Heterogeneous systems are becoming more common on High Performance Computing (HPC) systems. Even using tools like CUDA [1] and OpenCL [2] it is a non-trivial task to obtain optimal performance on the GPU. Approaches to simplifying this task include Merge [3] (a library based framework for heterogeneous multi-core systems), Zippy [4] (a framework for parallel execution of codes on multiple GPU’s), BSGP [5] (a new programming language for general purpose computation on the GPU) and CUDA-lite [6] (an enhancement to CUDA that transforms code based on annotations). In addition, efforts are underway to improve compiler tools for automatic parallelization and optimization of affine loop nests for GPU’s [7] and for automatic translation of OpenMP parallelized codes to CUDA [8]. In this paper we present an alternative approach: a new computational framework for the development of massively data parallel scientific codes applications suitable for use on such petascale\/exascale hybrid systems built upon the highly scalable Cactus framework [9,10] As the first non-trivial demonstration of its usefulness, we successfully developed a new 3D CFD code that achieves improved performance.<\/p>\n","protected":false},"excerpt":{"rendered":"
Heterogeneous systems are becoming more common on High Performance Computing (HPC) systems. Even using tools like CUDA [1] and OpenCL [2] it is a non-trivial task to obtain optimal performance on the GPU. Approaches to simplifying this task include Merge [3] (a library based framework for heterogeneous multi-core systems), Zippy [4] (a framework for parallel […]<\/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":[89,104,90,3],"tags":[14,1795,452,20,1793,298,176,70,244],"class_list":["post-5782","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-fluid-dynamics","category-opencl","category-paper","tag-cuda","tag-fluid-dynamics","tag-heterogeneous-systems","tag-nvidia","tag-opencl","tag-optimization","tag-package","tag-programming-techniques","tag-tesla-s1070"],"views":2426,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5782","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=5782"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5782\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5782"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5782"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5782"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}