dPRO: A Generic Profiling and Optimization System for Expediting Distributed DNN Training

• URL: http://arxiv.org/abs/2205.02473v1
• Date: Thu, 5 May 2022 07:15:25 GMT
• Title: dPRO: A Generic Profiling and Optimization System for Expediting Distributed DNN Training
• Authors: Hanpeng Hu, Chenyu Jiang, Yuchen Zhong, Yanghua Peng, Chuan Wu, Yibo Zhu, Haibin Lin, Chuanxiong Guo
• Abstract summary: This paper proposes dPRO, a tool to identify performance bottlenecks in distributed training systems. We implement dPRO on multiple deep learning frameworks (PyTorch, MXNet, AllReduce and Server architecture) and representative communication schemes. Extensive experiments show that dPRO predicts performance of distributed training in various settings with5% errors in most cases and finds optimization strategies with up to87.1%-up over the baselines.
• Score: 12.413533491501548
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distributed training using multiple devices (i.e., GPU servers) has been widely adopted for learning DNN models over large datasets. However, the performance of large-scale distributed training tends to be far from linear speed-up in practice. Given the complexity of distributed systems, it is challenging to identify the root cause(s) of inefficiency and exercise effective performance optimizations when unexpected low training speed occurs. To date, there exists no software tool which diagnoses performance issues and helps expedite distributed DNN training, while the training can be run using different machine learning frameworks. This paper proposes dPRO, a toolkit that includes: (1) an efficient profiler that collects runtime traces of distributed DNN training across multiple frameworks, especially fine-grained communication traces, and constructs global data flow graphs including detailed communication operations for accurate replay; (2) an optimizer that effectively identifies performance bottlenecks and explores optimization strategies (from computation, communication and memory aspects) for training acceleration. We implement dPRO on multiple deep learning frameworks (PyTorch, TensorFlow, MXNet) and representative communication schemes (AllReduce and Parameter Server architecture). Extensive experiments show that dPRO predicts performance of distributed training in various settings with<5% errors in most cases and finds optimization strategies with up to87.1%speed-up over the baselines.

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