As the number of transistors that are integrated onto a silicon die continues to increase, the compute power is becoming a commodity. This has enabled a whole host of new applications that rely on high-throughput computations. Recently, the need for faster and cost-effective applications in form-factor constrained environments has driven an interest in on-chip acceleration of algorithms based on Monte Carlo simulations. Though Field Programmable Gate Arrays (FPGAs), with hundreds of on-chip arithmetic units, show significant promise for accelerating these embarrassingly parallel simulations, a challenge exists in sharing access to simulation data amongst many concurrent experiments. This thesis presents a compute architecture for accelerating Monte Carlo simulations based on the Network-on-Chip (NoC) paradigm for on-chip communication. We demonstrate through the complete implementation of a Monte Carlo-based image reconstruction algorithm for Single-Photon Emission Computed Tomography (SPECT) imaging that this complex problem can be accelerated by two orders of magnitude on even a modestly-sized FPGA over a 2GHz Intel Core 2 Duo Processor. Futhermore, we have created a framework for further increasing parallelism by scaling our architecture across multiple compute devices and by extending our original design to a multi-FPGA system nearly linear increase in acceleration with logic resources was achieved.<\/p>\n","protected":false},"excerpt":{"rendered":"
As the number of transistors that are integrated onto a silicon die continues to increase, the compute power is becoming a commodity. This has enabled a whole host of new applications that rely on high-throughput computations. Recently, the need for faster and cost-effective applications in form-factor constrained environments has driven an interest in on-chip acceleration […]<\/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":[36,89,38,3],"tags":[1787,479,478,14,377,512,1788,72,20,953,390,567],"class_list":["post-6812","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-medicine","category-paper","tag-algorithms","tag-computed-tomography","tag-ct","tag-cuda","tag-fpga","tag-image-reconstruction","tag-medicine","tag-monte-carlo-simulation","tag-nvidia","tag-nvidia-geforce-gtx-470","tag-thesis","tag-tomography"],"views":2712,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6812","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=6812"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6812\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6812"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6812"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6812"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}