IEICE TRANSACTIONS on Fundamentals
Parallel Sparse Cholesky Factorization on a Heterogeneous Platform
Summary :
We present a new approach for sparse Cholesky factorization on a heterogeneous platform with a graphics processing unit (GPU). The sparse Cholesky factorization is one of the core algorithms of numerous computing applications. We tuned the supernode data structure and used a parallelization method for GPU tasks to increase GPU utilization. Results show that our approach substantially reduces computational time.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E96-A No.4 pp.833-834
- Publication Date
- 2013/04/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E96.A.833
- Type of Manuscript
- LETTER
- Category
- Algorithms and Data Structures
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Dan ZOU, Yong DOU, Rongchun LI, "Parallel Sparse Cholesky Factorization on a Heterogeneous Platform" in IEICE TRANSACTIONS on Fundamentals,
vol. E96-A, no. 4, pp. 833-834, April 2013, doi: 10.1587/transfun.E96.A.833.
Abstract: We present a new approach for sparse Cholesky factorization on a heterogeneous platform with a graphics processing unit (GPU). The sparse Cholesky factorization is one of the core algorithms of numerous computing applications. We tuned the supernode data structure and used a parallelization method for GPU tasks to increase GPU utilization. Results show that our approach substantially reduces computational time.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E96.A.833/_p
Copy
@ARTICLE{e96-a_4_833,
author={Dan ZOU, Yong DOU, Rongchun LI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Parallel Sparse Cholesky Factorization on a Heterogeneous Platform},
year={2013},
volume={E96-A},
number={4},
pages={833-834},
abstract={We present a new approach for sparse Cholesky factorization on a heterogeneous platform with a graphics processing unit (GPU). The sparse Cholesky factorization is one of the core algorithms of numerous computing applications. We tuned the supernode data structure and used a parallelization method for GPU tasks to increase GPU utilization. Results show that our approach substantially reduces computational time.},
keywords={},
doi={10.1587/transfun.E96.A.833},
ISSN={1745-1337},
month={April},}
Copy
TY - JOUR
TI - Parallel Sparse Cholesky Factorization on a Heterogeneous Platform
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 833
EP - 834
AU - Dan ZOU
AU - Yong DOU
AU - Rongchun LI
PY - 2013
DO - 10.1587/transfun.E96.A.833
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E96-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2013
AB - We present a new approach for sparse Cholesky factorization on a heterogeneous platform with a graphics processing unit (GPU). The sparse Cholesky factorization is one of the core algorithms of numerous computing applications. We tuned the supernode data structure and used a parallelization method for GPU tasks to increase GPU utilization. Results show that our approach substantially reduces computational time.
ER -
English
