In the big data era, in-memory data analytics is an effective means of achieving high performance data processing and realizing the value of data in a timely manner. Efforts in this direction have been spent on various aspects, including in-memory algorithmic designs and system optimizations. In this paper, we propose to develop the next-generation in-memory relational database processing techniques on coupled CPU-GPU architectures. Particularly, we demonstrate novel design and implementations of query processing paradigms to utilize the strengths of coupled CPU-GPU architectures such as shared main memory and cache hierarchy. We propose a fine-grained method to distribute workload onto available processors, since the CPU and the GPU share the same main memory space. Besides, we propose an in-cache paradigm for query processing to take advantage of shared cache hierarchy to overcome memory stalls of query processing. Our experimental results demonstrate that 1) the proposed fine-grained and in-cache query processing significantly improve the performance of in-memory databases, and 2) such coupled architectures are more energy efficient in query processing compared with other discrete systems.<\/p>\n","protected":false},"excerpt":{"rendered":"
In the big data era, in-memory data analytics is an effective means of achieving high performance data processing and realizing the value of data in a timely manner. Efforts in this direction have been spent on various aspects, including in-memory algorithmic designs and system optimizations. In this paper, we propose to develop the next-generation in-memory […]<\/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":[36,11,90,3],"tags":[1787,1782,667,1793],"class_list":["post-13412","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-opencl","category-paper","tag-algorithms","tag-computer-science","tag-databases","tag-opencl"],"views":2227,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13412","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=13412"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13412\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13412"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13412"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13412"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}