This work presents the Open Reconfigurable Computing Language (OpenRCL) system designed to enable low-power high-performance reconfigurable computing with imperative programming language such as C\/C++. The key idea is to expose the FPGA platform as a compiler target for applications expressed in the OpenCL paradigm. To this end, we present a combination of low-level virtual machine instruction set, execution model, many-core architecture, and associated compiler to achieve high performance and power efficiency by exploiting the FPGA’s distributed memories and abundant hardware structures (such as DSP blocks, long carry-chains, and registers). Our resulting OpenRCL system not only allows programmers to easily express parallelism through the API defined in the OpenCL standard but also supports coarse-grain multithreading and dataflow-style fine-grain threading while permitting bit-level resource control. An OpenRCL prototype machine with 30 processing nodes was implemented using a Virtex-5 (XCV5LX155T-2) FPGA. For the well-known Parallel Prefix Sum (Scan) problem, comparing the runtime of the same problem on a GeForce 9400m using the OpenCL SDK from Apple Inc., the OpenRCL machine demonstrates comparable performance with a 5x reduction in core power consumption.<\/p>\n","protected":false},"excerpt":{"rendered":"
This work presents the Open Reconfigurable Computing Language (OpenRCL) system designed to enable low-power high-performance reconfigurable computing with imperative programming language such as C\/C++. The key idea is to expose the FPGA platform as a compiler target for applications expressed in the OpenCL paradigm. To this end, we present a combination of low-level virtual machine […]<\/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":[11,90,3],"tags":[955,1782,377,20,453,1793],"class_list":["post-2669","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-compilers","tag-computer-science","tag-fpga","tag-nvidia","tag-nvidia-geforce-9400-m","tag-opencl"],"views":2375,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2669","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=2669"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2669\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2669"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2669"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2669"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}