Dynamic clustering for distinct parallel programming models on NoC-based MPSoCs

Abstract

This paper investigates the impact of dynamic clustering and the use of hardware support for distinct parallel programming models in an NoC-based MPSoC environment. Using a dynamically adaptable hardware, the platform provides clusters that implement either a shared memory organization or a distributed memory organization in order to meet applications' requirements without any computational overhead. The entire process is completely transparent for the programmer. In addition, a scheduler is used to take advantage of changes on the degree of parallelism of an application to improve workload balancing. Experimental results show that dynamic clustering can improve performance up to 77% (54% in average) and can provide energy savings up to 58% (42% in average).