{"id":4992,"date":"2011-08-03T13:39:54","date_gmt":"2011-08-03T10:39:54","guid":{"rendered":"http:\/\/hgpu.org\/?p=4992"},"modified":"2011-08-03T13:39:54","modified_gmt":"2011-08-03T10:39:54","slug":"exploring-reconfigurable-architectures-for-explicit-finite-difference-option-pricing-models","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4992","title":{"rendered":"Exploring reconfigurable architectures for explicit finite difference option pricing models"},"content":{"rendered":"<p>This paper explores the application of reconfigurable hardware and graphics processing units (GPUs) to the acceleration of financial computation using the finite difference (FD) method. A parallel pipelined architecture has been developed to support concurrent valuation of independent options with high pricing throughput. Our FPGA implementation running at 106 MHz on an xc4vlx160 device demonstrates a speed up of 12 times over a Pentium 4 processor at 3.6 GHz in single-precision arithmetic; while the FPGA is 3.6 times slower than a Tesla C1060 240-Core GPU at 1.3 GHz, it is 9 times more energy efficient.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper explores the application of reconfigurable hardware and graphics processing units (GPUs) to the acceleration of financial computation using the finite difference (FD) method. A parallel pipelined architecture has been developed to support concurrent valuation of independent options with high pricing throughput. Our FPGA implementation running at 106 MHz on an xc4vlx160 device demonstrates [&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":[89,576,3],"tags":[14,1804,327,377,20,395,199],"class_list":["post-4992","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-finance","category-paper","tag-cuda","tag-finance","tag-finite-difference","tag-fpga","tag-nvidia","tag-nvidia-geforce-8600-gt","tag-tesla-c1060"],"views":1990,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4992","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=4992"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4992\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4992"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4992"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4992"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}