Minimizing Response Times of Automotive Dataflows on Multicore ∗

Dataflow software architectures are prevalent in prototypes of advanced automotive systems, for both driver-assisted and autonomous driving. Safety constraints of these sys-tems necessitate real-time performance guarantees. Automo-tive prototypes often ensure such constraints through over-provisioning and dedicated hardware; however, a commer-cially viable system must utilize as few low-cost multicore processors as possible to meet size, weight, and power con-straints. In short, these platforms must do more with less. To this end, we develop cache-aware and overhead-cognizant scheduling techniques that lessen guaranteed response times without unnecessarily constraining platform utilization. We implement these techniques in PGMRT, a portable middle-ware framework for managing real-time dataflow applica-tions on multicore platforms. The efficacy of our techniques is demonstrated through overhead-aware schedulability ex-periments and runtime observations. Results for our test platform show that cache-aware clustered scheduling out-performs naı̈ve partitioned and global approaches in terms of schedulability and end-to-end response times of dataflows.