{"id":8249,"date":"2012-09-22T16:20:46","date_gmt":"2012-09-22T13:20:46","guid":{"rendered":"http:\/\/hgpu.org\/?p=8249"},"modified":"2012-09-22T16:20:46","modified_gmt":"2012-09-22T13:20:46","slug":"exploration-of-parallelization-frameworks-for-computational-finance","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8249","title":{"rendered":"Exploration of Parallelization Frameworks for Computational Finance"},"content":{"rendered":"<p>This paper presents a comparison of parallelization frameworks for efficient execution of computational finance workloads. We use a Value-at-Risk (VaR) workload to evaluate OpenCL and OpenMP parallelization frameworks on multi-core CPUs as opposed to GPUs. In addition, we study the impact of SMT on performance using GCC (4.4) and IBM XLC (11.01) compilers for both single-precision and double-precision codes. We use an 8-core, 4-way SMT IBM Power7 with Linux (RHEL 6.0, 2.6.32 kernel) to evaluate OpenCL and OpenMP. Using the IBM XLC compiler, 2-way SMT is able to provide over 30% average improvement as compared to 1 SMT thread per core, whereas, 4-way SMT is able to provide over 50% average improvement as compared to 1 SMT thread per core.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a comparison of parallelization frameworks for efficient execution of computational finance workloads. We use a Value-at-Risk (VaR) workload to evaluate OpenCL and OpenMP parallelization frameworks on multi-core CPUs as opposed to GPUs. In addition, we study the impact of SMT on performance using GCC (4.4) and IBM XLC (11.01) compilers for both [&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":[576,90,3],"tags":[592,1804,1793],"class_list":["post-8249","post","type-post","status-publish","format-standard","hentry","category-finance","category-opencl","category-paper","tag-computational-finance","tag-finance","tag-opencl"],"views":2555,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8249","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=8249"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8249\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8249"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8249"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8249"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}