: Wyrzykowski, Roman (ed.) et al., Parallel processing and applied
mathematics. 11th international conference, PPAM 2015, Krakow, Poland,
September 6‒9, 2015. Revised selected papers. Part II. Cham: Springer
(ISBN 978-3-319-32151-6/pbk; 978-3-319-32152-3/ebook). Lecture Notes in
Computer Science 9574, 143-152 (2016).
py: 2016
pu: Cham: Springer
la: EN
cc:
ut: sparse matrices; spmv; gpus; openacc; cusparse
ci:
li: doi:10.1007/978-3-319-32152-3_14
ab: Summary: The aim of this paper is to show that well known SPARSKIT SpMV
routines for {\it Ellpack-Itpack} and {\it Jagged Diagonal} formats can
be easily and successfully adapted to a hybrid GPU-accelerated computer
environment using OpenACC. We formulate general guidelines for simple
steps that should be done to transform source codes with irregular data
access into efficient OpenACC programs. We also advise how to improve
the performance of such programs by tuning data structures to utilize
hardware properties of GPUs. Numerical experiments show that our
accelerated versions of SPARSKIT SpMV routines achieve the performance
comparable with the performance of the corresponding CUSPARSE routines
optimized by NVIDIA.
rv: