Evaluating the use of GPUs in liver image segmentation and HMMER database searches

In this paper we present the results of parallelizing two life sciences applications, Markov random fieldsbased (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 130x speedup, and our hmmsearch implementation achieves up to 38x 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.

• Type: Conference
• Year: 2009
• Where: IPPS
• Authors: John Paul Walters, Vidyananth Balu, Suryaprakash Kompalli, Vipin Chaudhary
• Keywords: Distributed And Parallel Computing, HMMER’s Viterbi Algorithm, IPPS 2009, Nvidia 8800 Gtx, Optimized Mrf Kernel