Blocking-Aware Partitioned Real-Time Scheduling for Uniform Heterogeneous Multicore Platforms

Abstract

Heterogeneous multicore processors have recently become de facto computing engines for state-of-the-art embedded applications. Nonetheless, very little research focuses on the scheduling of periodic (implicit-deadline) real-time tasks upon heterogeneous multicores under the requirements of task synchronization, which is stemmed from resource access conflicts and can greatly affect the schedulability of tasks. In view of partitioned Earliest Deadline First and Multiprocessor Stack Resource Policy, we first discuss the blocking-aware utilization bound for uniform heterogeneous multicores and then illustrate its non-monotonicity, where the bound may decrease with more deployed cores. Following the insights obtained from the bound analysis, taking the system heterogeneity into consideration, we propose a Synchronization-Aware Task Partitioning Algorithm for Heterogeneous Multicores (SA-TPA-HM)). Several resource-guided and heterogeneity-oriented mapping heuristics are incorporated to reduce the negative impacts of blocking interferences for better schedulability performance of tasks and balanced workload distribution across cores. The extensive simulation results show that SA-TPA-HM can obtain the schedulability ratios approximate to an Integer Non-Linear Programming--based solution, and much higher (e.g., 60% more) in contrast to the existing partitioning algorithms targeted at homogeneous multicores. The measurement results in Linux kernel further reveal the practical viability of SA-TPA-HM that can experience lower runtime overhead (e.g., 15% less) when compared to other mapping schemes.

New Insights Into the Real-Time Performance of a Multicore Processor

“…Another popular approach is to replace the standard Linux scheduler with earliest-deadline first (EDF) [30,31]. Though this method reduces runtime overheads, sharing resources between tasks on multicore processors introduces blocking delays and priority inversion.…”

“…Though this method reduces runtime overheads, sharing resources between tasks on multicore processors introduces blocking delays and priority inversion. To overcome such limitation, Han et al [30] proposed a blocking-aware partitioned scheme which incorporates several resource-guided mapping heuristics. Because dynamic deadlines on the EDF scheduler constitute task priorities, Lelli et al [31] has adopted deadline inheritance.…”