In recent years, Intel introduced oneAPI as a unified and cross-architecture programming model based on the Data Parallel C++ (DPC++) language, which in turn, is based on the C++ and SYCL standard languages. In order to facilitate the migration of legacy CUDA code originally written for NVIDIA GPUs, developers can employ the Intel DPC++ Compatibility Tool, which aims to automatically migrate code from CUDA to SYCL. While this tool-assisted code migration is a good starting point for leveraging the Intel oneAPI ecosystem, manual steps for code completion and tuning are still required. In this paper, we present our experiences migrating AutoDock-GPU, a widely-used molecular docking application, from CUDA to SYCL. Our discussion focuses on: (1) the use of this automated source-code migration tool, (2) the required manual code refinement for functionality and optimization, and (3) the comparison of the performance achieved in this manner on multi-core CPUs as well as on high-end GPUs, such as NVIDIA A100 and the recently-launched Intel Data Center Max 1550 device.<\/p>\n","protected":false},"excerpt":{"rendered":"
In recent years, Intel introduced oneAPI as a unified and cross-architecture programming model based on the Data Parallel C++ (DPC++) language, which in turn, is based on the C++ and SYCL standard languages. In order to facilitate the migration of legacy CUDA code originally written for NVIDIA GPUs, developers can employ the Intel DPC++ Compatibility […]<\/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":[66,11,89,3],"tags":[1790,1782,14,1588,20,2066,2118,176,1845],"class_list":["post-28303","post","type-post","status-publish","format-standard","hentry","category-chemistry","category-computer-science","category-nvidia-cuda","category-paper","tag-chemistry","tag-computer-science","tag-cuda","tag-molecular-docking","tag-nvidia","tag-nvidia-a100","tag-oneapi","tag-package","tag-sycl"],"views":2349,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/28303","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=28303"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/28303\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=28303"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=28303"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=28303"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}