Minimizing response times of automotive dataflows on multicore

Glenn A. Elliott, Namhoon AKim, Jeremy P. Erickson, Cong Liu, James H. Andersony
2014 2014 IEEE 20th International Conference on Embedded and Real-Time Computing Systems and Applications  
Dataflow software architectures are prevalent in prototypes of advanced automotive systems, for both driver-assisted and autonomous driving. Safety constraints of these systems necessitate real-time performance guarantees. Automotive prototypes often ensure such constraints through overprovisioning and dedicated hardware; however, a commercially viable system must utilize as few low-cost multicore processors as possible to meet size, weight, and power constraints. In short, these platforms must
more » ... 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 PGM RT , a portable middleware framework for managing real-time dataflow applications on multicore platforms. The efficacy of our techniques is demonstrated through overhead-aware schedulability experiments and runtime observations. Results for our test platform show that cache-aware clustered scheduling outperforms naïve partitioned and global approaches in terms of schedulability and end-to-end response times of dataflows. *
doi:10.1109/rtcsa.2014.6910527 dblp:conf/rtcsa/ElliottAELA14 fatcat:t57zg2tz3vftxp5bunjfcj4enu