Massively parallel CMA-ES with increasing population

David RedonCRIStAL, BONUS, Pierre FortinCRIStAL, BONUS, Bilel DerbelCRIStAL, BONUS, Miwako TsujiRIKEN CCS, Mitsuhisa SatoRIKEN CCS
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Abstract

The Increasing Population Covariance Matrix Adaptation Evolution Strategy (IPOP-CMA-ES) algorithm is a reference stochastic optimizer dedicated to blackbox optimization, where no prior knowledge about the underlying problem structure is available. This paper aims at accelerating IPOP-CMA-ES thanks to high performance computing and parallelism when solving large optimization problems. We first show how BLAS and LAPACK routines can be introduced in linear algebra operations, and we then propose two strategies for deploying IPOP-CMA-ES efficiently on large-scale parallel architectures with thousands of CPU cores. The first parallel strategy processes the multiple searches in the same ordering as the sequential IPOP-CMA-ES, while the second one processes concurrently these multiple searches. These strategies are implemented in MPI+OpenMP and compared on 6144 cores of the supercomputer Fugaku. We manage to obtain substantial speedups (up to several thousand) and even super-linear ones, and we provide an in-depth analysis of our results to understand precisely the superior performance of our second strategy.
大规模并行 CMA-ES 随着人口的增加而增加
增殖群体协方差矩阵适应进化策略(IPOP-CMA-ES)算法是一种专用于黑箱优化的参考随机优化器,在这种情况下,没有关于底层问题结构的先验知识。本文旨在利用高性能计算和并行性加速 IPOP-CMA-ES 解决大型优化问题。我们首先展示了如何在线性代数运算中引入 BLAS 和 LAPACK 例程,然后提出了在具有数千个 CPU 内核的大规模并行架构上高效部署 IPOP-CMA-ES 的两种策略。第一种并行策略以与顺序 IPOP-CMA-ES 相同的顺序处理多重搜索,而第二种策略则同时处理这些多重搜索。这些策略是在 MPI+OpenMP 中实现的,并在超级计算机 Fugaku 的 6144 个内核上进行了比较。我们设法获得了大幅提速(高达数千),甚至超线性,我们对结果进行了深入分析,以准确理解第二种策略的优越性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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