dc.contributor.author | Trancoso, Pedro | en |
dc.contributor.author | Othonos, D. | en |
dc.contributor.author | Artemiou, A. | en |
dc.creator | Trancoso, Pedro | en |
dc.creator | Othonos, D. | en |
dc.creator | Artemiou, A. | en |
dc.date.accessioned | 2019-11-13T10:42:30Z | |
dc.date.available | 2019-11-13T10:42:30Z | |
dc.date.issued | 2009 | |
dc.identifier.isbn | 978-1-60558-413-3 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/55081 | |
dc.description.abstract | Decision Support System (DSS) workloads are known to be one of the most time-consuming database workloads that processes large data sets. Traditionally, DSS queries have been accelerated using large-scale multiprocessor. The topic addressed in this work is to analyze the benefits of using high-performance/low- cost processors such as the GPUs and the Cell/BE to accelerate DSS query execution. In order to overcome the programming effort of developing code for different architectures, in this work we explore the use of a platform, Rapidmind, which offers the possibility of executing the same program on both Cell/BE and GPUs. To achieve this goal we propose data-parallel versions of the original database scan and join algorithms. In our experimental results we compare the execution of three queries from the standard DSS benchmark TPC-H on two systems with two different GPU models, a system with the Cell/BE processor, and a system with dual quad-core Xeon processors. The results show that parallelism can be well exploited by the GPUs. The speedup values observed were up to 21× compared to a single processor system. Copyright 2009 ACM. | en |
dc.source | Proceedings of the 6th ACM Conference on Computing Frontiers, CF 2009 | en |
dc.source | 6th ACM Conference on Computing Frontiers, CF 2009 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885742164&doi=10.1145%2f1531743.1531763&partnerID=40&md5=6ec2cc8630d6df870c6c25dde27a5ae1 | |
dc.subject | Decision support systems | en |
dc.subject | Artificial intelligence | en |
dc.subject | Parallel processing systems | en |
dc.subject | Multicore programming | en |
dc.subject | Program processors | en |
dc.subject | Performance Evaluation | en |
dc.subject | GPU | en |
dc.subject | Data parallel | en |
dc.subject | Cell/BE | en |
dc.subject | Data-parallel model | en |
dc.subject | Decision Support System | en |
dc.subject | Rapidmind | en |
dc.title | Data parallel acceleration of decision support queries using cell/BE and GPUs | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.identifier.doi | 10.1145/1531743.1531763 | |
dc.description.startingpage | 117 | |
dc.description.endingpage | 126 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Conference Object | en |
dc.description.notes | <p>Sponsors: ACM SIGMicro | en |
dc.description.notes | Conference code: 100066 | en |
dc.description.notes | Cited By :12</p> | en |
dc.contributor.orcid | Trancoso, Pedro [0000-0002-2776-9253] | |
dc.gnosis.orcid | 0000-0002-2776-9253 | |