GA–EDA: Hybrid Design Space Exploration Engine for Multicore Architecture

Abstract

Emergence of modern multicore architectures has made runtime reconfiguration of system resources possible. All reconfigurable system resources constitute a design space and the proper selection of configuration of these resources to improve the system performance is known as Design Space Exploration (DSE). This reconfiguration feature helps in appropriate allocation of system resources to improve the efficiency in terms of performance, energy consumption, throughput, etc. Different techniques like exhaustive search of design space, architect’s experience, etc. are used for optimization of system resources to achieve desired goals. In this work, we hybridized two optimization algorithms, i.e., Genetic Algorithm (GA) and Estimation of Distribution Algorithm (EDA) for DSE of computer architecture. This hybrid algorithm achieved optimal balance between two objectives (minimal energy consumption and maximal throughput) by using decision variables such as number of cores, cache size and operating frequency. The final set of optimal solutions proposed by this GA–EDA hybrid algorithm is explored and verified by running different benchmark applications derived from SPLASH-2 benchmark suite on a cycle level simulator. The significant reduction in energy consumption without extensive impact on throughput in simulation results validate the use of this GA–EDA hybrid algorithm for DSE of multicore architecture. Moreover, the simulation results are compared with that of standalone GA, EDA and fuzzy logic to show the efficiency of GA–EDA hybrid algorithm.