Summary of Qsync: Quantization-minimized Synchronous Distributed Training Across Hybrid Devices, by Juntao Zhao et al.
QSync: Quantization-Minimized Synchronous Distributed Training Across Hybrid Devices
by Juntao Zhao, Borui Wan, Yanghua Peng, Haibin Lin, Yibo Zhu, Chuan Wu
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed QSync system enables efficient synchronous data-parallel deep neural network (DNN) training over hybrid devices by strategically exploiting quantized operators. This approach combines heterogeneous training and inference GPUs, presenting challenges due to compute capability and memory capacity disparities. QSync selects a quantization-minimized setting for operators in the distributed DNN training graph based on each device’s available resource capacity, minimizing model accuracy degradation while maintaining training efficiency brought by quantization. The system includes a predictor that reflects sensitivity of DNN layers on fixed-point and floating-point low-precision operators, a replayer that accurately estimates latency of distributed hybrid mixed-precision training, and an allocator that efficiently synchronizes workers with minimized model accuracy degradation. QSync bridges the PyTorch computational graph to an optimized backend for quantization kernel performance and flexible support for various GPU architectures. Experimental results show that QSync’s predictor accurately simulates distributed mixed-precision training with <5% error, achieving a consistent 0.27-1.03% accuracy improvement over from-scratch training tasks compared to uniform precision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary QSync is a new way to train deep neural networks (DNNs) using a combination of different computer devices. Normally, these devices are used separately for training and inference tasks, but QSync lets them work together efficiently. This is important because it can help speed up the training process while also reducing errors. To do this, QSync uses special operators that reduce the amount of data being processed, which makes it easier to train on many devices at once. The system includes several components that work together to make sure the training process goes smoothly and efficiently. This approach has been shown to improve accuracy by up to 1% compared to traditional methods. |
Keywords
* Artificial intelligence * Inference * Neural network * Precision * Quantization
