{"id":9947,"date":"2013-07-12T23:26:55","date_gmt":"2013-07-12T20:26:55","guid":{"rendered":"http:\/\/hgpu.org\/?p=9947"},"modified":"2013-07-12T23:34:20","modified_gmt":"2013-07-12T20:34:20","slug":"parallel-graph-processing-on-graphics-processors-made-easy","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=9947","title":{"rendered":"Parallel Graph Processing on Graphics Processors Made Easy"},"content":{"rendered":"<p>This paper demonstrates Medusa, a programming framework for parallel graph processing on graphics processors (GPUs). Medusa enables developers to leverage the massive parallelism and other hardware features of GPUs by writing sequential C\/C++ code for a small set of APIs. This simplifies the implementation of parallel graph processing on the GPU. The runtime system of Medusa automatically executes the user-defined APIs in parallel on the GPU, with a series of graph-centric optimizations based on the architecture features of GPUs. We will demonstrate the steps of developing GPU-based graph processing algorithms with Medusa, and the superior performance of Medusa with both real-world and synthetic datasets.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper demonstrates Medusa, a programming framework for parallel graph processing on graphics processors (GPUs). Medusa enables developers to leverage the massive parallelism and other hardware features of GPUs by writing sequential C\/C++ code for a small set of APIs. This simplifies the implementation of parallel graph processing on the GPU. The runtime system of [&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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,89,3],"tags":[1787,1782,14,158,20,176,378],"class_list":["post-9947","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-graph-theory","tag-nvidia","tag-package","tag-tesla-c2050"],"views":2342,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9947","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=9947"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9947\/revisions"}],"predecessor-version":[{"id":9948,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9947\/revisions\/9948"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9947"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9947"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9947"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}