DistGNN-MB: Distributed Large-Scale Graph Neural Network Training on x86 via Minibatch Sampling

• URL: http://arxiv.org/abs/2211.06385v1
• Date: Fri, 11 Nov 2022 18:07:33 GMT
• Title: DistGNN-MB: Distributed Large-Scale Graph Neural Network Training on x86 via Minibatch Sampling
• Authors: Md Vasimuddin, Ramanarayan Mohanty, Sanchit Misra, Sasikanth Avancha
• Abstract summary: DistGNN-MB trains GraphSAGE 5.2x faster than the widely-used DistDGL. At this scale, DistGNN-MB trains GraphSAGE and GAT 10x and 17.2x faster, respectively, as compute nodes scale from 2 to 32.
• Score: 3.518762870118332
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training Graph Neural Networks, on graphs containing billions of vertices and edges, at scale using minibatch sampling poses a key challenge: strong-scaling graphs and training examples results in lower compute and higher communication volume and potential performance loss. DistGNN-MB employs a novel Historical Embedding Cache combined with compute-communication overlap to address this challenge. On a 32-node (64-socket) cluster of $3^{rd}$ generation Intel Xeon Scalable Processors with 36 cores per socket, DistGNN-MB trains 3-layer GraphSAGE and GAT models on OGBN-Papers100M to convergence with epoch times of 2 seconds and 4.9 seconds, respectively, on 32 compute nodes. At this scale, DistGNN-MB trains GraphSAGE 5.2x faster than the widely-used DistDGL. DistGNN-MB trains GraphSAGE and GAT 10x and 17.2x faster, respectively, as compute nodes scale from 2 to 32.

Related papers

• Graph Transformers for Large Graphs [57.19338459218758]
  This work advances representation learning on single large-scale graphs with a focus on identifying model characteristics and critical design constraints. A key innovation of this work lies in the creation of a fast neighborhood sampling technique coupled with a local attention mechanism. We report a 3x speedup and 16.8% performance gain on ogbn-products and snap-patents, while we also scale LargeGT on ogbn-100M with a 5.9% performance improvement.
  arXiv  Detail & Related papers  (2023-12-18T11:19:23Z)
• Cached Operator Reordering: A Unified View for Fast GNN Training [24.917363701638607]
  Graph Neural Networks (GNNs) are a powerful tool for handling structured graph data and addressing tasks such as node classification, graph classification, and clustering. However, the sparse nature of GNN computation poses new challenges for performance optimization compared to traditional deep neural networks. We address these challenges by providing a unified view of GNN computation, I/O, and memory.
  arXiv  Detail & Related papers  (2023-08-23T12:27:55Z)
• 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)
• BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs [2.984386665258243]
  BatchGNN is a distributed CPU system that showcases techniques to efficiently train GNNs on terabyte-sized graphs. BatchGNN achieves an average $3times$ speedup over DistDGL on three GNN models trained on OGBN graphs.
  arXiv  Detail & Related papers  (2023-06-23T23:25:34Z)
• Training Graph Neural Networks on Growing Stochastic Graphs [114.75710379125412]
  Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked data. We propose to learn GNNs on very large graphs by leveraging the limit object of a sequence of growing graphs, the graphon.
  arXiv  Detail & Related papers  (2022-10-27T16:00:45Z)
• 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)
• Increase and Conquer: Training Graph Neural Networks on Growing Graphs [116.03137405192356]
  We consider the problem of learning a graphon neural network (WNN) by training GNNs on graphs sampled Bernoulli from the graphon. Inspired by these results, we propose an algorithm to learn GNNs on large-scale graphs that, starting from a moderate number of nodes, successively increases the size of the graph during training.
  arXiv  Detail & Related papers  (2021-06-07T15:05:59Z)
• GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching [2.654276707313136]
  GNNIE is an accelerator designed to run a broad range of Graph Neural Networks (GNNs) It tackles workload imbalance by (i) splitting node feature operands into blocks, (ii) reordering and redistributing computations, and (iii) using a flexible MAC architecture with low communication overheads among the processing elements. GNNIE achieves average speedups of over 8890x over a CPU and 295x over a GPU over multiple datasets on graph attention networks (GATs), graph convolutional networks (GCNs), GraphSAGE, GINConv, and DiffPool.
  arXiv  Detail & Related papers  (2021-05-21T20:07:14Z)
• DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks [58.48833325238537]
  Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible. In this paper, we presentGNN that optimize the well-known Deep Graph Library (DGL) for full-batch training on CPU clusters. Our results on four common GNN benchmark datasets show up to 3.7x speed-up using a single CPU socket and up to 97x speed-up using 128 CPU sockets.
  arXiv  Detail & Related papers  (2021-04-14T08:46:35Z)
• 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)

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.