Optimising energy and overhead for large parameter space simulations

TL;DR

This paper applies a genetic algorithm to efficiently identify optimal parameter sets across multiple objectives, such as energy and overhead, in large simulation spaces, enabling better decision-making and reducing energy use.

Contribution

It introduces a novel application of genetic algorithms to find Pareto frontiers for multi-objective optimization in large parameter spaces, improving energy efficiency and overhead management.

Findings

Reduced energy consumption by approximately 36%

Effectively identified Pareto optimal parameter sets

Demonstrated approach on HTC-Sim with RL scheduler

Abstract

Many systems require optimisation over multiple objectives, where objectives are characteristics of the system such as energy consumed or increase in time to perform the work. Optimisation is performed by selecting the `best' set of input parameters to elicit the desired objectives. However, the parameter search space can often be far larger than can be searched in a reasonable time. Additionally, the objectives are often mutually exclusive -- leading to a decision being made as to which objective is more important or optimising over a combination of the objectives. This work is an application of a Genetic Algorithm to identify the Pareto frontier for finding the optimal parameter sets for all combinations of objectives. A Pareto frontier can be used to identify the sets of optimal parameters for which each is the `best' for a given combination of objectives -- thus allowing decisions to be made with full knowledge. We demonstrate this approach for the HTC-Sim simulation system in the case where a Reinforcement Learning scheduler is tuned for the two objectives of energy consumption and task overhead. Demonstrating that this approach can reduce the energy consumed by ~36% over previously published work without significantly increasing the overhead.