{"id":2052,"date":"2010-12-13T21:46:47","date_gmt":"2010-12-13T21:46:47","guid":{"rendered":"http:\/\/hgpu.org\/?p=2052"},"modified":"2010-12-13T21:46:47","modified_gmt":"2010-12-13T21:46:47","slug":"embracing-heterogeneity-parallel-programming-for-changing-hardware","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2052","title":{"rendered":"Embracing Heterogeneity: Parallel Programming for Changing Hardware"},"content":{"rendered":"<p>Computer systems are undergoing significant change: to improve performance and efficiency, architects are exposing more microarchitectural details directly to programmers. Software that exploits specialized accelerators, such as GPUs, and specialized processor features, such as software-controlled memory, exposes limitations in existing compiler and OS infrastructure. In this paper we propose a pragmatic approach, motivated by our experience with Merge [3], for building applications that will tolerate changing hardware. Our approach allows programmers to leverage different processor-specific or domain-specific toolchains to create software modules specialized for different hardware configurations, and it provides language mechanisms to enable the automatic mapping of the application to these processor-specific modules. We show this approach can be used to manage computing resources in complex heterogeneous processors and to enable aggressive compiler optimizations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Computer systems are undergoing significant change: to improve performance and efficiency, architects are exposing more microarchitectural details directly to programmers. Software that exploits specialized accelerators, such as GPUs, and specialized processor features, such as software-controlled memory, exposes limitations in existing compiler and OS infrastructure. In this paper we propose a pragmatic approach, motivated by our [&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":[11,3],"tags":[865,1782,70],"class_list":["post-2052","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-architecture","tag-computer-science","tag-programming-techniques"],"views":2177,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2052","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=2052"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2052\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2052"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2052"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2052"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}