Distributed Matrix-Based Sampling for Graph Neural Network Training

• URL: http://arxiv.org/abs/2311.02909v3
• Date: Fri, 19 Apr 2024 08:46:31 GMT
• Title: Distributed Matrix-Based Sampling for Graph Neural Network Training
• Authors: Alok Tripathy, Katherine Yelick, Aydin Buluc,
• Abstract summary: We propose a matrix-based bulk sampling approach that expresses sampling as a sparse matrix multiplication (SpGEMM) and samples multiple minibatches at once. When the input graph topology does not fit on a single device, our method distributes the graph and use communication-avoiding SpGEMM algorithms to scale GNN minibatch sampling. In addition to new methods for sampling, we introduce a pipeline that uses our matrix-based bulk sampling approach to provide end-to-end training results.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs) offer a compact and computationally efficient way to learn embeddings and classifications on graph data. GNN models are frequently large, making distributed minibatch training necessary. The primary contribution of this paper is new methods for reducing communication in the sampling step for distributed GNN training. Here, we propose a matrix-based bulk sampling approach that expresses sampling as a sparse matrix multiplication (SpGEMM) and samples multiple minibatches at once. When the input graph topology does not fit on a single device, our method distributes the graph and use communication-avoiding SpGEMM algorithms to scale GNN minibatch sampling, enabling GNN training on much larger graphs than those that can fit into a single device memory. When the input graph topology (but not the embeddings) fits in the memory of one GPU, our approach (1) performs sampling without communication, (2) amortizes the overheads of sampling a minibatch, and (3) can represent multiple sampling algorithms by simply using different matrix constructions. In addition to new methods for sampling, we introduce a pipeline that uses our matrix-based bulk sampling approach to provide end-to-end training results. We provide experimental results on the largest Open Graph Benchmark (OGB) datasets on $128$ GPUs, and show that our pipeline is $2.5\times$ faster than Quiver (a distributed extension to PyTorch-Geometric) on a $3$-layer GraphSAGE network. On datasets outside of OGB, we show a $8.46\times$ speedup on $128$ GPUs in per-epoch time. Finally, we show scaling when the graph is distributed across GPUs and scaling for both node-wise and layer-wise sampling algorithms.

Related papers

• Efficient Heterogeneous Graph Learning via Random Projection [58.4138636866903]
  Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on heterogeneous graphs. Recent pre-computation-based HGNNs use one-time message passing to transform a heterogeneous graph into regular-shaped tensors. We propose a hybrid pre-computation-based HGNN, named Random Projection Heterogeneous Graph Neural Network (RpHGNN)
  arXiv  Detail & Related papers  (2023-10-23T01:25:44Z)
• A Local Graph Limits Perspective on Sampling-Based GNNs [7.601210044390688]
  We propose a theoretical framework for training Graph Neural Networks (GNNs) on large input graphs via training on small, fixed-size sampled subgraphs. We prove that parameters learned from training sampling-based GNNs on small samples of a large input graph are within an $epsilon$-neighborhood of the outcome of training the same architecture on the whole graph.
  arXiv  Detail & Related papers  (2023-10-17T02:58:49Z)
• BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing [0.0]
  Graph neural networks (GNNs) have extended the success of deep neural networks (DNNs) to non-Euclidean graph data. Existing systems are inefficient to train large graphs with billions of nodes and edges with GPUs. This paper proposes BGL, a distributed GNN training system designed to address the bottlenecks with a few key ideas.
  arXiv  Detail & Related papers  (2021-12-16T00:37:37Z)
• VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization [70.8567058758375]
  VQ-GNN is a universal framework to scale up any convolution-based GNNs using Vector Quantization (VQ) without compromising the performance. Our framework avoids the "neighbor explosion" problem of GNNs using quantized representations combined with a low-rank version of the graph convolution matrix.
  arXiv  Detail & Related papers  (2021-10-27T11:48:50Z)
• Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining [58.10436813430554]
  Mini-batch training of graph neural networks (GNNs) requires a lot of computation and data movement. We argue in favor of performing mini-batch training with neighborhood sampling in a distributed multi-GPU environment. We present a sequence of improvements to mitigate these bottlenecks, including a performance-engineered neighborhood sampler. We also conduct an empirical analysis that supports the use of sampling for inference, showing that test accuracies are not materially compromised.
  arXiv  Detail & Related papers  (2021-10-16T02:41:35Z)
• A Unified Lottery Ticket Hypothesis for Graph Neural Networks [82.31087406264437]
  We present a unified GNN sparsification (UGS) framework that simultaneously prunes the graph adjacency matrix and the model weights. We further generalize the popular lottery ticket hypothesis to GNNs for the first time, by defining a graph lottery ticket (GLT) as a pair of core sub-dataset and sparse sub-network.
  arXiv  Detail & Related papers  (2021-02-12T21:52:43Z)
• Scalable Graph Neural Networks via Bidirectional Propagation [89.70835710988395]
  Graph Neural Networks (GNN) is an emerging field for learning on non-Euclidean data. This paper presents GBP, a scalable GNN that utilizes a localized bidirectional propagation process from both the feature vectors and the training/testing nodes. An empirical study demonstrates that GBP achieves state-of-the-art performance with significantly less training/testing time.
  arXiv  Detail & Related papers  (2020-10-29T08:55:33Z)
• 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)
• Accelerating Graph Sampling for Graph Machine Learning using GPUs [2.9383911860380127]
  NextDoor is a system designed to perform graph sampling on GPU resources. NextDoor employs a new approach to graph sampling that we call transit-parallelism. We implement several graph sampling applications, and show that NextDoor runs them orders of magnitude faster than existing systems.
  arXiv  Detail & Related papers  (2020-09-14T19:03:33Z)
• Scaling Graph Neural Networks with Approximate PageRank [64.92311737049054]
  We present the PPRGo model which utilizes an efficient approximation of information diffusion in GNNs. In addition to being faster, PPRGo is inherently scalable, and can be trivially parallelized for large datasets like those found in industry settings. We show that training PPRGo and predicting labels for all nodes in this graph takes under 2 minutes on a single machine, far outpacing other baselines on the same graph.
  arXiv  Detail & Related papers  (2020-07-03T09:30:07Z)

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.