Abstract: The use of heterogeneous systems in supercomputing is on the rise
as they improve both performance and energy eciency. However, the pro-
gramming of these machines requires considerable e ort to get the best results
in massively data-parallel applications. Maat is a library that enables OpenCL
programmers to eciently execute single data-parallel kernels using all the
available devices on a heterogeneous system. It o ers a set of load balanc-
ing methods, which perform the data partitioning and distribution among the
devices, exploiting more of the performance of the system and consequently re-
ducing 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 eciency of the di erent 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 di erent frequencies. The
obtained results show that in all the studied cases there is at least one load
balancing method that improves energy eciency.
