{"id":18375,"date":"2018-07-15T13:09:18","date_gmt":"2018-07-15T10:09:18","guid":{"rendered":"https:\/\/hgpu.org\/?p=18375"},"modified":"2018-07-15T13:09:18","modified_gmt":"2018-07-15T10:09:18","slug":"cloudcl-single-paradigm-distributed-heterogeneous-computing-for-cloud-infrastructures","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=18375","title":{"rendered":"CloudCL: Single-Paradigm Distributed Heterogeneous Computing for Cloud Infrastructures"},"content":{"rendered":"<p>The ever-growing demand for compute resources has reached a wide range of application domains, and with that has created a larger audience for compute-intensive tasks. In this paper, we present the CloudCL framework, which empowers users to run compute-intensive tasks without having to face the total cost of ownership of operating an extensive high-performance compute infrastructure. CloudCL enables developers to tap the ubiquitous availability of cloudbased heterogeneous resources using a single-paradigm compute framework, without having to consider dynamic resource management and inter-node communication. In an extensive performance evaluation, we demonstrate the feasibility of the framework, yielding close-to-linear scale-out capabilities for certain workloads.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The ever-growing demand for compute resources has reached a wide range of application domains, and with that has created a larger audience for compute-intensive tasks. In this paper, we present the CloudCL framework, which empowers users to run compute-intensive tasks without having to face the total cost of ownership of operating an extensive high-performance compute [&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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,90,3],"tags":[750,1782,946,242,20,1732,1793,176],"class_list":["post-18375","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-cloud","tag-computer-science","tag-java","tag-mpi","tag-nvidia","tag-nvidia-grid-k520","tag-opencl","tag-package"],"views":2650,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/18375","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=18375"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/18375\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=18375"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=18375"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=18375"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}