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How To Optimize With Sparse Data?


sinelogixtech

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parse matrix(DATA)-vector multiply is an important operation in a wide range of problems. One of the key factors determining the performance of this operation is sustained memory bandwidth. In the IBM POWER architecture, there is a hardware component called a prefetch data stream that can significantly increase sustained memory bandwidth. We have developed a new family of storage formats for sparse matrices that exploits this capability. Test results show that our new streamed storage formats can significantly improve the performance of sparse matrix and vector multiply on IBM POWER processors, compared to traditional compressed sparse row and block compressed sparse row formats. The new formats also provide a benefit on x86 processors.

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Many data mining algorithms rely on eigenvalue computations or iterative linear solvers in which the running time is dominated by sparse matrix-vector products. Sparse matrix-vector multiplication on modern machines often runs one to two orders of magnitude slower than peak hardware performance, and because of their lack of structure, the worst performance is often observed for matrices from text retrieval and other data mining applications. In this paper we explore a set of memory hierarchy optimizations for sparse matrix-vector multiplication, concentrating on matrices that arises in text and image retrieval. We also consider algorithms that multiply the sparse matrix by a set of vectors.

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