Fast parallel genetic programming: multi-core CPU versus many-core GPU
Fast parallel genetic programming: multi-core CPU versus many-core GPU
复制标题
快速并行遗传编程：多核 CPU 与众核 GPU
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
D. Chitty
中科院分区：
文献类型：
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作者：
D. Chitty
遗传编程（GP）是一种计算密集型技术，在本质上也高度平行。近年来，通过利用具有数百个处理核心的多核图形卡的平行计算能力，通过基于GP CPU的标准方法实现了显着的性能提高。这使得能够并行评估健身案例和候选解决方案。但是，本文将证明，通过充分利用多核CPU，也可以实现类似的性能提高。本文将提出一个新的GP模型，该模型表明效率更高，同时还利用了缓存内存。此外，本文介绍的模型将利用流媒体SIMD扩展来获得进一步的性能改进。还提供了GP模型的并行版本，该版本优化了多个线程执行和缓存内存。提出的结果将表明，GP的多核CPU实现可以产生与GP最新图形卡实现相匹配的性能水平。实际上，证明了比标准GP高达420倍的性能增长，并且比图形卡实现的增益三倍。
Genetic Programming (GP) is a computationally intensive technique which is also highly parallel in nature. In recent years, significant performance improvements have been achieved over a standard GP CPU-based approach by harnessing the parallel computational power of many-core graphics cards which have hundreds of processing cores. This enables both fitness cases and candidate solutions to be evaluated in parallel. However, this paper will demonstrate that by fully exploiting a multi-core CPU, similar performance gains can also be achieved. This paper will present a new GP model which demonstrates greater efficiency whilst also exploiting the cache memory. Furthermore, the model presented in this paper will utilise Streaming SIMD Extensions to gain further performance improvements. A parallel version of the GP model is also presented which optimises multiple thread execution and cache memory. The results presented will demonstrate that a multi-core CPU implementation of GP can yield performance levels that match and exceed those of the latest graphics card implementations of GP. Indeed, a performance gain of up to 420-fold over standard GP is demonstrated and a threefold gain over a graphics card implementation.
DOI：
10.1007/bfb0055923
发表时间：
1998
期刊：
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影响因子：
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作者：
Moshe Sipper
通讯作者：
Moshe Sipper