SIMD Parallel Sparse Matrix-Vector and Transposed-Matrix-Vector Multiplication in DD Precision-Reference-Cited by-同舟云学术

SIMD Parallel Sparse Matrix-Vector and Transposed-Matrix-Vector Multiplication in DD Precision

Published:2017 Issue: Volume: Page:21-34
ISSN:0302-9743
Container-title:High Performance Computing for Computational Science – VECPAR 2016
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Author:

Hishinuma Toshiaki,Hasegawa Hidehiko,Tanaka Teruo

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-61982-8_4

Reference14 articles.

1. Kouya, T.: A highly efficient implementation of multiple precision sparse matrix-vector multiplication and its application to product-type Krylov subspace methods. Int. J. Numer. Methods Appl. 7(2), 107–119 (2012)

2. Bailey, D.H.: High-precision floating-point arithmetic in scientific computation. Comput. Sci. Eng. 7, 54–61 (2005)

3. Intel. http://software.intel.com/en-us/articles/intel-intrinsics-guide

4. Lecture Notes in Computer Science;T Hishinuma,2014

5. Barrett, R., et al.: Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods, pp. 57–65. SIAM (1994)

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