In recent years, the need for continuous processing and analysis of data streams has increased rapidly. To achieve high throughput-rates, stream-applications make use of operator-parallelization, batching-strategies and distribution. Another possibility is to utilize co-processors capabilities per operator. Further, the database community noticed, that a column-oriented architecture is essential for efficient co-processing, since the data transfer overhead is smaller compared to transferring whole tables. However, current systems still rely on a row-wise architecture for stream processing, because it requires data structures for high velocity. In contrast, stream portions are in rest while being bound to a window. With this, we are able to alter the per-window event representation from row to column orientation, which will enable us to exploit GPU acceleration. To provide general-purpose GPU capabilities for stream processing, the varying window sizes lead to challenges. Since very large windows cannot be passed directly to the GPU, we propose to split the variable-length windows into fixed-sized window portions. Further, each such portion has a column-oriented event representation. In this paper, we present a time and space efficient, data corruption free concept for this task. Finally, we identify open research challenges related to co-processing in the context of stream processing.<\/p>\n","protected":false},"excerpt":{"rendered":"
In recent years, the need for continuous processing and analysis of data streams has increased rapidly. To achieve high throughput-rates, stream-applications make use of operator-parallelization, batching-strategies and distribution. Another possibility is to utilize co-processors capabilities per operator. Further, the database community noticed, that a column-oriented architecture is essential for efficient co-processing, since the data transfer […]<\/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-14168","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":2221,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14168","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=14168"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14168\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14168"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=14168"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=14168"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}