{"id":10180,"date":"2013-08-03T00:00:15","date_gmt":"2013-08-02T21:00:15","guid":{"rendered":"http:\/\/hgpu.org\/?p=10180"},"modified":"2013-08-03T00:00:15","modified_gmt":"2013-08-02T21:00:15","slug":"strategies-for-optimization-of-parallel-programs","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10180","title":{"rendered":"Strategies for Optimization of Parallel Programs"},"content":{"rendered":"<p>Multi-core processors are present in most forms of computing, from a pocket-size smartphone to supercomputers. Consequently, parallel and concurrent programming has reemerged as a pressing concern for everyone interested in exploring all the potential computational power in these machines. Writing parallel, and specially concurrent, programs is not a trivial task as it requires a different reasoning model about the program. Moreover, most of the existing computer is sequential, and does not take advantage of the underlying parallelism of multicore CPUs. The proposed work intends to improve and further advance techniques that automatically parallelize a program. The result of this work should provide better ways of generating parallel programs that are faster and correct.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Multi-core processors are present in most forms of computing, from a pocket-size smartphone to supercomputers. Consequently, parallel and concurrent programming has reemerged as a pressing concern for everyone interested in exploring all the potential computational power in these machines. Writing parallel, and specially concurrent, programs is not a trivial task as it requires a different [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":[11,89,90,3],"tags":[1782,14,20,251,1793,298,390],"class_list":["post-10180","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-opencl","category-paper","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-285","tag-opencl","tag-optimization","tag-thesis"],"views":2307,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10180","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=10180"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10180\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10180"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10180"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10180"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}