McCore: A Holistic Management of High-Performance Heterogeneous Multicores

Heterogeneous multicore systems have emerged as a promising approach to scale performance in high-end desktops within limited power and die size constraints. Despite their advantages, these systems face three major challenges: memory bandwidth limitation, shared cache contention, and heterogeneity. Small cores in these systems tend to occupy a significant portion of shared LLC and memory bandwidth, despite their lower computational capabilities, leading to performance degradation of up to 18% in memory-intensive workloads. Therefore, it is crucial to address these challenges holistically, considering shared resources and core heterogeneity while managing shared cache and bandwidth. To tackle these issues, we propose McCore, a comprehensive solution that reorganizes the heterogeneous multicore memory hierarchy and effectively leverages this structure through a hardware-based reinforcement learning (RL) scheduler. The McCore structure aims to enhance performance by partitioning the shared LLC based on each cluster's asymmetric computing power and conditionally enabling fine-grained access in small cores. The McCore RL agent holistically controls these structures, incorporating a hardware-based online RL scheduler that accounts for bandwidth utilization and caching effectiveness to consider the heterogeneity in McCore structures. By implementing the RL agent module as hardware that cooperates with existing hardware monitors and performance counters, low-latency scheduling is enabled without burdening the OS kernel. McCore achieves a 25.1% performance gain compared to the baseline and significantly outperforms existing state-of-the-art cache partitioning, sparse access managing schemes, and heterogeneous multicore schedulers, providing a comprehensive solution for high-performance heterogeneous multicore systems.