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GTC ON-DEMAND

Developer - Algorithms
Presentation
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Accelerating Symmetric Matrix-Vector Product on Fermi GPU
Abstract:
We aim in the work presented here to describe an optimized numerical kernels computing the symmetric matrix-vector product (Level 2 BLAS) on the last NVIDIA TESLA GPU family, codenamed Fermi (C2070). Due to its inherent memory-bound nature, this kernel represents one of the most critical operations in computing the tridiagonal form of a symmetric dense matrix, which is the preprocessing step toward calculating the eigenpairs. Using a novel design to address the irregular memory accesses by hiding latency and increasing bandwidth, our preliminary asymptotic results show up to 3.5 fold speedups over existing numerical libraries.
 
Topics:
Developer - Algorithms
Type:
Poster
Event:
GTC Silicon Valley
Year:
2012
Session ID:
P2401
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