{"id":9542,"date":"2013-06-07T23:43:38","date_gmt":"2013-06-07T20:43:38","guid":{"rendered":"http:\/\/hgpu.org\/?p=9542"},"modified":"2013-06-07T23:43:38","modified_gmt":"2013-06-07T20:43:38","slug":"scientific-computing-on-hybrid-architectures","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=9542","title":{"rendered":"Scientific Computing on Hybrid Architectures"},"content":{"rendered":"<p>Modern computer architectures, with multicore CPUs and GPUs or other accelerators, make stronger demands than ever on writers of scientific code. Normally, the most efficient program has to be written &#8211; using a substantial effort &#8211; by expert programmers for a certain application on a particular computer. This thesis deals with several algorithmic and technical approaches towards effectively satisfying the demand for high performance parallel scientific applications on hybrid computer architectures without incurring such a high cost in expert programmer time. Efficient programming is accomplished by writing performanceportable code where performance-critical functionality is provided either by an optimized library or by adaptively selecting which computational tasks that are executed on the CPU and the accelerator.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Modern computer architectures, with multicore CPUs and GPUs or other accelerators, make stronger demands than ever on writers of scientific code. Normally, the most efficient program has to be written &#8211; using a substantial effort &#8211; by expert programmers for a certain application on a particular computer. This thesis deals with several algorithmic and technical [&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":[11,89,3],"tags":[1782,14,555,20,253,931,390],"class_list":["post-9542","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-hybrid-computing","tag-nvidia","tag-nvidia-geforce-gtx-260","tag-tesla-m2050","tag-thesis"],"views":1905,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9542","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=9542"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9542\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9542"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9542"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9542"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}