{"id":5075,"date":"2011-08-10T16:30:31","date_gmt":"2011-08-10T13:30:31","guid":{"rendered":"http:\/\/hgpu.org\/?p=5075"},"modified":"2011-08-10T16:30:31","modified_gmt":"2011-08-10T13:30:31","slug":"pricing-the-american-option-using-reconfigurable-hardware","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5075","title":{"rendered":"Pricing the American Option Using Reconfigurable Hardware"},"content":{"rendered":"<p>We present a novel reconfigurable hardware architecture for accelerating American option pricing using the binomial lattice algorithm. The architecture provides double precision floating point pricing, evaluating up to N = 64,000 time steps in the binomial lattice. Advanced memory management techniques and optimized control logic allow for 4-way parallelism on a single-asset evaluation. These techniques achieve a 73-times speedup over an optimized CPU implementation, and a considerable improvement over the best previous reconfigurable hardware implementation. A significant advantage of our approach is that the speed up is on a per asset basis whereas all previous approaches on FPGA and GPU architectures achieve their speed up by evaluating many assets in parallel.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a novel reconfigurable hardware architecture for accelerating American option pricing using the binomial lattice algorithm. The architecture provides double precision floating point pricing, evaluating up to N = 64,000 time steps in the binomial lattice. Advanced memory management techniques and optimized control logic allow for 4-way parallelism on a single-asset evaluation. These techniques [&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":[36,89,576,3],"tags":[1787,14,1804,327,377,20,244],"class_list":["post-5075","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-finance","category-paper","tag-algorithms","tag-cuda","tag-finance","tag-finite-difference","tag-fpga","tag-nvidia","tag-tesla-s1070"],"views":2234,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5075","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=5075"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5075\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5075"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5075"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5075"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}