{"id":3194,"date":"2011-03-13T10:53:09","date_gmt":"2011-03-13T10:53:09","guid":{"rendered":"http:\/\/hgpu.org\/?p=3194"},"modified":"2011-03-13T10:53:09","modified_gmt":"2011-03-13T10:53:09","slug":"obsidian-gpu-programming-in-haskell","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3194","title":{"rendered":"Obsidian: GPU Programming in Haskell"},"content":{"rendered":"<p>Obsidian is a language for data-parallel programming embedded in Haskell. As the Obsidian programs are run, C code is generated. This C code can be compiled for an NVIDIA 8800 series GPU (Graphics Processing Unit), or for other high-end NVIDIA GPUs. The idea is that the style of programming used in Lava for structural hardware design [2] can be applied to data-parallel programming as well. Therefore Obsidian programmers use combinators that have much in common with those used in Lava. However, where Lava generates the netlist for a fixed-size circuit, Obsidian can generate GPU programs that are parametric in input size.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Obsidian is a language for data-parallel programming embedded in Haskell. As the Obsidian programs are run, C code is generated. This C code can be compiled for an NVIDIA 8800 series GPU (Graphics Processing Unit), or for other high-end NVIDIA GPUs. The idea is that the style of programming used in Lava for structural hardware [&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":[215,955,1782,14,20,357,176],"class_list":["post-3194","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-code-generation","tag-compilers","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-8800-gts","tag-package"],"views":3130,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3194","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=3194"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3194\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3194"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3194"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3194"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}