Accurate Contention Estimate Scheduling Method Using Multiple Clusters of Many-core Platform

Embedded systems such as self-driving systems require a computing platform with high computing power and low power consumption. Multi-/many-core platforms satisfy exactly these requirements. However, for hard real-time applications, multiple demands on shared resources can hinder realtime performance. Memory is among the resources that can most dramatically impair the desired performance. Therefore, we addressed contentions induced by the shared memory. We improve the predictability of contentions by dividing tasks into the memory access phase and the execution phase using a Directed Acyclic Graph (DAG). Existing methods are able to make accurate contention estimations for one Compute Cluster (CC) of a Clustered many-core processor. Our method is able to do the same for multiple CCs, thereby doubling the scalability in consideration of contentions. Using an Integer Linear Programming (ILP) formulation, we produced a static, non-preemptive, partitioned, time-triggered schedule. We also conducted an experiment in order to minimize the makespan. The evaluation confirmed that our new method reduced the makespan by increasing the number of CCs.