Energy efficiency of load balancing for data-parallel applications in heterogeneous systems

The use of heterogeneous systems in supercomputing is on the rise as they improve both performance and energy efficiency. However, the programming of these machines requires considerable effort to get the best results in massively data-parallel applications. Maat is a library that enables OpenCL programmers to efficiently execute single data-parallel kernels using all the available devices on a heterogeneous system. It offers a set of load balancing methods, which perform the data partitioning and distribution among the devices, exploiting more of the performance of the system and consequently reducing execution time. Until now, however, a study of the implications of these on the energy consumption has not been made. Therefore, this paper analyses the energy efficiency of the different load balancing methods compared to a baseline system that uses just a single GPU. To evaluate the impact of the heterogeneity of the system, the GPUs were set to different frequencies. The obtained results show that in all the studied cases there is at least one load balancing method that improves energy efficiency.