Gdev: first-class GPU resource management in the operating system
USENIX ATC'12 Proceedings of the 2012 USENIX conference on Annual Technical Conference
Paradigmatic shifts for exascale supercomputing
The Journal of Supercomputing
Towards adaptive GPU resource management for embedded real-time systems
ACM SIGBED Review
Zero-copy I/O processing for low-latency GPU computing
Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems
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General-purpose computing on graphics processing units, also known as GPGPU, is a burgeoning technique to enhance the computation of parallel programs. Applying this technique to real-time applications, however, requires additional support for timeliness of execution. In particular, the non-preemptive nature of GPGPU, associated with copying data to/from the device memory and launching code onto the device, needs to be managed in a timely manner. In this paper, we present a responsive GPGPU execution model (RGEM), which is a user-space runtime solution to protect the response times of high-priority GPGPU tasks from competing workload. RGEM splits a memory-copy transaction into multiple chunks so that preemption points appear at chunk boundaries. It also ensures that only the highest-priority GPGPU task launches code onto the device at any given time, to avoid performance interference caused by concurrent launches. A prototype implementation of an RGEM-based CUDA runtime engine is provided to evaluate the real-world impact of RGEM. Our experiments demonstrate that the response times of high-priority GPGPU tasks can be protected under RGEM, whereas their response times increase in an unbounded fashion without RGEM support, as the data sizes of competing workload increase.