Abstract

In this paper we present the results of parallelizing two life sciences applications, Markov random fields-based (MRF) liver segmentation and HMMER's Viterbi algorithm, using GPUs. We relate our experiences in porting both applications to the GPU as well as the techniques and optimizations that are most beneficial. The unique characteristics of both algorithms are demonstrated by implementations on an NVIDIA 8800 GTX Ultra using the CUDA programming environment. We test multiple enhancements in our GPU kernels in order to demonstrate the effectiveness of each strategy. Our optimized MRF kernel achieves over 130times speedup, and our hmmsearch implementation achieves up to 38times speedup. We show that the differences in speedup between MRF and hmmsearch is due primarily to the frequency at which the hmmsearch must read from the GPU's DRAM.

Keywords

SpeedupComputer scienceCUDAPortingParallel computingKernel (algebra)General-purpose computing on graphics processing unitsViterbi algorithmArtificial intelligenceHidden Markov modelComputer graphics (images)Programming languageSoftware

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Publication Info

Year
2009
Type
article
Pages
1-12
Citations
61
Access
Closed

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John Paul Walters, Vidyananth Balu, Suryaprakash Kompalli et al. (2009). Evaluating the use of GPUs in liver image segmentation and HMMER database searches. 2009 IEEE International Symposium on Parallel & Distributed Processing , 1-12. https://doi.org/10.1109/ipdps.2009.5161073

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DOI
10.1109/ipdps.2009.5161073

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