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ICASSP
2010
IEEE
13 years 6 months ago
Fast signal analysis and decomposition on graphs using the Sparse Matrix Transform
Recently, the Sparse Matrix Transform (SMT) has been proposed as a tool for estimating the eigen-decomposition of high dimensional data vectors [1]. The SMT approach has two major...
Leonardo R. Bachega, Guangzhi Cao, Charles A. Boum...
FCCM
2007
IEEE
165views VLSI» more  FCCM 2007»
13 years 7 months ago
Sparse Matrix-Vector Multiplication Design on FPGAs
Creating a high throughput sparse matrix vector multiplication (SpMxV) implementation depends on a balanced system design. In this paper, we introduce the innovative SpMxV Solver ...
Junqing Sun, Gregory D. Peterson, Olaf O. Storaasl...
ACL
1997
13 years 7 months ago
Fast Context-Free Parsing Requires Fast Boolean Matrix Multiplication
Valiant showed that Boolean matrix multiplication (BMM) can be used for CFG parsing. We prove a dual result: CFG parsers running in time O([Gl[w[3-e) on a grammar G and a string w...
Lillian Lee
JACM
2002
83views more  JACM 2002»
13 years 5 months ago
Fast context-free grammar parsing requires fast boolean matrix multiplication
In 1975, Valiant showed that Boolean matrix multiplication can be used for parsing contextfree grammars (CFGs), yielding the asympotically fastest (although not practical) CFG par...
Lillian Lee
ICPP
2009
IEEE
14 years 14 days ago
Perfomance Models for Blocked Sparse Matrix-Vector Multiplication Kernels
—Sparse Matrix-Vector multiplication (SpMV) is a very challenging computational kernel, since its performance depends greatly on both the input matrix and the underlying architec...
Vasileios Karakasis, Georgios I. Goumas, Nectarios...