{"id":18974,"date":"2019-06-30T15:04:37","date_gmt":"2019-06-30T12:04:37","guid":{"rendered":"https:\/\/hgpu.org\/?p=18974"},"modified":"2019-06-30T15:04:37","modified_gmt":"2019-06-30T12:04:37","slug":"automated-generation-of-opencl-programs-based-on-algebra-algorithmic-approach","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=18974","title":{"rendered":"Automated Generation of OpenCL Programs Based on Algebra-Algorithmic Approach"},"content":{"rendered":"<p>The paper proposes the further development of algebra-algorithmic design and synthesis tools towards the development of OpenCL programs. The method for semi-automatic parallelization of cyclic operators is proposed. The particular feature of the approach consists in using high-level algebraalgorithmic program specifications (schemes) and rewriting rules technique. The developed tools provide the construction of parallel algorithm schemes by superposition of predefined language constructs of Glushkov&#8217;s system of algorithmic algebra, which are considered as reusable components. An algorithm scheme is a basis for the generation of corresponding source code in a target programming language. The approach is illustrated with an example of developing an OpenCL interpolation program used in a numerical weather forecasting. The results of the experiment consisting in executing the generated OpenCL program on a graphics processing unit are given.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The paper proposes the further development of algebra-algorithmic design and synthesis tools towards the development of OpenCL programs. The method for semi-automatic parallelization of cyclic operators is proposed. The particular feature of the approach consists in using high-level algebraalgorithmic program specifications (schemes) and rewriting rules technique. The developed tools provide the construction of parallel algorithm [&hellip;]<\/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":[11,89,90,3],"tags":[215,1782,14,452,20,1982,1793],"class_list":["post-18974","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-opencl","category-paper","tag-code-generation","tag-computer-science","tag-cuda","tag-heterogeneous-systems","tag-nvidia","tag-nvidia-geforce-gtx-840-m","tag-opencl"],"views":2838,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/18974","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=18974"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/18974\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=18974"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=18974"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=18974"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}