{"id":2922,"date":"2011-02-21T14:49:03","date_gmt":"2011-02-21T14:49:03","guid":{"rendered":"http:\/\/hgpu.org\/?p=2922"},"modified":"2011-02-21T14:49:03","modified_gmt":"2011-02-21T14:49:03","slug":"a-massively-parallel-framework-using-p-systems-and-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2922","title":{"rendered":"A massively parallel framework using P systems and GPUs"},"content":{"rendered":"<p>Since CUDA programing model appeared on the general purpose computations, the developers can extract all the power contained in GPUs (Graphics Processing Unit) across many computational domains. Among these domains, P systems or membrane systems provide a high level computational modeling framework that allows, in theory, to obtain polynomial time solutions to NP-complete problems by trading time for space, and also to model biological phenomena in the area of computational systems biology. P systems are massively parallel distributed devices and their computation can be divided in two levels of parallelism: membranes, that can be expressed as blocks in CUDA programming model; and objects, that can be expressed as threads in CUDA programming model. In this paper, we present our initial ideas of developing a simulator for the class of recognizer P systems with active membranes by using the CUDA programing model to exploit the massively parallel nature of those systems at maximum. Experimental results of a preliminary version of our simulator on a Tesla C1060 GPU show a 60X of speed-up compared to the sequential code.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Since CUDA programing model appeared on the general purpose computations, the developers can extract all the power contained in GPUs (Graphics Processing Unit) across many computational domains. Among these domains, P systems or membrane systems provide a high level computational modeling framework that allows, in theory, to obtain polynomial time solutions to NP-complete problems by [&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":[10,11,89,3],"tags":[1781,1782,14,20,752,199],"class_list":["post-2922","post","type-post","status-publish","format-standard","hentry","category-biology","category-computer-science","category-nvidia-cuda","category-paper","tag-biology","tag-computer-science","tag-cuda","tag-nvidia","tag-p-systems","tag-tesla-c1060"],"views":2271,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2922","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=2922"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2922\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2922"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2922"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2922"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}