{"id":3482,"date":"2011-04-06T20:20:23","date_gmt":"2011-04-06T20:20:23","guid":{"rendered":"http:\/\/hgpu.org\/?p=3482"},"modified":"2011-04-08T12:56:07","modified_gmt":"2011-04-08T12:56:07","slug":"an-algorithmic-incremental-and-iterative-development-method-to-parallelize-dusty-deck-fortran-hpc-codes-in-gpgpus-using-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3482","title":{"rendered":"An algorithmic incremental and iterative development method to parallelize dusty-deck FORTRAN HPC codes in GPGPUs using CUDA"},"content":{"rendered":"<p>State-of-the-art high-speed and economical graphic card processors (GPUs) provide high multiprocessing power for high performance computing (HPC). But software development for high performance computing is profound and requires a good comprehension of algorithms, applications, and architectures. This paper outlines an incremental and iterative software development process for porting dusty-deck HPC application source codes to a selected GPU-enabled architecture. A new type of dependency, namely recurrent transmission dependency, is introduced. Some experiments are reported.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>State-of-the-art high-speed and economical graphic card processors (GPUs) provide high multiprocessing power for high performance computing (HPC). But software development for high performance computing is profound and requires a good comprehension of algorithms, applications, and architectures. This paper outlines an incremental and iterative software development process for porting dusty-deck HPC application source codes to a [&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":[11,89,3],"tags":[1782,14,989,95,20],"class_list":["post-3482","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-fortran","tag-high-level-languages","tag-nvidia"],"views":1783,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3482","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=3482"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3482\/revisions"}],"predecessor-version":[{"id":3518,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3482\/revisions\/3518"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3482"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3482"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3482"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}