GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism

• URL: http://arxiv.org/abs/2406.17145v1
• Date: Mon, 24 Jun 2024 21:32:51 GMT
• Title: GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism
• Authors: Byungsoo Jeon, Mengdi Wu, Shiyi Cao, Sunghyun Kim, Sunghyun Park, Neeraj Aggarwal, Colin Unger, Daiyaan Arfeen, Peiyuan Liao, Xupeng Miao, Mohammad Alizadeh, Gregory R. Ganger, Tianqi Chen, Zhihao Jia,
• Abstract summary: Deep neural networks (DNNs) continue to grow rapidly in size, making them infeasible to train on a single device. This paper presents a new pipeline-parallel scheme that partitions a DNN into pipeline stages whose dependencies are identified by a directed acyclic graph. We also develop GraphPipe, a distributed system that exploits MME strategies to enable performant and scalable DNN training.
• Score: 20.44114440511298
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep neural networks (DNNs) continue to grow rapidly in size, making them infeasible to train on a single device. Pipeline parallelism is commonly used in existing DNN systems to support large-scale DNN training by partitioning a DNN into multiple stages, which concurrently perform DNN training for different micro-batches in a pipeline fashion. However, existing pipeline-parallel approaches only consider sequential pipeline stages and thus ignore the topology of a DNN, resulting in missed model-parallel opportunities. This paper presents graph pipeline parallelism (GPP), a new pipeline-parallel scheme that partitions a DNN into pipeline stages whose dependencies are identified by a directed acyclic graph. GPP generalizes existing sequential pipeline parallelism and preserves the inherent topology of a DNN to enable concurrent execution of computationally-independent operators, resulting in reduced memory requirement and improved GPU performance. In addition, we develop GraphPipe, a distributed system that exploits GPP strategies to enable performant and scalable DNN training. GraphPipe partitions a DNN into a graph of stages, optimizes micro-batch schedules for these stages, and parallelizes DNN training using the discovered GPP strategies. Evaluation on a variety of DNNs shows that GraphPipe outperforms existing pipeline-parallel systems such as PipeDream and Piper by up to 1.6X. GraphPipe also reduces the search time by 9-21X compared to PipeDream and Piper.

Related papers

• GNNPipe: Scaling Deep GNN Training with Pipelined Model Parallelism [10.723541176359452]
  Communication is a key bottleneck for distributed graph neural network (GNN) training. GNNPipe is a new approach that scales the distributed full-graph deep GNN training.
  arXiv  Detail & Related papers  (2023-08-19T18:44:14Z)
• Communication-Free Distributed GNN Training with Vertex Cut [63.22674903170953]
  CoFree-GNN is a novel distributed GNN training framework that significantly speeds up the training process by implementing communication-free training. We demonstrate that CoFree-GNN speeds up the GNN training process by up to 10 times over the existing state-of-the-art GNN training approaches.
  arXiv  Detail & Related papers  (2023-08-06T21:04:58Z)
• Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
  Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency. We present an IPU-optimized release of our custom SNN Python package, snnTorch.
  arXiv  Detail & Related papers  (2022-11-19T15:44:08Z)
• Receptive Field-based Segmentation for Distributed CNN Inference Acceleration in Collaborative Edge Computing [93.67044879636093]
  We study inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing network. We propose a novel collaborative edge computing using fused-layer parallelization to partition a CNN model into multiple blocks of convolutional layers.
  arXiv  Detail & Related papers  (2022-07-22T18:38:11Z)
• Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis [28.464210819376593]
  Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solve complex problems on unstructured networks, such as node classification, graph classification, or link prediction, with high accuracy. However, both inference and training of GNNs are complex, and they uniquely combine the features of irregular graph processing with dense and regular computations. This complexity makes it very challenging to execute GNNs efficiently on modern massively parallel architectures.
  arXiv  Detail & Related papers  (2022-05-19T17:11:45Z)
• PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication [24.05916878277873]
  Graph Convolutional Networks (GCNs) is the state-of-the-art method for learning graph-structured data. distributed GCN training incurs prohibitive overhead of communicating node features and feature gradients among partitions. We propose PipeGCN, a scheme that hides the communication overhead by pipelining inter- partition communication.
  arXiv  Detail & Related papers  (2022-03-20T02:08:03Z)
• BaPipe: Exploration of Balanced Pipeline Parallelism for DNN Training [9.551339069298011]
  BaPipe is a pipeline parallelism training framework for distributed deep learning. It automatically explores pipeline parallelism training methods and balanced partition strategies for distributed training. BaPipe provides up to 3.2x speedup and 4x memory reduction in various platforms.
  arXiv  Detail & Related papers  (2020-12-23T08:57:39Z)
• Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks [55.98291376393561]
  Graph neural networks (GNNs) have emerged as a powerful tool for learning software engineering tasks. Recurrent neural networks (RNNs) are well-suited to long sequential chains of reasoning, but they do not naturally incorporate program structure. We introduce a novel GNN architecture, the Instruction Pointer Attention Graph Neural Networks (IPA-GNN), which improves systematic generalization on the task of learning to execute programs.
  arXiv  Detail & Related papers  (2020-10-23T19:12:30Z)
• Accurate, Efficient and Scalable Training of Graph Neural Networks [9.569918335816963]
  Graph Neural Networks (GNNs) are powerful deep learning models to generate node embeddings on graphs. It is still challenging to perform training in an efficient and scalable way. We propose a novel parallel training framework that reduces training workload by orders of magnitude compared with state-of-the-art minibatch methods.
  arXiv  Detail & Related papers  (2020-10-05T22:06:23Z)
• LGNN: A Context-aware Line Segment Detector [53.424521592941936]
  We present a novel real-time line segment detection scheme called Line Graph Neural Network (LGNN) Our LGNN employs a deep convolutional neural network (DCNN) for proposing line segment directly, with a graph neural network (GNN) module for reasoning their connectivities. Compared with the state-of-the-art, LGNN achieves near real-time performance without compromising accuracy.
  arXiv  Detail & Related papers  (2020-08-13T13:23:18Z)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.