# Scheduling Optimization Techniques for Neural Network Training

arXiv: https://arxiv.org/abs/2110.00929 · HTML: https://mlsanity.com/paper/2110.00929/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 67 references that are in the map.
- First posted on arXiv 3 Oct 2021.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://mlsanity.com/paper/2009.11462/) (2020)
- [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://mlsanity.com/paper/2004.02984/) (2020)
- [MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems](https://mlsanity.com/paper/1512.01274/) (2015)
- [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://mlsanity.com/paper/2006.03236/) (2020)
- [Asynchronous Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1710.06952/) (2017)
- [Memory-Efficient Pipeline-Parallel DNN Training](https://mlsanity.com/paper/2006.09503/) (2020)
- [DAPPLE: A Pipelined Data Parallel Approach for Training Large Models](https://mlsanity.com/paper/2007.01045/) (2020)
- [Deep learning with Elastic Averaging SGD](https://mlsanity.com/paper/1412.6651/) (2014)
- [Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads](https://mlsanity.com/paper/1901.05758/) (2019)
- [Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters](https://mlsanity.com/paper/1706.03292/) (2017)
- [Toward Understanding the Impact of Staleness in Distributed Machine Learning](https://mlsanity.com/paper/1810.03264/) (2018)
- [TicTac: Accelerating Distributed Deep Learning with Communication Scheduling](https://mlsanity.com/paper/1803.03288/) (2018)
- [LightPAFF: A Two-Stage Distillation Framework for Pre-training and Fine-tuning](https://mlsanity.com/paper/2004.12817/) (2020)
- [Priority-based Parameter Propagation for Distributed DNN Training](https://mlsanity.com/paper/1905.03960/) (2019)
- [Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study](https://mlsanity.com/paper/1509.04210/) (2015)
- [Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications](https://mlsanity.com/paper/1902.04610/) (2019)
- [Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning](https://mlsanity.com/paper/2012.02732/) (2020)

## Abstract (arXiv)

Neural network training requires a large amount of computation and thus GPUs are often used for the acceleration. While they improve the performance, GPUs are underutilized during the training.This paper proposes out-of-order (ooo) backprop, an effective scheduling technique for neural network training. By exploiting the dependencies of gradient computations, ooo backprop enables to reorder their executions to make the most of the GPU resources. We show that the GPU utilization in single-GPU, data-parallel, and pipeline-parallel training can be commonly improve by applying ooo back-prop and prioritizing critical operations. We propose three scheduling algorithms based on ooo backprop. For single-GPU training, we schedule with multi-stream out-of-order computation to mask the kernel launch overhead. In data-parallel training, we reorder the gradient computations to maximize the overlapping of computation and parameter communication; in pipeline-parallel training, we prioritize critical gradient computations to reduce the pipeline stalls.We evaluate our optimizations with twelve neural networks including a light-weight computer vision model (MobileNet) and largeNLP models (BERT and GPT-3) with up to forty eight V100 GPUs.Our scheduling algorithms effectively improve the performance of single-GPU training as well as data- and pipeline-parallel training.Compared to the respective state of the art training systems, the throughput is substantially improved for single-GPU, data-parallel, and pipeline-parallel training.