Performance evaluation of two load balancing algorithms on a hybrid parallel architecture

Abstract

Accelerated Processing Units (APUs) are an emerging architecture that integrates, in a single silicon chip, the traditional CPU and the GPU. Due to its heterogeneous architecture, APUs impose new challenges to data parallel applications that want to take advantage of all the processing units available on the hardware to minimize its execution time. Some standards help in the task of writing parallel code for heterogeneous devices, but it is not easy to find the data division between CPU and GPU that will minimize the execution time. In this context, this work further extends and details load balancing algorithms designed to be used in a data parallel problem. Also, a sensitivity analysis of the parameters used in our models was performed. The results have shown that the algorithms are effective in their purpose of improving the performance of an application on an heterogeneous environment.