Acceleration of grammatical evolution using graphics processing units: computational intelligence on consumer games and graphics hardware
Acceleration of grammatical evolution using graphics processing units: computational intelligence on consumer games and graphics hardware
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使用图形处理单元加速语法演化：消费游戏和图形硬件上的计算智能
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
J. Jaros
中科院分区：
文献类型：
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作者：
P. Pospichal;E. Murphy;M. O’Neill;J. Schwarz;J. Jaros
一些文献表明，符号回归适用于金融市场的数据分析和预测。语法进化（GE）是遗传编程（GP）的一种基于语法的形式，已成功应用于解决包括符号回归在内的各种任务。然而，通常计算GP中的解决方案的适应度的计算工作可以限制可能的应用领域和/或所进行的实验的范围。本文讨论了利用主流图形处理器（GPU）加速GE求解符号回归。讨论了GPU的优化细节，分析了NVCC编译器。我们设计了一个有效的映射到CUDA框架的算法，在这样做必须解决GPU的方法，如PCI-express瓶颈和主内存事务的约束。这是GE首次在GPU上运行。我们测量我们的实现运行在CPU Core i7和GPU GTX 480的一个核心上，以及用JAVA编写的GE库GEVA。结果表明，我们的算法提供了相同的收敛性，它是适合于大量的回归点，GPU能够达到高达39倍的速度比GEVA在C语言编写的串行CPU代码的加速比。总之，如果使用得当，GPU可以为GE处理符号回归提供有趣的性能提升。
Several papers show that symbolic regression is suitable for data analysis and prediction in financial markets. Grammatical Evolution (GE), a grammar-based form of Genetic Programming (GP), has been successfully applied in solving various tasks including symbolic regression. However, often the computational effort to calculate the fitness of a solution in GP can limit the area of possible application and/or the extent of experimentation undertaken. This paper deals with utilizing mainstream graphics processing units (GPU) for acceleration of GE solving symbolic regression. GPU optimization details are discussed and the NVCC compiler is analyzed. We design an effective mapping of the algorithm to the CUDA framework, and in so doing must tackle constraints of the GPU approach, such as the PCI-express bottleneck and main memory transactions. This is the first occasion GE has been adapted for running on a GPU. We measure our implementation running on one core of CPU Core i7 and GPU GTX 480 together with a GE library written in JAVA, GEVA. Results indicate that our algorithm offers the same convergence, and it is suitable for a larger number of regression points where GPU is able to reach speedups of up to 39 times faster when compared to GEVA on a serial CPU code written in C. In conclusion, properly utilized, GPU can offer an interesting performance boost for GE tackling symbolic regression.