{"id":4869,"date":"2011-07-23T22:11:37","date_gmt":"2011-07-23T19:11:37","guid":{"rendered":"http:\/\/hgpu.org\/?p=4869"},"modified":"2011-07-23T22:11:37","modified_gmt":"2011-07-23T19:11:37","slug":"exploring-graphics-processor-performance-for-general-purpose-applications","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4869","title":{"rendered":"Exploring graphics processor performance for general purpose applications"},"content":{"rendered":"<p>Graphics processors are designed to perform many floating-point operations per second. Consequently, they are an attractive architecture for high-performance computing at a low cost. Nevertheless, it is still not very clear how to exploit all their potential for general-purpose applications. In this work we present a comprehensive study of the performance of an application executing on the GPU. In addition, we analyze the possibility of using the graphics card to extend the life-time of a computer system. In our experiments we compare the execution on a mid-class GPU (NVIDIA GeForce FX 5700LE) with a high-end CPU (Pentium 4 3.2 GHz). The results show that to achieve high speedup with the GPU you need to: (1) format the vectors into two-dimensional arrays; (2) process large data arrays; and (3) perform a considerable amount of operations per data element. Finally, we study the performance when upgrading a low-end system by simply adding a GPU. This solution is cheaper, results in smaller power consumption and achieves higher speedup (8.1x versus 1.3x) than a full upgrade to a new high-end system.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Graphics processors are designed to perform many floating-point operations per second. Consequently, they are an attractive architecture for high-performance computing at a low cost. Nevertheless, it is still not very clear how to exploit all their potential for general-purpose applications. In this work we present a comprehensive study of the performance of an application executing [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","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":[11,3],"tags":[218,1782,20,903,67,31],"class_list":["post-4869","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-brook","tag-computer-science","tag-nvidia","tag-nvidia-geforce-fx-5700","tag-performance","tag-review"],"views":2299,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4869","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=4869"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4869\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4869"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4869"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4869"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}