GPU Accelerated Parallel Cholesky Factorization
2011 ◽
Vol 148-149
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pp. 1370-1373
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Keyword(s):
One of the fundamental problems in scientific computing is to find solutions for linear equation systems. For finite element problem, Cholesky factorization is often used to solve symmetric positive definite matrices. In this paper, Cholesky factorization is massively parallelized and three different optimization methods - highly parallel factorization, tile strategy and memory scheduling are used to accelerate Cholesky factorization effectively. A novel algorithm using OpenCL is implemented. Testing on GPU shows that performance of the algorithm increases with the dimension of matrix, reaching 785.41GFlops, about 50x times speedup. Cholesky factorization is remarkably improved with OpenCL on GPU.
2010 ◽
Vol 31
(5)
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pp. 2899-2920
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1986 ◽
Vol 296
(2)
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pp. 621-621
2011 ◽
Vol 225-226
◽
pp. 970-973
2019 ◽
Vol 352
◽
pp. 300-323
2019 ◽
Vol 16
(3)
◽
pp. 036016
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2019 ◽
Vol 78
(9)
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pp. 2933-2943
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