During the past decades, evolutionary computation (EC) has demonstrated promising potential in solving various complex optimization problems of relatively small scales. Nowadays, however, ongoing developments in modern science and engineering are bringing increasingly grave challenges to the conventional EC paradigm in terms of scalability. As problem scales increase, on the one hand, the encoding spaces (i.e., dimensions of the decision vectors) are intrinsically larger; on the other hand, EC algorithms often require growing numbers of function evaluations (and probably larger population sizes as well) to work properly. To meet such emerging challenges, not only does it require delicate algorithm designs, but more importantly, a high-performance computing framework is indispensable. Hence, we develop a distributed GPU-accelerated algorithm library -- EvoX. First, we propose a generalized workflow for implementing general EC algorithms. Second, we design a scalable computing framework for running EC algorithms on distributed GPU devices. Third, we provide user-friendly interfaces to both researchers and practitioners for benchmark studies as well as extended real-world applications. To comprehensively assess the performance of EvoX, we conduct a series of experiments, including: (i) scalability test via numerical optimization benchmarks with problem dimensions/population sizes up to millions; (ii) acceleration test via a neuroevolution task with multiple GPU nodes; (iii) extensibility demonstration via the application to reinforcement learning tasks on the OpenAI Gym. The code of EvoX is available at https://github.com/EMI-Group/EvoX.
翻译:在过去几十年中,进化计算(EC)在解决规模相对较小的各种复杂优化问题方面显示出了大有潜力。然而,如今,现代科学和工程的不断发展给常规欧盟委员会模式的可缩放性带来了越来越严峻的挑战。一方面,随着问题规模的扩大,编码空间(即决定矢量的维度)在本质上更大;另一方面,EC算法往往需要越来越多的功能评价(以及可能更大的人口规模)才能正常运作。为了应对这些新出现的挑战,不仅需要微妙的算法设计,而且更重要的是,一个高性能计算框架是不可或缺的。因此,我们开发了一个分布式的GPU加速算法图书馆 -- -- EvoX。首先,我们建议了一个通用的流程来实施通用的EC算法。第二,我们设计了一个可缩放的计算框架,用于在分布式的GPU设备上运行EC算法。第三,我们为研究人员和从业人员提供方便用户的界面,用于基准研究以及扩展现实世界应用。为了全面评估EVOX的绩效,我们进行了一系列实验,其中包括:(i)EPU-AC-加速算法应用的 EVI-S-Cliumalimalimal imal imal imal imal imal imal ex imal ex imal imal imal imal imal imal imal imital imital impol imital imital imital imital imit (i) (i) (i) (i) (i) imital imital imital imital imital imital imital imital imital imital imital im im im im im im) abal imital imital imital im im im im im im im im) abal imital imital imal im) abal im) abildal imital imital imital im) abal imital im imital im imital imital imital imital imital imital