The common approach to program many-core processor is to write processor-specific code with low level APIs for different processors, which could achieve good performance but would result in serious portability issues: programmers are required to write a specific version code for target architecture. Therefore, we present ab-Stream, an extensible framework for programming many-threaded processor based on SUIF Intermediate Representation. ab-Stream abstracts many-core many-threaded processor into a unified architecture and ab-Stream program is an OpenMP-like program with different directives for many-core processor. Furthermore, a prototype of ab-Stream was implemented to map ab-Stream programs into many-core GPU. Experiments show that our implementation can execute transformed code correctly and efficiently on CUDA-enabled GPUs. Furthermore, performance of ab-Stream version code produced by our prototype can outperform original GPU code and is close to handoptimized GPU code.<\/p>\n","protected":false},"excerpt":{"rendered":"
The common approach to program many-core processor is to write processor-specific code with low level APIs for different processors, which could achieve good performance but would result in serious portability issues: programmers are required to write a specific version code for target architecture. Therefore, we present ab-Stream, an extensible framework for programming many-threaded processor based […]<\/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":[11,89,3],"tags":[1782,14,20,234,67],"class_list":["post-7917","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-280","tag-performance"],"views":2206,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7917","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=7917"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7917\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7917"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7917"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7917"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}