Massively parallel CMA-ES with increasing population

TL;DR

This paper enhances the parallel efficiency of the IPOP-CMA-ES optimization algorithm for large-scale problems by implementing two strategies on supercomputers, achieving significant speedups and analyzing their performance.

Contribution

It introduces two parallelization strategies for IPOP-CMA-ES on high-performance computing architectures, improving scalability and efficiency for large optimization problems.

Findings

Up to several thousand speedups achieved.

Super-linear speedups observed with the second strategy.

Detailed analysis explains the superior performance of the second approach.

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.