David RedonCRIStAL, BONUS, Pierre FortinCRIStAL, BONUS, Bilel DerbelCRIStAL, BONUS, Miwako TsujiRIKEN CCS, Mitsuhisa SatoRIKEN CCS
{"title":"Massively parallel CMA-ES with increasing population","authors":"David RedonCRIStAL, BONUS, Pierre FortinCRIStAL, BONUS, Bilel DerbelCRIStAL, BONUS, Miwako TsujiRIKEN CCS, Mitsuhisa SatoRIKEN CCS","doi":"arxiv-2409.11765","DOIUrl":null,"url":null,"abstract":"The Increasing Population Covariance Matrix Adaptation Evolution Strategy\n(IPOP-CMA-ES) algorithm is a reference stochastic optimizer dedicated to\nblackbox optimization, where no prior knowledge about the underlying problem\nstructure is available. This paper aims at accelerating IPOP-CMA-ES thanks to\nhigh performance computing and parallelism when solving large optimization\nproblems. We first show how BLAS and LAPACK routines can be introduced in\nlinear algebra operations, and we then propose two strategies for deploying\nIPOP-CMA-ES efficiently on large-scale parallel architectures with thousands of\nCPU cores. The first parallel strategy processes the multiple searches in the\nsame ordering as the sequential IPOP-CMA-ES, while the second one processes\nconcurrently these multiple searches. These strategies are implemented in\nMPI+OpenMP and compared on 6144 cores of the supercomputer Fugaku. We manage to\nobtain substantial speedups (up to several thousand) and even super-linear\nones, and we provide an in-depth analysis of our results to understand\nprecisely the superior performance of our second strategy.","PeriodicalId":501422,"journal":{"name":"arXiv - CS - Distributed, Parallel, and Cluster Computing","volume":"189 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Distributed, Parallel, and Cluster Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.11765","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.