Multitasking Real-time Embedded GPU Computing Tasks

Abstract

In this study, we consider the specific characteristics of workloads that involve multiple real-time embedded GPU computing tasks and design several schedulers that use alternative approaches. Then, we compare the performance of schedulers and determine which scheduling approach is more effective for a given workload and why. The major conclusions of this study include: (a) Small kernels benefit from running kernels concurrently. (b) The combination of small kernels, high-priority kernels with longer runtimes, and lower-priority kernels with shorter runtimes benefits from a CPU scheduler that dynamically changes kernel order on the Fermi architecture. (c) Due to limitations of existing GPU architectures, currently CPU schedulers outperform their GPU counterparts. We also highlight the shortcomings of current GPU architectures with regard to running multiple real-time tasks, and recommend new features that would improve scheduling, including hardware priorities, preemption, programmable scheduling, and a common time concept and atomics across the CPU and GPU.