Can Flexible Multi-Core Scheduling Help to Execute Machine Learning Algorithms Resource-Efficiently?

Abstract

Machine learning techniques such as model-based optimization are frequently used to solve expensive problems. Since a sequential execution of these algorithms is time-intensive due to the problem complexity, several attempts have been made to parallelize existing approaches. However, no state-of-the-art technique is able to efficiently exploit the full potential of multi-core architectures up to now. In this work, we propose a flexible extension to the Resource-Aware Model-Based Optimization framework (RAMBO) adopting multi-core scheduling techniques, which allows to use the available resources in a more efficient way and thus reduces the time required to solve expensive optimization problems.