GPUs offer high-performance floating-point computation at commodity prices, but their usage is hindered by programming models which expose the user to irregularities in the current shared-memory environments and require learning new interfaces and semantics. This thesis will demonstrate that the message-passing paradigm can be conceptually cleaner than the current data-parallel models for programming GPUs because it can hide the quirks of current GPU shared-memory environments, as well as GPU-specific features, behind a well-established and well-understood interface. This will be shown by demonstrating a proof-of-concept MPI implementation which provides cleaner, simpler code with a reasonable performance cost. This thesis will also demonstrate that, although there is a virtualization constraint imposed by MPI, this constraint is harmless as long as the virtualization was already chosen to be optimal in terms of a strong execution model and nearly-optimal execution time. This will be demonstrated by examining execution times with varying virtualization using a computationally-expensive micro-kernel.<\/p>\n","protected":false},"excerpt":{"rendered":"
GPUs offer high-performance floating-point computation at commodity prices, but their usage is hindered by programming models which expose the user to irregularities in the current shared-memory environments and require learning new interfaces and semantics. This thesis will demonstrate that the message-passing paradigm can be conceptually cleaner than the current data-parallel models for programming GPUs because […]<\/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":[1782,14,263,242,20,357,390,167],"class_list":["post-1219","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-data-parallelism","tag-mpi","tag-nvidia","tag-nvidia-geforce-8800-gts","tag-thesis","tag-virtualization"],"views":2042,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1219","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=1219"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1219\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1219"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1219"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1219"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}