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GTC ON-DEMAND

Genomics & Bioinformatics
Presentation
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Restricting the Seed-and-Extend Search Space in GPU-Based Short-Read Alignment
Abstract:
Most research into the use of GPUs for biological sequence alignment has focused on the choice and implementation of appropriate parallel algorithms for sequence matching. This strategy has yielded a number of GPU-based implementations with speeds 5 to 10 times faster than CPU implementations with comparable sensitivity and mapping quality. We have taken a different approach to the use of GPUs by implementing a series of CUDA kernels that filter the set of reference locations at which to compute seed-and-extend alignments, thereby decreasing the amount of parallel sequence-matching computation and improving the overall throughput of the GPU/CPU pipeline. Even without extreme CUDA code optimization, we observe increased sensitivity (i.e., a larger number of reported valid mappings) with throughput as good as or better than existing GPU-based sequence aligners.
 
Topics:
Genomics & Bioinformatics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2014
Session ID:
S4248
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