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Simultaneous Multithreading (SMT) has the ability to dramatically improve real-time scheduling, but existing methods are cumbersome, frequently need specialized hardware, or are limited to producing table-based schedules. Here, an easily portable method for quickly applying SMT to priority-driven hard real-time systems is given. Using a combination of integer linear programming and heuristic bin-packing, a partitioned Earliest-Deadline-First (EDF) scheduler that takes advantage of SMT is produced. The integer linear programming and partitioning are done offline, but generally require only a few seconds, even given over a hundred tasks. A large-scale schedulability study is conducted, showing that compared to partitioned scheduling without SMT, the schedulable utilization for the considered hardware platform is nearly doubled in the best cases. 

Autonomous vehicles often employ computer-vision (CV) algorithms that track the movements of pedestrians and other vehicles to maintain safe distances from them. These algorithms are usually expressed as real-time processing graphs that have cycles due to back edges that provide history information. If immediate back history is required, then such a cycle must execute sequentially. Due to this requirement, any graph that contains a cycle with utilization exceeding 1.0 is categorically unschedulable, i.e., bounded graph response times cannot be guaranteed. Unfortunately, such cycles can occur in practice, particularly if conservative execution-time assumptions are made, as befits a safety-critical system. This dilemma can be obviated by allowing older back history, which enables parallelism in cycle execution at the expense of possibly affecting the accuracy of tracking. However, the efficacy of this solution hinges on the resulting history-vs.-accuracy trade-off that it exposes. In this paper, this trade-off is explored in depth through an experimental study conducted using the open-source CARLA autonomous driving simulator. Somewhat surprisingly, easing away from always requiring immediate back history proved to have only a marginal impact on accuracy in this study.