Accelerating Broadcast Communication with GPU Compression for Deep Learning Workloads

• Main Authors: Zhou, Qinghua, Anthony, Quentin, Shafi, Aamir, Subramoni, Hari, Panda, Dhabaleswar K. DK
• Format: Conference Proceeding
• Language: English
• Subjects: Benchmark testing; Broadcast; Compression; Computational modeling; Deep learning; GPU-Aware MPI; Graphics processing units; High performance computing; Libraries; Training
• ISSN: 2640-0316
• DOI: 10.1109/HiPC56025.2022.00016

With the rapidly increasing model sizes, state-of-the-art Deep Learning (DL) models rely on multiple GPU nodes to run distributed training. Large message communication of GPU data between the GPUs is becoming a performance bottleneck in the overall training performance. GPU-Aware MPI libraries are widely adopted for state-of-the-art DL frameworks to improve communication performance. In the existing optimization solutions for Distributed Data-Parallel (DDP) training, the broadcast operation is often utilized to sync up the updated model parameters among all the GPUs. However, for state-of-the-art GPU-Aware MPI libraries, broadcasting large GPU data turns to overburden the training performance due to the limited bandwidth of interconnect between the GPU nodes. On the other hand, the recent research on using GPU-based compression libraries to lower the pressure on the nearly saturated interconnection and co-designing online compression with the communication pattern provides a new perspective to optimize the performance of broadcast on modern GPU clusters.In this paper, we redesign the GPU-Aware MPI library to enable efficient collective-level online compression with an optimized chunked-chain scheme for large message broadcast communication. The proposed design is evaluated to show benefits at both microbenchmark and application levels. At the microbenchmark level, the proposed design can reduce the broadcast communication latency by up to 80.9% compared to the baseline using a state-of-the-art MPI library and 55.1% compared to the existing point-to-point-based compression on modern GPU clusters. For DDP training with PyTorch, the proposed design reduces the training time by up to 15.0% and 6.4% compared to the existing chunked-chain scheme and point-to-point-based compression, respectively, while keeping similar training accuracy. To the best of our knowledge, this is the first work that leverages online GPU-based compression techniques to significantly accelerate broadcast communication for DL workloads.