Load balancing is a key aspect to face when implementing any parallel application for Graphic Processing Units (GPUs). It is particularly crucial if one considers that it strongly impacts on performance, power and energy efficiency of the whole application. Many different partitioning techniques have been proposed in the past to deal with either very regular workloads (static techniques) or with irregular workloads (dynamic techniques). Nevertheless, it has been proven that no one of them provides a sound trade-off, from the performance point of view, when applied in both cases. More recently, a dynamic multi-phase approach has been proposed for workload partitioning and work item-to-thread allocation. Thanks to its very low complexity and several architecture-oriented optimizations, it can provide the best results in terms of performance with respect to the other approaches in the literature with both regular and irregular datasets. Besides the performance comparison, no analysis has been conducted to show the effect of all these techniques on power and energy consumption on both GPUs for desktop and GPUs for low-power embedded systems. This paper shows and compares, in terms of performance, power, and energy efficiency, the experimental results obtained by applying all the different static, dynamic, and semi-dynamic techniques at the state of the art to different datasets and over different GPU technologies (i.e., NVIDIA Maxwell GTX 980 device, NVIDIA Jetson Kepler TK1 low-power embedded system).