Performance study of interference on GPU and CPU resources with multiple applications

In the last years, the performance and capabilities of graphics processing units (GPUs) improved drastically, mostly due to the demands of the entertainment market, with consumers and companies alike pushing for improvements in the level of visual fidelity, which is only achieved with high performing GPU solutions. Beside the entertainment market, there is an ongoing global research effort for using such immense computing power for applications beyond graphics, such as the domain of general purpose computing. Efficiently combining these GPUs resources with existing CPU resources is also an important and open research task. This paper is a contribution to that effort, focusing on analysis of performance factors of combining both resource types, while introducing also a novel job scheduler that manages these two resources. Through experimental performance evaluation, this paper reports what are the most important factors and design considerations that must be taken into account while designing such job scheduler.