Pedro Trancoso,

Department of Computer Science, University of Cyprus


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 achieve this goal we propose data-parallel versions of the original database scan and join algorithms. For this work we use the Rapidmind platform to code our algorithms as it offers the possibility of executing the same program on both Cell/BE and GPUs. 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 of the same generation (8500 and 8800), 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. In addition, for most cases, GPU performance surpassed the general-purpose multi-core.


Date: 2009-Jun-30     Time: 14:00:00     Room: 336

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