{"id":12933,"date":"2014-10-14T20:41:10","date_gmt":"2014-10-14T17:41:10","guid":{"rendered":"http:\/\/hgpu.org\/?p=12933"},"modified":"2014-10-14T20:41:10","modified_gmt":"2014-10-14T17:41:10","slug":"a-case-study-of-opencl-on-an-android-mobile-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12933","title":{"rendered":"A Case Study of OpenCL on an Android Mobile GPU"},"content":{"rendered":"<p>An observation in supercomputing in the past decade illustrates the transition of pervasive commodity products being integrated with the world&#8217;s fastest system. Given today&#8217;s exploding popularity of mobile devices, we investigate the possibilities for high performance mobile computing. Because parallel processing on mobile devices will be the key element in developing a mobile and computationally powerful system, this study was designed to assess the computational capability of a GPU on a low-power, ARM-based mobile device. The methodology for executing computationally intensive benchmarks on a handheld mobile GPU is presented, including the practical aspects of working with the existing Android-based software stack and leveraging the OpenCL-based parallel programming model. The empirical results provide the performance of an OpenCL N-body benchmark and an auto-tuning kernel parameterization strategy. The achieved computational performance of the lowpower mobile Adreno GPU is compared with a quad-core ARM, an x86 Intel processor, and a discrete AMD GPU.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>An observation in supercomputing in the past decade illustrates the transition of pervasive commodity products being integrated with the world&#8217;s fastest system. Given today&#8217;s exploding popularity of mobile devices, we investigate the possibilities for high performance mobile computing. Because parallel processing on mobile devices will be the key element in developing a mobile and computationally [&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":[11,90,3],"tags":[1238,7,1200,1782,258,1793],"class_list":["post-12933","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-arm","tag-ati","tag-ati-radeon-hd-6970","tag-computer-science","tag-n-body-simulation","tag-opencl"],"views":5005,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12933","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=12933"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12933\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12933"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12933"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12933"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}