We have developed GSNP, a software package with GPU acceleration, for single-nucleotide polymorphism detection on DNA sequences generated from second-generation sequencing equipment. Compared with SOAPsnp, a popular, high-performance CPU-based SNP detection tool, GSNP has several distinguishing features: First, we design a sparse data representation format to reduce memory access as well as branch divergence. Second, we develop a multipass sorting network to efficiently sort a large number of small arrays on the GPU. Third, we compute a table of frequently used scores once to avoid repeated, expensive computation and to reduce random memory access. Fourth, we apply customized compression schemes to the output data to improve the I\/O performance. As a result, on a server equipped with an Intel Xeon E5630 2.53 GHZ CPU and an NVIDIA Tesla M2050 GPU, it took GSNP about two hours to analyze a whole human genome dataset whereas the CPU-based, single-threaded SOAPsnp took three days for the same task on the same machine.<\/p>\n","protected":false},"excerpt":{"rendered":"
We have developed GSNP, a software package with GPU acceleration, for single-nucleotide polymorphism detection on DNA sequences generated from second-generation sequencing equipment. Compared with SOAPsnp, a popular, high-performance CPU-based SNP detection tool, GSNP has several distinguishing features: First, we design a sparse data representation format to reduce memory access as well as branch divergence. Second, […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[10,3],"tags":[123,1781,832,20,176,9,931],"class_list":["post-6251","post","type-post","status-publish","format-standard","hentry","category-biology","category-paper","tag-bioinformatics","tag-biology","tag-compression","tag-nvidia","tag-package","tag-sorting","tag-tesla-m2050"],"views":2493,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6251","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6251"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6251\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6251"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6251"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6251"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}