ML-Based Thermal and Cache Contention Alleviation on Clustered Manycores With 3-D HBM

Enabled by the recent advancements in 2.5D/3-D integration and packaging, the integration of clustered manycore processors with high-bandwidth memory (HBM) is gaining prominence to satisfy the increasing memory bandwidth demands. Although this integration can offer significant performance gains, it is still limited by cache contention in the final-level cache on the clusters and by the thermal issues in the 3-D HBM. While the existing state-of-the-art resource management techniques have tackled these issues in isolation, we argue that the cache contention and the temperature of both the manycore and the HBM must be considered jointly to harness the full performance potential of such modern architectures. To cover this gap in the literature, we present MTCM, the first resource management technique that considers the cache contention in maximizing the system performance, while maintaining the thermal safety across both the manycore and the HBM stack. Enabled by our accurate, yet lightweight, neural network models, our proposed task migration and dynamic voltage and frequency scaling policies can accurately predict the impact of runtime decisions on the performance and temperature of both the subsystems. Our extensive evaluation experiments reveal a significant performance improvement over existing state of the art by up to $1\times $ , while maintaining thermal safety of both the manycore and the HBM.