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Abstract: Sparse matrix-vector multiplication is an important computational kernel that tends to perform poorly on modern processors, largely because of its high ratio of memory operations to arithmetic operations. Optimizing this algorithm is difficult, both because of the complexity of memory systems and because the performance is highly dependent on the nonzero structure of the matrix. The Sparsity system is designed to address these problem by allowing users to automatically build sparse matrix... (Update)
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BibTeX entry: (Update)
Eun-Jin Im and Katherine Yelick. Optimizing sparse matrix computations for register reuse in sparsity. In V.N.Alexandrov, J.J. Dongarra, and C.J.K.Tan, editors, Proceedings of the http://citeseer.ist.psu.edu/451044.html More
@article{ im01optimizing,
author = "Eun-Jin Im and Katherine Yelick",
title = "Optimizing Sparse Matrix Computations for Register Reuse in {SPARSITY}",
journal = "Lecture Notes in Computer Science",
volume = "2073",
pages = "127+",
year = "2001",
url = "citeseer.ist.psu.edu/451044.html" }
Citations (may not include all citations):376 The cache performance and optimizations of blocked algorithm.. (context) - Lam, Rothberg et al. - 1991
25 Improving memory-system performance of sparse matrix-vector .. - Toledo - 1997
23 Optimizing the Performance of Sparse Matrix - Vector Multipl.. (context) - Im - 2000
18 Technical Report ANL-95/11 - Revision (context) - Balay, Gropp et al. - 2000
7 Compiler Support for Sparse Matrix Computations (context) - Bik - 1996
4 Documentation for the Basic Linear Algebra Subprograms (context) - Forum - 1999
3 Technical Report TRPA (context) - Marques, Decsription et al. - 1995
The graph only includes citing articles where the year of publication is known.
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