Work-in-Progress: Impacts of Critical-Section Granularity When Accessing Shared Resources.

The prevalence of computer-vision applications in autonomous vehicles necessitates the use of graphics processing units (GPUs), which must be shared among tasks due to size, weight, power, and cost constraints. Such sharing is typically managed via locking protocols. However, experiments reveal a trade-off in the granularity of such sharing; GPU operations' execution times are reduced if multiple GPU accesses are grouped in a single lock request (i.e., form a single critical section) rather than assuming only one access per critical section. This grouping exposes a broader trade-off between the extra lock overhead (and blocking) incurred by an individual task, and the analytical blocking experienced by all other tasks in the system. This trade-off is expressed herein via an extended resource model and explored using example task systems, demonstrating the impact of different access-grouping heuristics.