Current programming models and compiler technologies for multi-core processors do not exploit well the performance benefits obtainable by applying algorithm-specific, i.e., semantic-specific optimizations to a particular application. In this work, we propose a pattern-making methodology that allows algorithm-specific optimizations to be encapsulated into "optimization patterns" that are expressed in terms of pre-processor directives so that simple annotations can result in significant performance improvements. To validate this new methodology, a framework, named EPOD, is developed to map such directives to the underlying optimization schemes. We have identified and implemented a number of optimization patterns for three representative computer platforms. Our experimental results show that a pattern-guided compiler can outperform the state-of-the-art compilers and even achieve performance as competitive as hand-tuned code. Thus, such a pattern-making methodology represents an encouraging direction for domain experts’ experience and knowledge to be integrated into general-purpose compilers.<\/p>\n","protected":false},"excerpt":{"rendered":"
Current programming models and compiler technologies for multi-core processors do not exploit well the performance benefits obtainable by applying algorithm-specific, i.e., semantic-specific optimizations to a particular application. In this work, we propose a pattern-making methodology that allows algorithm-specific optimizations to be encapsulated into "optimization patterns" that are expressed in terms of pre-processor directives so that […]<\/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":[36,11,89,3],"tags":[1787,215,1782,14,20,251,298],"class_list":["post-6618","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-code-generation","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-285","tag-optimization"],"views":1736,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6618","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=6618"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6618\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6618"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6618"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6618"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}