{"id":8564,"date":"2012-11-26T23:54:21","date_gmt":"2012-11-26T21:54:21","guid":{"rendered":"http:\/\/hgpu.org\/?p=8564"},"modified":"2012-11-26T23:54:21","modified_gmt":"2012-11-26T21:54:21","slug":"a-compiler-toolkit-for-array-based-languages-targeting-cpugpu-hybrid-systems","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8564","title":{"rendered":"A compiler toolkit for array-based languages targeting CPU\/GPU hybrid systems"},"content":{"rendered":"<p>This paper presents a compiler toolkit that addresses two important emerging challenges: (1) effectively compiling dynamic array-based languages such as MATLAB, Python and R; and (2) effectively utilizing a wide range of rapidly evolving hybrid CPU\/GPU architectures. The toolkit provides: a high-level IR specifically designed to express a wide range of arraybased computations and indexing modes; Velociraptor, a CPU\/GPU code generator and runtime library; and RaijinCL, a portable autotuning GPU library for key BLAS routines. A compiler developer uses the toolkit by generating VelociraptorIR for key parts of an input program, and using Velociraptor to automatically generate CPU\/GPU code. The toolkit leverages OpenCL and LLVM for GPU and CPU code generation respectively, and can thus be used for a wide variety of target architectures. To demonstrate different possible uses of the toolkit, the paper presents a proof-of-concept CPU\/GPU Python compiler, and a GPU extension of a MATLAB JIT.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a compiler toolkit that addresses two important emerging challenges: (1) effectively compiling dynamic array-based languages such as MATLAB, Python and R; and (2) effectively utilizing a wide range of rapidly evolving hybrid CPU\/GPU architectures. The toolkit provides: a high-level IR specifically designed to express a wide range of arraybased computations and indexing [&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":[11,90,3],"tags":[7,642,1307,215,955,1782,1793,176,513,378],"class_list":["post-8564","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-ati","tag-ati-radeon-hd-5850","tag-ati-radeon-hd-7970","tag-code-generation","tag-compilers","tag-computer-science","tag-opencl","tag-package","tag-python","tag-tesla-c2050"],"views":3639,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8564","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=8564"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8564\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8564"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8564"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8564"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}