OpenPOWER Summit 2021

IBM Bayesian Optimization Accelerator (BOA) is a global optimization toolkit which applies machine learning techniques to solve some challenges arising from many practical engineering and designing problems: computational or experimental simulations of the sampling space is very expensive; the objective functions have multiple local optima; the collected data are noisy and do not have derivatives or analytic forms. Other features of BOA include batch sampling, parameter analysis, extensive implementations of kernel functions, acquisition functions and optimization techniques. The solution is integrated as an appliance which can be easily hooked to existing High Performance Computing (HPC) or enterprise environment of different Operating Systems. In this talk, we will discuss how BOA works with existing HPC environment to get the optimization done. How to write interface functions to connect BOA with external workload to be optimized. We will also present some use case studies which show performance gains against some traditional methods such as grid search and random search.