{"id":15437,"date":"2016-02-08T23:09:37","date_gmt":"2016-02-08T21:09:37","guid":{"rendered":"http:\/\/hgpu.org\/?p=15437"},"modified":"2016-02-08T23:09:37","modified_gmt":"2016-02-08T21:09:37","slug":"integrating-gpgpu-computations-with-cpu-coroutines-in-c","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=15437","title":{"rendered":"Integrating GPGPU computations with CPU coroutines in C++"},"content":{"rendered":"<p>We present results on integration of two major GPGPU APIs with reactor-based event processing model in C++ that utilizes coroutines. With current lack of universally usable GPGPU programming interface that gives optimal performance and debates about the style of implementing asynchronous computing in C++, we present a working implementation that allows a uniform and seamless approach to writing C++ code with continuations that allow processing on CPUs or CUDA\/OpenCL accelerators. Performance results are provided that show, if corner cases are avoided, this approach has negligible performance cost on latency.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present results on integration of two major GPGPU APIs with reactor-based event processing model in C++ that utilizes coroutines. With current lack of universally usable GPGPU programming interface that gives optimal performance and debates about the style of implementing asynchronous computing in C++, we present a working implementation that allows a uniform and seamless [&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":[89,90,3,12],"tags":[1855,7,1854,14,20,1015,974,1793,1783],"class_list":["post-15437","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-opencl","category-paper","category-physics","tag-amd-radeon-r7-265","tag-ati","tag-ati-radeon-hd-7570","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-460","tag-nvidia-geforce-gtx-580","tag-opencl","tag-physics"],"views":3137,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15437","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=15437"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15437\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=15437"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=15437"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=15437"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}