{"id":10577,"date":"2013-09-23T22:59:43","date_gmt":"2013-09-23T19:59:43","guid":{"rendered":"http:\/\/hgpu.org\/?p=10577"},"modified":"2013-09-23T22:59:43","modified_gmt":"2013-09-23T19:59:43","slug":"multi-gpu-acceleration-of-black-scholes-equation-based-option-pricing","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10577","title":{"rendered":"Multi-GPU Acceleration of Black-Scholes Equation based Option Pricing"},"content":{"rendered":"<p>In high-frequency trading of option, &quot;milliseconds earn or lose millions&quot;, the computational speed of predicting option price is the crucial factor for option traders to efficiently decide the price and evaluate the corresponding risk.Black-Scholes equation is a mathematical equation describing the option pricing over time. Multi-GPU is a recently developed platform for high-performance computing, which can be applied to high-efficient solving partial differential equation. In this paper, besides the conventional tridiagonal matrix algorithm, we develop multi-GPU block cyclic reduction based implicit scheme and multi-GPU block cyclic reduction based Crank-Nicolson scheme for Black-Scholes equation.Computational results show that, multi-GPU block cyclic reduction based schemes for Black-Scholes equation provides highly efficient prediction of option price along with iterations (time).Computational results also show that, multi-GPU significantly accelerate the solution speed of Black-Schole equations for option pricing.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In high-frequency trading of option, &quot;milliseconds earn or lose millions&quot;, the computational speed of predicting option price is the crucial factor for option traders to efficiently decide the price and evaluate the corresponding risk.Black-Scholes equation is a mathematical equation describing the option pricing over time. Multi-GPU is a recently developed platform for high-performance computing, which [&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":[36,89,576,3],"tags":[1787,14,810,1804,20,1436,550,551],"class_list":["post-10577","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-finance","category-paper","tag-algorithms","tag-cuda","tag-differential-equations","tag-finance","tag-nvidia","tag-nvidia-geforce-gtx-660","tag-partial-differential-equations","tag-pdes"],"views":3341,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10577","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=10577"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10577\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10577"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10577"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10577"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}