Abstract:

This paper describes GPUSync, which is a framework for managing graphics processing units (GPUs) in multi-GPU multicore real-time systems.  GPUSync was designed with flexibility, predictability, and parallelism in mind.  Specifically, it can be applied under either static- or dynamic-priority CPU scheduling; can allocate CPUs/GPUs on a partitioned, clustered, or global basis;  provides flexible mechanisms for allocating GPUs to tasks; enables task state to be migrated among different GPUs, with the potential of breaking such state into smaller “chunks”; provides migration cost predictors that determine when migrations can be effective; enables a single GPU's different engines to be accessed in parallel; properly supports GPU-related interrupt and worker threads according to the sporadic task model, even when GPU drivers are closed-source; and provides budget policing to the extent possible, given that GPU access is non-preemptive.  No prior real-time GPU management framework provides a comparable range of features.

G. Elliott, B. Ward, and J. Anderson, "GPUSync: A Framework for Real-Time GPU Management." Proceedings of the 35th Real-Time Systems Symposium (RTSS), December 2013.

Copy of paper available here.  Full version with appendix.

Source code here.

(The material behind this paper was developed over the course of a year and a half of research. Not all of our results could be put in a single paper.  Other information on GPUSync, such as memory transmission costs to GPUs and the effects on the system memory bus, can be found here.)