AI 中文总结
针对多核系统中数据密集型应用通信需求增长及现有方法忽视缓存一致性等问题，提出CoCo框架，在统一成本模型下联合优化任务映射和路由，结合一致性引导映射与强化学习路由，实验证明其有效提升系统性能。
AI 中文摘要
数据密集型应用的快速增长增加了多核系统中的通信需求。缓存一致性虽对正确通信和数据一致性至关重要，但因频繁数据共享和一致性活动带来大量开销。随着系统规模和工作负载复杂度增加，一致性流量加剧通信压力，任务映射和路由的协同优化对提升系统性能至关重要。现有方法多忽视缓存一致性，且映射和路由成本评估器分离。为此提出CoCo框架，在统一成本模型下联合集成任务映射和路由，整合通信成本、一致性开销和负载不平衡为单一目标，结合一致性引导的任务映射与基于强化学习的路由。实验表明CoCo与现有方法相比，链路利用率降低88.46%，分组延迟降低17.40%，执行时间降低17.58%，凸显缓存一致性在协同优化设计中的重要性。
英文摘要
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.
CommentsAccepted by IEEE/ACM International Conference on Embedded Software (EMSOFT), with its proceedings published in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2026
