Cooperative Multitasking for GPU-Accelerated Grid Systems

Exploiting the graphics processing unit (GPU) is useful to obtain higher performance with a less number of host machines in grid systems. One problem in GPU-accelerated grid systems is the lack of efficient multitasking mechanisms. In this paper, we propose a cooperative multitasking method capable of simultaneous execution of a graphics application and a CUDA-based scientific application on a single GPU. To prevent significant performance drop in frame rate, our method (1) divides scientific tasks into smaller subtasks and (2) serially executes them at the appropriate intervals. Experimental results show that the proposed method is useful to control the frame rate of the graphics application and the throughput of the scientific application. For example, matrix multiplication can be processed at 50% of the dedicated throughput while achieving interactive rendering at 54 frames per second.