In this paper, we present the design and implementation of an Open Computing Language (OpenCL) framework that targets heterogeneous accelerator multicore architectures with local memory. The architecture consists of a general-purpose processor core and multiple accelerator cores that typically do not have any cache. Each accelerator core, instead, has a small internal local memory. Our OpenCL runtime is based on software-managed caches and coherence protocols that guarantee OpenCL memory consistency to overcome the limited size of the local memory. To boost performance, the runtime relies on three source-code transformation techniques, work-item coalescing, web-based variable expansion and preload-poststore buffering, performed by our OpenCL C source-to-source translator. Work-item coalescing is a procedure to serialize multiple SPMD-like tasks that execute concurrently in the presence of barriers and to sequentially run them on a single accelerator core. It requires the web-based variable expansion technique to allocate local memory for private variables. Preload-poststore buffering is a buffering technique that eliminates the overhead of software cache accesses. Together with work-item coalescing, it has a synergistic effect on boosting performance. We show the effectiveness of our OpenCL framework, evaluating its performance with a system that consists of two Cell BE processors. The experimental result shows that our approach is promising.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we present the design and implementation of an Open Computing Language (OpenCL) framework that targets heterogeneous accelerator multicore architectures with local memory. The architecture consists of a general-purpose processor core and multiple accelerator cores that typically do not have any cache. Each accelerator core, instead, has a small internal local memory. Our […]<\/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":[1782,273,1793],"class_list":["post-2508","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-computer-science","tag-memory-model","tag-opencl"],"views":2424,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2508","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=2508"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2508\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2508"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2508"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2508"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}