{"id":19586,"date":"2020-02-02T16:38:43","date_gmt":"2020-02-02T14:38:43","guid":{"rendered":"https:\/\/hgpu.org\/?p=19586"},"modified":"2020-02-02T16:38:43","modified_gmt":"2020-02-02T14:38:43","slug":"optimization-of-a-discontinuous-galerkin-solver-with-opencl-and-starpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=19586","title":{"rendered":"Optimization of a discontinuous Galerkin solver with OpenCL and StarPU"},"content":{"rendered":"<p>Since the recent advance in microprocessor design, the optimization of computing software becomes more and more technical. One of the difficulties is to transform sequential algorithms into parallel ones. A possible solution is the task-based design. In this approach, it is possible to describe the parallelization possibilities of the algorithm automatically. The task-based design is also a good strategy to optimize software in an incremental way. The objective of this paper is to describe a practical experience of a task-based parallelization of a Discontinuous Galerkin method in the context of electromagnetic simulations. The task-based description is managed by the StarPU runtime. Additional acceleration is obtained by OpenCL.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Since the recent advance in microprocessor design, the optimization of computing software becomes more and more technical. One of the difficulties is to transform sequential algorithms into parallel ones. A possible solution is the task-based design. In this approach, it is possible to describe the parallelization possibilities of the algorithm automatically. The task-based design is [&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":[36,157,90,3],"tags":[1787,1796,20,2008,1793,176,551,1931],"class_list":["post-19586","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-mathematics","category-opencl","category-paper","tag-algorithms","tag-mathematics","tag-nvidia","tag-nvidia-geforce-gtx-1050","tag-opencl","tag-package","tag-pdes","tag-tesla-p100"],"views":3256,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/19586","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=19586"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/19586\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=19586"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=19586"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=19586"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}