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GTC ON-DEMAND

Life & Material Science
Presentation
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Reformulating Algorithms for the GPU
Speakers:
Michela Taufer, Narayan Ganesan
- University of Delaware
Abstract:
Important applications in signal, data processing and bioinformatics that use dynamic programming are difficult to parallelize due to intrinsic data dependencies. We demonstrate a novel technique to extract parallelism out of data dependent algorithms and reformulate the same for GPUs. This simple technique breaks the dependencies and resolves them at an optimal point later in time, thus obtaining remarkable speedup on GPUs. We present a case study from computational biology i.e., protein motif-finding. We also present how the same technique can be extended and applied to other relevant problems such as gene-prediction and phylogenetics.
 
Topics:
Life & Material Science, Developer - Algorithms, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2010
Session ID:
2034
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