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GTC ON-DEMAND

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Abstract:
We present a novel sparse matrix formulation that uses modified merge algorithms. In contrast to conventional sparse matrix algorithms, which suffer from data divergence within large work arrays, this method allows us to maintain contiguous data layouts at all stages of the process. This also allows us to take advantage of ideas from optimized parallel merge algorithms for efficient GPU performance. Performance comparisons are presented. We are motivated by quantum mechanical simulations of atomic systems, which are limited by the computational cost of the eigenvalue solution. Linear scaling methods have been developed which require multiplication of large sparse matrices, where the number of non-zeros per row can be relatively large although still much less than the matrix dimension.
We present a novel sparse matrix formulation that uses modified merge algorithms. In contrast to conventional sparse matrix algorithms, which suffer from data divergence within large work arrays, this method allows us to maintain contiguous data layouts at all stages of the process. This also allows us to take advantage of ideas from optimized parallel merge algorithms for efficient GPU performance. Performance comparisons are presented. We are motivated by quantum mechanical simulations of atomic systems, which are limited by the computational cost of the eigenvalue solution. Linear scaling methods have been developed which require multiplication of large sparse matrices, where the number of non-zeros per row can be relatively large although still much less than the matrix dimension.  Back
 
Topics:
Life & Material Science, Developer - Algorithms, Computational Physics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5443
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