NSGAII Applied to Unified Second Level Cache Memory Hierarchy Tuning Aiming Energy and Performance Optimization

Abstract

The evolutionary algorithm NSGAII was applied to the problem of cache memory hierarchy optimization, considering unified second level. The proposed multiobjective approach considers two main objectives: energy consumption and performance related to the number of cycles necessary to run an application.Experiments done with 18 applications from two benchmarks (PowerStone and Mibench) permitted to conclude that found solutions, when NSGAII is applied, are close to optimal solutions. Results also were compared with an existing heuristic (TECH-CYCLES) and was observed that the quality of results obtained are superior in all analyzed cases, being in average 187 times lower in terms of the cost function (FC=Energy x Cycles) that represents the two components: energy and cycles of the application. Evaluating the impact in terms of number of simulations and obtained results, could be noticed that NSGAII needs only 1% of search space, becoming competitive for architecture exploration with unified second level.

Exploração do Espaço de Projetos para Alocação de Recursos em Nuvem

“…A continuidade de atividades no âmbito deste trabalho objetivam aprofundar o estudo e experimentac ¸ão com a ferramenta MultiExplorer, com enfoque no algoritmo genético NSGA-II [Cordeiro and Silva-Filho 2010]. Adicionalmente, ênfase será dada no estudo teórico da fronteira de Pareto aplicado à explorac ¸ão de projetos.…”