Paper Info
Title
Data parallel acceleration of decision support queries using Cell/BE and GPUs
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 21x compared to a single processor system.
Year
DOI
Venue
2009
10.1145/1531743.1531763
Conf. Computing Frontiers
Keywords
Field
DocType
single processor system,standard dss benchmark tpc-h,decision support,data parallel acceleration,different gpu model,time-consuming database workloads,low-cost processor,original database,different architecture,dual quad-core xeon processor,dss query,dss query execution,decision support system
Data set,Computer science,Parallel computing,Decision support system,DirectX,Multiprocessing,Acceleration,Xeon,Speedup
Conference
Citations 
PageRank 
References 
10
0.68
22
Authors
3
Name
Order
Citations
PageRank
Pedro Trancoso137743.79
Despo Othonos2100.68
Artemakis Artemiou3121.78