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GTC ON-DEMAND

Developer - Algorithms
Presentation
Media
Deriving Parallelism and GPU Acceleration of Algorithms with Inter-Dependent Data Fields
Speakers:
James Malcolm
- Accelereyes
Abstract:
This poster presents an approach to derive parallelism in algorithms that involve building sparse matrix that represents relationships between inter-dependent data fields and enhancing its performance on the GPU. This work compares the algorithm performance on the GPU to its CPU variant that employs the traditional sparse matrix-vector multiplication (SpMV) approach. We have also compared our algorithm performance with CUSP SpMV on GPU. The softwares used in this work are MATLAB and Jacket - GPU engine for MATLAB
 
Topics:
Developer - Algorithms
Type:
Poster
Event:
GTC Silicon Valley
Year:
2010
Session ID:
P10A10
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