Enabling Efficient Large-Scale Deep Learning Training with Cache Coherent Disaggregated Memory Systems

Modern deep learning (DL) training is memory-consuming, constrained by the memory capacity of each computation component and cross-device communication bandwidth. In response to such constraints, current approaches include increasing parallelism in distributed training and optimizing inter-device communication. However, model parameter communication is becoming a key performance bottleneck in distributed DL training. To improve parameter communication performance, we propose COARSE, a disaggregated memory extension for distributed DL training. COARSE is built on modern cache-coherent interconnect (CCI) protocols and MPI-like collective communication for synchronization, to allow low-latency and parallel access to training data and model parameters shared among worker GPUs. To enable high bandwidth transfers between GPUs and the disaggregated memory system, we propose a decentralized parameter communication scheme to decouple and localize parameter synchronization traffic. Furthermore, we propose dynamic tensor routing and partitioning to fully utilize the non-uniform serial bus bandwidth varied across different cloud computing systems. Finally, we design a deadlock avoidance and dual synchronization to ensure high-performance parameter synchronization. Our evaluation shows that COARSE achieves up to 48.3% faster DL training compared to the state-of-the-art MPI AllReduce communication.