{"id":5855,"date":"2011-10-10T18:08:23","date_gmt":"2011-10-10T15:08:23","guid":{"rendered":"http:\/\/hgpu.org\/?p=5855"},"modified":"2011-10-10T18:08:23","modified_gmt":"2011-10-10T15:08:23","slug":"streaming-oriented-parallelization-of-domain-independent-irregular-kernels","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5855","title":{"rendered":"Streaming-Oriented Parallelization of Domain-Independent Irregular Kernels"},"content":{"rendered":"<p>Current parallelizing and optimizing compilers use techniques for the recognition of computational kernels to improve the quality of the target code. Domain-independent kernels characterize the computations carried out in an application, independently of the implementation details of a given programming language. This paper presents streaming-oriented parallelizing transformations for irregular assignment and irregular reduction kernels. The advantage of these code transformations is that they enable the parallelization of many algorithms with little effort without a depth knowledge of the particular application. The experimental results show the efficiency on current GPUs, although the main goal of the proposed techniques is not performance, but assist the programmer in the parallelization for a better productivity.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Current parallelizing and optimizing compilers use techniques for the recognition of computational kernels to improve the quality of the target code. Domain-independent kernels characterize the computations carried out in an application, independently of the implementation details of a given programming language. This paper presents streaming-oriented parallelizing transformations for irregular assignment and irregular reduction kernels. The [&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":[36,11,3],"tags":[1787,7,417,218,1782,252,67,119],"class_list":["post-5855","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-paper","tag-algorithms","tag-ati","tag-ati-radeon-hd-4850","tag-brook","tag-computer-science","tag-openmp","tag-performance","tag-presentation"],"views":2335,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5855","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=5855"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5855\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5855"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5855"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5855"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}