DocumentCode :
2466800
Title :
Auto-Tuning CUDA Parameters for Sparse Matrix-Vector Multiplication on GPUs
Author :
Guo, Ping ; Wang, Liqiang
Author_Institution :
Dept. of Comput. Sci., Univ. of Wyoming, Laramie, WY, USA
fYear :
2010
fDate :
17-19 Dec. 2010
Firstpage :
1154
Lastpage :
1157
Abstract :
Graphics Processing Unit (GPU) has become an attractive coprocessor for scientific computing due to its massive processing capability. The sparse matrix-vector multiplication (SpMV) is a critical operation in a wide variety of scientific and engineering applications, such as sparse linear algebra and image processing. This paper presents an auto-tuning framework that can automatically compute and select CUDA parameters for SpMV to obtain the optimal performance on specific GPUs. The framework is evaluated on two NVIDIA GPU platforms, GeForce 9500 GTX and GeForce GTX 295.
Keywords :
coprocessors; matrix multiplication; sparse matrices; tuning; GeForce 9500 GTX; GeForce GTX 295; NVIDIA GPU platforms; SpMV; auto-tuning CUDA parameters; auto-tuning framework; coprocessor; graphics processing unit; sparse matrix-vector multiplication; Finite element methods; Graphics processing unit; Instruction sets; Kernel; Performance evaluation; Sparse matrices; Tuning; CUDA; GPU; performance; sparse matrix-vector multiplication;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational and Information Sciences (ICCIS), 2010 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-8814-8
Electronic_ISBN :
978-0-7695-4270-6
Type :
conf
DOI :
10.1109/ICCIS.2010.285
Filename :
5709485
Link To Document :
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