Parallel workloads are commonly modeled as directed acyclic graphs (DAGs). While DAG scheduling is an important tool, it is plagued by capacity loss; it is not uncommon to see half of a platform go unused. Here this loss is attacked from a new direction: reducing per-DAG utilization prior to assigning computing cores to a DAG. Specifically, simultaneous multithreading (SMT) is used to schedule individual nodes of a DAG task in parallel on the same physical computing core. An optimization program is given that applies SMT to a DAG in a way that minimizes total utilization without compromising correctness. Results for both individual DAGs and systems of DAGs are evaluated using both a large-scale study of synthetic DAGs and a case study. Optimal use of the program can reduce DAG utilization and required core counts by over 40% in the best cases and by 25% in nearly half of cases. Runtime requirements for the optimization program are considered, and a tunable parameter is provided to make tradeoffs between runtime and optimality, allowing even DAGs with 500 nodes to benefit.

Graphics Processing Units (GPUs) are widely used in safety-critical real-time systems such as autonomous vehicles due to their high performance on artificial intelligence (AI) work-loads. As the computing power of recent GPUs keeps growing, it becomes increasingly possible to allow multiple independent programs to access the GPU concurrently. This complicates timing analysis, as contention for shared GPU resources renders execution times less predictable and worst-case execution times (WCETs) difficult to estimate. This paper provides a method for producing enemy programs that intentionally contend for GPU resources in order to enable more confident measurement-based WCET estimations. This paper provides an experiment-driven method to design effective enemy programs for several different interference channels—specific shared resources within the GPU through which concurrent computations may impact others' execution times. The method is flexible and can be applied to different GPU sharing mechanisms. The enemies are evaluated against a large number of real GPU applications, and the results indicate that these enemies cause higher slowdowns for GPU tasks than other baseline resource-stressing methods.