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.

翻译：递增种群协方差矩阵自适应进化策略（IPOP-CMA-ES）算法是一种经典的随机优化器，专门用于黑箱优化问题，其中关于底层问题结构没有先验知识。本文旨在通过高性能计算与并行化技术，在求解大规模优化问题时加速IPOP-CMA-ES。我们首先展示了如何在线性代数运算中引入BLAS和LAPACK例程，随后提出了两种策略，用于在拥有数千个CPU核心的大规模并行架构上高效部署IPOP-CMA-ES。第一种并行策略按照与串行IPOP-CMA-ES相同的顺序处理多个搜索过程，而第二种策略则并发处理这些多路搜索。这些策略基于MPI+OpenMP实现，并在超级计算机“富岳”的6144个核心上进行了对比测试。我们成功实现了显著的加速比（最高达数千倍），甚至获得了超线性加速，并通过深入的结果分析，精确阐释了第二种策略性能更优的原因。