{"id":12417,"date":"2014-07-01T23:57:05","date_gmt":"2014-07-01T20:57:05","guid":{"rendered":"http:\/\/hgpu.org\/?p=12417"},"modified":"2014-07-01T23:57:05","modified_gmt":"2014-07-01T20:57:05","slug":"parallelizing-the-cellular-potts-model-on-gpu-and-multi-core-cpu-an-opencl-cross-platform-study","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12417","title":{"rendered":"Parallelizing the cellular potts model on GPU and multi-core CPU: An OpenCL cross-platform study"},"content":{"rendered":"<p>In this paper, we present the analysis and development of a cross-platform OpenCL parallelization of the Cellular Potts Model (CPM). In general, the evolution of the CPM is time-consuming. Using data-parallel programming model such as CUDA can accelerate the process, but it is highly dependent on the hardware type and manufacturer. Recently, OpenCL has attracted a lot of attention and been widely used by researchers. OpenCL provides a flexible solution, which allows us to come up with an implementation that can execute on both GPUs and multi-core CPUs regardless of the hardware type and manufacturer. Some optimizations are also made for both GPU and multi-core CPU implementations of the CPM, and we also propose a resource management method, MLBBRM. Experimental results show that the developed optimized algorithms for both GPU and multi-core CPU have an average speedup of about 30x and 8x respectively compared with the single threaded CPU implementation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we present the analysis and development of a cross-platform OpenCL parallelization of the Cellular Potts Model (CPM). In general, the evolution of the CPM is time-consuming. Using data-parallel programming model such as CUDA can accelerate the process, but it is highly dependent on the hardware type and manufacturer. Recently, OpenCL has attracted [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[10,11,90,3],"tags":[7,1422,1781,1782,1793],"class_list":["post-12417","post","type-post","status-publish","format-standard","hentry","category-biology","category-computer-science","category-opencl","category-paper","tag-ati","tag-ati-radeon-hd-7870","tag-biology","tag-computer-science","tag-opencl"],"views":2102,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12417","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=12417"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12417\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12417"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12417"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12417"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}