{"id":5665,"date":"2011-09-23T17:45:31","date_gmt":"2011-09-23T14:45:31","guid":{"rendered":"http:\/\/hgpu.org\/?p=5665"},"modified":"2011-09-23T17:45:31","modified_gmt":"2011-09-23T14:45:31","slug":"accelerating-reaction-diffusion-simulations-with-general-purpose-graphics-processing-units","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5665","title":{"rendered":"Accelerating reaction-diffusion simulations with general-purpose graphics processing units"},"content":{"rendered":"<p>SUMMARY: We present a massively parallel stochastic simulation algorithm (SSA) for reaction-diffusion systems implemented on Graphics Processing Units (GPUs). These are designated chips optimized to process a high number of floating point operations in parallel, rendering them well-suited for a range of scientific high-performance computations. Newer GPU generations provide a high-level programming interface which turns them into General-Purpose Graphics Processing Units (GPGPUs). Our SSA exploits GPGPU architecture to achieve a performance gain of two orders of magnitude over the fastest existing implementations on conventional hardware. AVAILABILITY: The software is freely available at http:\/\/www.csse.monash.edu.au\/~berndm\/inchman\/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>SUMMARY: We present a massively parallel stochastic simulation algorithm (SSA) for reaction-diffusion systems implemented on Graphics Processing Units (GPUs). These are designated chips optimized to process a high number of floating point operations in parallel, rendering them well-suited for a range of scientific high-performance computations. Newer GPU generations provide a high-level programming interface which turns [&hellip;]<\/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":[36,10,89,3],"tags":[1787,123,1781,14,20,176,144,861],"class_list":["post-5665","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-biology","category-nvidia-cuda","category-paper","tag-algorithms","tag-bioinformatics","tag-biology","tag-cuda","tag-nvidia","tag-package","tag-rendering","tag-stochastic-simulation"],"views":1878,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5665","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=5665"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5665\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5665"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5665"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5665"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}