Abstract A massively parallel Grammatical Evolution technique with OpenCL
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A massively parallel Grammatical Evolution technique with OpenCL

机译:使用OpenCL的大规模并行语法演变技术

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Abstract Grammatical Evolution (GE) is a bio-inspired metaheuristic capable of evolving programs in an arbitrary language using a formal grammar. Among the major applications of the technique, the automatic inference of models from data can be highlighted. As with other genetic programming techniques, GE has a high computational cost. However, the algorithm has steps that can be computed independently, enabling the use of parallel computing to reduce the execution time and, consequently, making it possible its application to larger and more complex problems. Here, models of massively parallel computation for GE are studied and proposed using OpenCL, a framework for the creation of parallel algorithms in heterogeneous computing environments. Computational experiments were conducted to analyze the performance of an implementation using GPUs (Graphics Processing Units), when compared to a sequential implementation in CPUs (Central Processing Units). Finally, speedups of up to 528 × were achieved, when all steps are performed in parallel in a GPU. Highlights A model of a massively parallel computation for Grammatical Evolution is proposed. Evaluation of the proposed technique using OpenCL. The candidate solutions and data are parallelized. Performance gains of up to 528 × were observed.
机译: 摘要 语法进化(GE)是一种受生物启发的元启发法,能够使用形式语法以任意语言演化程序。在该技术的主要应用中,可以强调从数据自动推断模型。与其他基因编程技术一样,GE具有很高的计算成本。但是,该算法具有可以独立计算的步骤,从而可以使用并行计算来减少执行时间,因此可以将其应用于更大更复杂的问题。在这里,使用OpenCL研究和提出了GE的大规模并行计算模型,OpenCL是在异构计算环境中创建并行算法的框架。与CPU(中央处理单元)中的顺序实现相比,进行了计算实验以分析使用GPU(图形处理单元)的实现的性能。最后,加速高达当所有步骤在GPU中并行执行时,实现了 528 × 突出显示 提出了用于语法进化的大规模并行计算模型。 / ce:para> 使用OpenCL对提议的技术进行评估。 候选解决方案和数据是并行的。 性能提升高达 528 ×

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