{"id":2812,"date":"2011-02-11T12:42:14","date_gmt":"2011-02-11T12:42:14","guid":{"rendered":"http:\/\/hgpu.org\/?p=2812"},"modified":"2011-02-11T12:42:14","modified_gmt":"2011-02-11T12:42:14","slug":"compiling-an-array-language-to-a-graphics-processor","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2812","title":{"rendered":"Compiling an Array Language to a Graphics Processor"},"content":{"rendered":"<p>Graphics processors are significantly faster than traditional processors, particularly for numerical code, and in recent years have become flexible enough to permit general-purpose use, rather than just graphics use. NVIDIA&#8217;s CUDA makes general-purpose graphics processor computing feasible, but it still requires significant programmer effort. My thesis is that array programming can be an effective way to program graphics processors, and that a restricted, functionally pure array language coupled with simple optimizations can have performance competitive with handwritten GPU programs. I support this thesis through the research language Barracuda, an array language embedded within Haskell that generates optimized CUDA code. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Graphics processors are significantly faster than traditional processors, particularly for numerical code, and in recent years have become flexible enough to permit general-purpose use, rather than just graphics use. NVIDIA&#8217;s CUDA makes general-purpose graphics processor computing feasible, but it still requires significant programmer effort. My thesis is that array programming can be an effective way [&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":[11,89,3],"tags":[1782,14,95,20,226,390],"class_list":["post-2812","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-high-level-languages","tag-nvidia","tag-nvidia-geforce-8800-gt","tag-thesis"],"views":1799,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2812","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=2812"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2812\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2812"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2812"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2812"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}