Multi-kernel Auto-Tuning on GPUs: Performance and Energy-Aware Optimization

Prompted by their very high computational capabilities and memory bandwidth, Graphics Processing Units (GPUs) are already widely used to accelerate the execution of many scientific applications. However, programmers are still required to have a very detailed knowledge of the GPU internal architecture when tuning the kernels, in order to improve either performance or energy-efficiency. Moreover, different GPU devices have different characteristics, moving a kernel to a different GPU typically requires re-tuning the kernel execution, in order to efficiently exploit the underlying hardware. The procedure proposed in this work is based on real-time kernel profiling and GPU monitoring and it automatically tunes parameters from several concurrent kernels to maximize the performance or minimize the energy consumption. Experimental results on NVIDIA GPU devices with up to 4 concurrent kernels show that the proposed solution achieves near optimal configurations. Furthermore, significant energy savings can be achieved by using the proposed energy-efficiency auto-tuning procedure.