The rapid growth of data-intensive applications increases communication demands in many-core systems, where cache coherence, while essential for correct communication and data consistency, introduces substantial overhead due to frequent data sharing and coherence activities. As system scale and workload complexity grow, the resulting coherence traffic intensifies communication pressure, making the co-optimization of task mapping and routing essential for improving system performance. However, most existing approaches overlook cache coherence, leaving a substantial portion of coherence-induced communication unaccounted for and creating a mismatch between optimization objectives and actual communication patterns. Furthermore, by employing separate cost evaluators for mapping and routing, these approaches complicate objective coordination, may lead to conflicting decisions, and fail to capture the coherence-induced coupling between the two stages. To address these challenges, we propose CoCo, a coherence-aware co-optimization framework that jointly integrates task mapping and routing under a unified cost model for realistic scenarios. This unified model integrates communication cost, coherence overhead, and load imbalance into a single objective, enabling coherence-aware decision-making and effective trade-offs among optimization goals. Guided by this model, CoCo combines coherence-guided task mapping with reinforcement learning-based routing, where directional link weights are adjusted according to communication behavior to improve traffic distribution, enabling coherence-aware co-optimization for many-core systems. Experimental results show that CoCo reduces link utilization by 88.46%, packet delay by 17.40%, and execution time by 17.58% compared with existing approaches, highlighting the importance of cache coherence in co-optimization design.
Modern clustered (power-island) multicore processors increasingly expose retention as a low-overhead low-power state, yet exploiting it in real-time systems critically depends on whether the scheduler can create sufficiently long cluster-level all-cores-idle windows that exceed a break-even length. This feasibility bottleneck is particularly acute for always-on arbitrary-deadline Directed Acyclic Graph (DAG) workloads, where precedence-induced execution fluctuations fragment idle times and the relative deadline may exceed the period. This paper proposes a retention-oriented scheduling framework for such workloads, consisting of two algorithms with a clear progression. PRES performs design-time planning and constructs a postponable division for each DAG, reshaping execution to aggregate slack into contiguous cluster-level idle windows; a lightweight event-driven runtime then enforces the plan and triggers retention without increasing online scheduling complexity. Building on PRES, PREHS further improves core utilization via multi-DAG merging and intra-cluster spatial sharing, consolidating workload onto fewer active clusters to reduce static/leakage energy while preserving deadline guarantees. We conduct extensive experiments across diverse cluster configurations and DAG structural characteristics, and further include a case study based on an application-derived DAG from a real-world communication/signal-processing pipeline. The results show that, compared with a representative work-conserving baseline with automatic retention/PG, PREHS reduces static energy consumption by up to 65.62%; moreover, under the same resource constraints, PREHS improves task acceptance ratio over PRES by up to 43 percentage points, with the largest gains observed in resource-tight configurations.
Xiangzhen Xiao, Weijie Wang, Weichen Liu et al.· ACM Transactions on Embedded...· 0 citations