FastFlow: Accelerating Deep Learning Model Training with Smart Offloading of Input Data Pipeline

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Abstract

When training a deep learning (DL) model, input data are pre-processed on CPUs and transformed into tensors, which are then fed into GPUs for gradient computations of model training. Expensive GPUs must be fully utilized during training to accelerate the training speed. However, intensive CPU operations for input data preprocessing (input pipeline) often lead to CPU bottlenecks; correspondingly, various DL training jobs suffer from GPU under-utilization. We propose FastFlow, a DL training system that automatically mitigates the CPU bottleneck by offloading (scaling out) input pipelines to remote CPUs. FastFlow carefully decides various offloading decisions based on performance metrics specific to applications and allocated resources, while leveraging both local and remote CPUs to prevent the inefficient use of remote resources and minimize the training time. FastFlow’s smart offloading policy and mechanisms are seamlessly integrated with TensorFlow for users to enjoy the smart offloading features without modifying the main logic. Our evaluations on our private DL cloud with diverse workloads on various resource environments show that FastFlow improves the training throughput by 1 ∼ 4.34× compared to Tensor-Flow without offloading, by 1 ∼ 4.52× compared to TensorFlow with manual CPU offloading (tf.data.service), and by 0.63 ∼ 2.06× compared to GPU offloading (DALI).