A multi-staged niched evolutionary approach for allocating parallel tasks with joint optimization of performance, energy, and temperature

Abstract

This paper presents a multi-stage multi-objective evolutionary approach (MS-MOEA) for allocating parallel computations on multi-core processors by joint optimizing performance (P), energy (E), and temperature (T). Evolutionary techniques have been shown to be effective for solving optimization problems, including our own previous work on solving the PET-optimized scheduling (PETOS) problem. There have long been a great many debates and rivalries between various evolutionary approaches, such as the SPEA or NSGA, with regard to their relative matters. The novelty of the proposed MS-MOEA approach is its amalgamation of the basic evolutionary algorithms that are already shown to be highly effective, thereby creating a niche of these techniques. The niche takes advantages of the strengths of each baseline technique for achieving additional enhancement in the precision of the optimization. We propose six multi-stage hybrids, each designed with either niched fitness assignment strategy, or combining populations from multiple MOEAs, or incorporating the problem knowledge into the conventional technique. The experimental results measure the quality of resulting Pareto fronts and demonstrate that the proposed MS-MOEAs yield better optimization for the PETOS problem in achieving three-objective in parallel task-to-core mapping.