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Title: A GPU framework for sparse matrix vector multiplication
Authors: Neelima, B.
Ram Mohana Reddy, Guddeti
Raghavendra, P.S.
Issue Date: 2014
Citation: Proceedings - IEEE 13th International Symposium on Parallel and Distributed Computing, ISPDC 2014, 2014, Vol., , pp.51-58
Abstract: The hardware and software evolutions related to Graphics Processing Units (GPUs), for general purpose computations, have changed the way the parallel programming issues are addressed. Many applications are being ported onto GPU for achieving performance gain. The GPU execution time is continuously optimized by the GPU programmers while optimizing pre-GPU computation overheads attracted the research community in the recent past. While GPU executes the programs given by a CPU, pre-GPU computation overheads does exists and should be optimized for a better usage of GPUs. The GPU framework proposed in this paper improves the overall performance of the application by optimizing pre-GPU computation overheads along with GPU execution time. This paper proposes a sparse matrix format prediction tool to predict an optimal sparse matrix format to be used for a given input matrix by analyzing the input sparse matrix and considering pre-GPU computation overheads. The sparse matrix format predicted by the proposed method is compared against the best performing sparse matrix formats posted in the literature. The proposed model is based on the static data that is available from the input directly and hence the prediction overhead is very small. Compared to GPU specific sparse format prediction, the proposed model is more inclusive and precious in terms of increasing overall application's performance. � 2014 IEEE.
Appears in Collections:2. Conference Papers

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