{"id":9521,"date":"2013-06-04T23:56:52","date_gmt":"2013-06-04T20:56:52","guid":{"rendered":"http:\/\/hgpu.org\/?p=9521"},"modified":"2013-06-04T23:56:52","modified_gmt":"2013-06-04T20:56:52","slug":"using-renderscript-and-rcuda-for-compute-intensive-tasks-on-mobile-devices-a-case-study","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=9521","title":{"rendered":"Using RenderScript and RCUDA for Compute Intensive tasks on Mobile Devices: a Case Study"},"content":{"rendered":"<p>The processing power of mobile devices is continuously increasing. In this paper we perform a case study in which we assess three different programming models that can be used to leverage this processing power for compute intensive tasks. We use an imaging algorithm and compare a reference implementation of this algorithm based on OpenCV with a multi threaded RenderScript implementation and an implementation based on computation offloading with Remote CUDA. Experiments show that on a modern Tegra 3 quad core device a multi threaded implementation can achieve a 2.2 speed up factor at the same energy cost, whereas computation offloading does neither lead to speed ups nor energy savings.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The processing power of mobile devices is continuously increasing. In this paper we perform a case study in which we assess three different programming models that can be used to leverage this processing power for compute intensive tasks. We use an imaging algorithm and compare a reference implementation of this algorithm based on OpenCV with [&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":[11,73,89,3],"tags":[1782,1791,14,20,1237],"class_list":["post-9521","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-computer-vision","category-nvidia-cuda","category-paper","tag-computer-science","tag-computer-vision","tag-cuda","tag-nvidia","tag-nvidia-tegra"],"views":2835,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9521","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=9521"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9521\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9521"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9521"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9521"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}