Fair share: Allocation of GPU resources for both performance and fairness

General-purpose computing on the GPU (GPGPU computing) is becoming widely adopted for an increasing variety of applications. However, it has been shown that as the available computing elements in the GPU increase with every generation some GPGPU applications fail to fully utilize the GPU resources. Spatial multitasking-subdividing GPU resources amongst concurrently-running applications-has been shown to increase overall system performance and utilization for GPGPU computing. However, dividing the computing resources among multiple applications to maximize system performance often results in one application having “unfair” access to GPU resources. Yet, evenly dividing resources among applications does not guarantee equal speedups to each application; nor does it take into account overall system performance. In this paper we examine several different ways to characterize “fairness” for GPGPU spatial multitasking, by balancing individual application's performance and overall system performance. We further present a run-time algorithm to predict and adjust the SM allocation at runtime to meet the desired fairness metric.