It is possible to purchase, for as little as $10,000, a cluster of computers with the capability to rival the supercomputers of only a few years ago. Now, users that have little to no experience developing distributed applications or managing a cluster are in a position to do so. To allow domain scientists to effectively utilize these resources, Distributed OpenCL (DOCL) was developed. DOCL is an easy-to-use foundation for peer-to-peer distributed computation on small to medium clusters. It is assumed that the end-user is a domain scientist, familiar with model development in environments such as Matlab, though inexperienced with distributed computation or parallel programming. The scope of this work includes the definition of a peer-to-peer protocol for discovering and establishing relationships with every node within a multicast domain, using the concepts of Zero-Configuration Networking, multicast DNS, and DNS Service Discovery. A problematic edge case of multicast DNS is detailed along with a mitigation technique. An XML schema is also described for basic peer communication and cluster management and inventory. A system for scheduling algorithm tasks on the cluster of heterogeneous compute devices was developed, including an automatic computation and communication cost measurement system. Finally, a graphical programming language was designed and implemented that allows non-expert programmers and modelers to develop new applications in a straightforward, accessible way.<\/p>\n","protected":false},"excerpt":{"rendered":"
It is possible to purchase, for as little as $10,000, a cluster of computers with the capability to rival the supercomputers of only a few years ago. Now, users that have little to no experience developing distributed applications or managing a cluster are in a position to do so. To allow domain scientists to effectively […]<\/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":[7,255,1782,510,452,20,251,1793],"class_list":["post-7920","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-ati","tag-ati-radeon-hd-4870","tag-computer-science","tag-distributed-computing","tag-heterogeneous-systems","tag-nvidia","tag-nvidia-geforce-gtx-285","tag-opencl"],"views":3663,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7920","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=7920"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7920\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7920"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7920"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7920"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}