Towards improved parallelism through order reduction of accessing data in nD matrices

Author:

Rahnama Behnam

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Reference16 articles.

1. NVidia (2012) Kepler K20 $$\times $$ × application performance technical brief. [online]. http://www.nvidia.com/docs/IO/122874/K20-and-K20X-application-performance-technical-brief.pdf

2. NVidia CUDA (2013) The NVIDIA CUDA basic linear algebra subroutines. [online]. https://developer.nvidia.com/cublas

3. Innovative Computing Laboratory (ICL) (2013) Matrix algebra on GPU and multicore architectures. [online]. http://icl.cs.utk.edu/magma/

4. Church PC et al (2011) Design of multiple sequence alignment algorithms on parallel, distributed memory supercomputers. In: Engineering in medicine and biology society. EMBC. IEEE, Boston, pp 924–927

5. Nath R, Tomov S, Dong T, Dongarra J (2011) Optimizing symmetric dense matrix-vector multiplication. High performance computing, networking, storage and analysis (SC). IEEE, Seatle, pp 1–10

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