{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T07:13:04Z","timestamp":1784099584459,"version":"3.55.0"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2013,8,28]]},"abstract":"<jats:p>GPU acceleration is a promising approach to speed up query processing of database systems by using low cost graphic processors as coprocessors. Two major trends have emerged in this area: (1) The development of frameworks for scheduling tasks in heterogeneous CPU\/GPU platforms, which is mainly in the context of coprocessing for applications and does not consider specifics of database-query processing and optimization. (2) The acceleration of database operations using efficient GPU algorithms, which typically cannot be applied easily on other database systems, because of their analytical-algorithm-specific cost models. One major challenge is how to combine traditional database query processing with GPU coprocessing techniques and efficient database operation scheduling in a GPU-aware query optimizer. In this thesis, we develop a hybrid query processing engine, which extends the traditional physical optimization process to generate hybrid query plans and to perform a cost-based optimization in a way that the advantages of CPUs and GPUs are combined. Furthermore, we aim at a portable solution between different GPU-accelerated database management systems to maximize applicability. Preliminary results indicate great potential.<\/jats:p>","DOI":"10.14778\/2536274.2536325","type":"journal-article","created":{"date-parts":[[2014,6,24]],"date-time":"2014-06-24T12:17:57Z","timestamp":1403612277000},"page":"1398-1403","source":"Crossref","is-referenced-by-count":39,"title":["Why it is time for a HyPE"],"prefix":"10.14778","volume":"6","author":[{"given":"Sebastian","family":"Bre\u00df","sequence":"first","affiliation":[{"name":"University of Magdeburg"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gunter","family":"Saake","sequence":"additional","affiliation":[{"name":"University of Magdeburg"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2013,8]]},"reference":[{"issue":"2","key":"e_1_2_1_1_1","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1002\/cpe.1631","article-title":"A Unified Platform for Task Scheduling on Heterogeneous Multicore Architectures","volume":"23","author":"Augonnet C.","year":"2011","journal-title":"Concurrency and Computation: Practice & Experience"},{"key":"e_1_2_1_2_1","volume-title":"Efficient Data Management for GPU Databases","author":"Bakkum P.","year":"2012"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1145\/1735688.1735706","volume-title":"GPGPU","author":"Bakkum P.","year":"2010"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1145\/2236584.2236593","volume-title":"DaMoN","author":"Beier F.","year":"2012"},{"issue":"8","key":"e_1_2_1_5_1","first-page":"1084","volume":"38","author":"Bre\u00df S.","year":"2013","journal-title":"Efficient Co-Processor Utilization in Database Query Processing. 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