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» Improved Matrix Interpretation
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118
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FPGA
2005
ACM
195views FPGA» more  FPGA 2005»
15 years 6 months ago
Sparse Matrix-Vector multiplication on FPGAs
Floating-point Sparse Matrix-Vector Multiplication (SpMXV) is a key computational kernel in scientific and engineering applications. The poor data locality of sparse matrices sig...
Ling Zhuo, Viktor K. Prasanna
ISCAS
2008
IEEE
217views Hardware» more  ISCAS 2008»
15 years 7 months ago
Approximate L0 constrained non-negative matrix and tensor factorization
— Non-negative matrix factorization (NMF), i.e. V ≈ WH where both V, W and H are non-negative has become a widely used blind source separation technique due to its part based r...
Morten Mørup, Kristoffer Hougaard Madsen, L...
134
Voted
EUROPAR
2010
Springer
15 years 1 months ago
Optimized Dense Matrix Multiplication on a Many-Core Architecture
Abstract. Traditional parallel programming methodologies for improving performance assume cache-based parallel systems. However, new architectures, like the IBM Cyclops-64 (C64), b...
Elkin Garcia, Ioannis E. Venetis, Rishi Khan, Guan...
93
Voted
ALMOB
2008
124views more  ALMOB 2008»
15 years 22 days ago
A scoring matrix approach to detecting miRNA target sites
Background: Experimental identification of microRNA (miRNA) targets is a difficult and time consuming process. As a consequence several computational prediction methods have been ...
Simon Moxon, Vincent Moulton, Jan T. Kim
SC
1995
ACM
15 years 4 months ago
Parallel Matrix-Vector Product Using Approximate Hierarchical Methods
Matrix-vector products (mat-vecs) form the core of iterative methods used for solving dense linear systems. Often, these systems arise in the solution of integral equations used i...
Ananth Grama, Vipin Kumar, Ahmed H. Sameh