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Abstract
A matrix times vector multiplication (matvec) is a cornerstone operation in iterative methods of solving large sparse systems of equations such as the conjugate gradients method (cg), the minimal residual method (minres), the generalized residual method (gmres) and exerts an influence on overall performance of those methods. An implementation of matvec is particularly demanding when one executes computations on a GPU (Graphics Processing Unit), because using this device one has to comply with certain programming rules in order to take advantage of parallel computing. In this paper, it will be shown how to modify the sparse matrix-vector multiplication based on CRS (Compressed Row Storage) to achieve about 3-5 times better performance on - a low cost - GPU (GeForce GTX 285, 1.48 GHz) than on a CPU (Intel Core i7, 2.67 GHz).
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- Category:
- Articles
- Type:
- artykuły w czasopismach recenzowanych i innych wydawnictwach ciągłych
- Published in:
-
Zeszyty Naukowe Wydziału ETI Politechniki Gdańskiej. Technologie Informacyjne
pages 307 - 312,
ISSN: 1732-1166 - Language:
- English
- Publication year:
- 2010
- Bibliographic description:
- Dziekoński A., Mrozowski M.: Tuning matrix-vector multiplication on GPU// Zeszyty Naukowe Wydziału ETI Politechniki Gdańskiej. Technologie Informacyjne. -., (2010), s.307-312
- Verified by:
- Gdańsk University of Technology
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