Effective Sparse Matrix Representation For The GPU Architectures
نویسندگان
چکیده
منابع مشابه
Effective Sparse Matrix Representation for the GPU Architectures
General purpose computation on graphics processing unit (GPU) is prominent in the high performance computing era of this time. Porting or accelerating the data parallel applications onto GPU gives the default performance improvement because of the increased computational units. Better performances can be seen if application specific fine tuning is done with respect to the architecture under con...
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Graphics processors are increasingly used in scientific applications due to their high computational power, which comes from hardware with multiple-level parallelism and memory hierarchy. Sparse matrix computations frequently arise in scientific applications, for example, when solving PDEs on unstructured grids. However, traditional sparse matrix algorithms are difficult to efficiently parallel...
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Sparse Matrix-Matrix multiplication is a key kernel that has applications in several domains such as scientific computing and graph analysis. Several algorithms have been studied in the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse matrixmatrix multiplication with a focus on performance portability across different high performance computing archite...
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ژورنال
عنوان ژورنال: International Journal of Computer Science, Engineering and Applications
سال: 2012
ISSN: 2231-0088
DOI: 10.5121/ijcsea.2012.2213