Scientific and engineering problems are frequently governed by partial differential equations; however, the analytical solutions of these equations are often impractical, thereby forcing the adoption of numerical methods. Basic Linear Algebra Subprograms (BLAS) operations constitute a fundamental component of these numerical approaches, incorporating essential tasks such as Level 1 operations (dot products and vector addition), Level 2 operations (matrix-vector multiplication), and Level 3 operations (matrix-matrix multiplication). Graphics Processing Units (GPUs), particularly those produced by NVIDIA, have gained significant computational power and are extensively employed to tackle a variety of numerical challenges. Nevertheless, substantial obstacles remain in targeting diverse GPU architectures, particularly concerning portability, the reduction of workarounds, and the enhancement of performance. This study utilizes directive-based programming languages, such as OpenACC, to effectively exploit GPU capabilities. We undertake a comprehensive comparative study and performance evaluation of the OpenACC programming model in comparison to CUDA in executing essential BLAS routines.<\/p>\n","protected":false},"excerpt":{"rendered":"
Scientific and engineering problems are frequently governed by partial differential equations; however, the analytical solutions of these equations are often impractical, thereby forcing the adoption of numerical methods. Basic Linear Algebra Subprograms (BLAS) operations constitute a fundamental component of these numerical approaches, incorporating essential tasks such as Level 1 operations (dot products and vector addition), […]<\/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":[430,1782,14,810,1682,37,324,20,2066,1321,176,550,551,67],"class_list":["post-29611","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-blas","tag-computer-science","tag-cuda","tag-differential-equations","tag-hpc","tag-linear-algebra","tag-matrix-multiplication","tag-nvidia","tag-nvidia-a100","tag-openacc","tag-package","tag-partial-differential-equations","tag-pdes","tag-performance"],"views":1605,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29611","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=29611"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29611\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=29611"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=29611"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=29611"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}