Abstract
For the time-domain linear system with diagonal sparse matrices, based on the popular preconditioned generalized minimum residual method (GMRES), we proposed an efficient solving algorithm on the graphics processing unit (GPU), which is called T-GMRES. In the proposed T-GMRES, three are the following novelties: (1) a new sparse storage format BRCSD is presented to alleviate the drawback of the diagonal format (DIA) that a large number of zeros are filled to maintain the diagonal structure when many diagonals are far away from the main diagonal; (2) an efficient sparse matrix-vector multiplication on GPU for BRCSD is proposed; and (3) for assembling the sparse matrix for BRCSD and the vector efficiently on GPU, a new kernel is suggested. The experimental results have validated the high efficiency and good performance of our proposed algorithm.
The research has been supported by the Natural Science Foundation of Zhejiang Province, China under grant number LY17F020021, and the Natural Science Foundation of Jiangsu Province, China under grant number BK20171480, and the Open Project Program of the State Laboratory of Computer Architecture under grant number CARCH201603, and the Qing Lan Project of Nanjing Normal University.
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Xia, Y., Gao, J., He, G. (2019). A Parallel Solving Algorithm on GPU for the Time-Domain Linear System with Diagonal Sparse Matrices. In: Ren, R., Zheng, C., Zhan, J. (eds) Big Scientific Data Benchmarks, Architecture, and Systems. SDBA 2018. Communications in Computer and Information Science, vol 911. Springer, Singapore. https://doi.org/10.1007/978-981-13-5910-1_7
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