The Intel DPC++ Compatibility Tool is a component of the Intel oneAPI Base Toolkit. This tool automatically transforms CUDA code into Data Parallel C++ (DPC++), thus assisting in the migration process. DPC++ is an implementation of the programming standard for heterogeneous computing known as SYCL, which unifies the development of parallel applications on CPUs, GPUs or even FPGAs. This paper analyzes the DPC++ Compatibility Tool by considering the manual intervention required and the problems encountered while migrating the Rodinia benchmarks. For this suite, this tool achieves an impressive rate of almost 87% for code successfully migrated. Moreover, a comparative study of the performance obtained by the migrated code was carried out, showing a moderate overhead in most of the migrated examples. Finally, a performance comparison on different devices was also performed.<\/p>\n","protected":false},"excerpt":{"rendered":"
The Intel DPC++ Compatibility Tool is a component of the Intel oneAPI Base Toolkit. This tool automatically transforms CUDA code into Data Parallel C++ (DPC++), thus assisting in the migration process. DPC++ is an implementation of the programming standard for heterogeneous computing known as SYCL, which unifies the development of parallel applications on CPUs, GPUs […]<\/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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3],"tags":[1782,14,377,452,20,176,67,1845],"class_list":["post-26977","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-fpga","tag-heterogeneous-systems","tag-nvidia","tag-package","tag-performance","tag-sycl"],"views":1442,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/26977","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=26977"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/26977\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=26977"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=26977"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=26977"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}