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CUDA-Based Jacobi's Iterative Method

Authors: Zhihui Zhang; Qinghai Miao; Ying Wang;

CUDA-Based Jacobi's Iterative Method

Abstract

Solving linear equations is a common problem in the fields of science and engineering. Accelerating its solving process is of great significance. Modern GPUs are high performance many-core processors fit for large scale parallel computing. They provide us a novel way for accelerating the solving process. A GPU based parallel Jacobi’s iterative solver for dense linear equations is presented in this paper. First, we introduce the backgrounds for accelerating solving linear equations together with GPUs and the corresponding parallel platform CUDA on it. Then we implement Jacobi’s iterative method on CUDA. Finally, we compare the experimental results of CUDA programs on GPU with traditional programs on CPU. Experiments show that it obtains a speedup of approximately 59 times with single floating point at a low precision, 19 times with double at a high precision.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Average
Average
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