WASET
	@article{(Open Science Index):https://publications.waset.org/pdf/2362,
	  title     = {Accelerating Sparse Matrix Vector Multiplication on Many-Core GPUs},
	  author    = {Weizhi Xu and  Zhiyong Liu and  Dongrui Fan and  Shuai Jiao and  Xiaochun Ye and  Fenglong Song and  Chenggang Yan},
	  country	= {},
	  institution	= {},
	  abstract     = {Many-core GPUs provide high computing ability and
substantial bandwidth; however, optimizing irregular applications
like SpMV on GPUs becomes a difficult but meaningful task. In this
paper, we propose a novel method to improve the performance of
SpMV on GPUs. A new storage format called HYB-R is proposed to
exploit GPU architecture more efficiently. The COO portion of the
matrix is partitioned recursively into a ELL portion and a COO
portion in the process of creating HYB-R format to ensure that there
are as many non-zeros as possible in ELL format. The method of
partitioning the matrix is an important problem for HYB-R kernel, so
we also try to tune the parameters to partition the matrix for higher
performance. Experimental results show that our method can get
better performance than the fastest kernel (HYB) in NVIDIA-s
SpMV library with as high as 17% speedup.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {6},
	  number    = {1},
	  year      = {2012},
	  pages     = {11 - 18},
	  ee        = {https://publications.waset.org/pdf/2362},
	  url   	= {https://publications.waset.org/vol/61},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 61, 2012},
	}