Data parallel acceleration of decision support queries using Cell/BE and GPUs

Pedro Trancoso, Despo Othonos, Artemakis Artemiou
Department of Computer Science, University of Cyprus, Cyprus
Proceedings of the 6th ACM conference on Computing frontiers, CF ’09


   title={Data parallel acceleration of decision support queries using Cell/BE and GPUs},

   author={Trancoso, P. and Othonos, D. and Artemiou, A.},

   booktitle={Proceedings of the 6th ACM conference on Computing frontiers},





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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.
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