Jointly utilizing multiple GPUs to train graph neural networks (GNNs) is crucial for handling large graphs and achieving high efficiency. However, we find that existing systems suffer from high communication costs and low GPU utilization due to improper data layout and training procedures. Thus, we propose a system dubbed Distributed Sampling and Pipelining (DSP) for multi-GPU GNN training. DSP adopts a tailored data layout to utilize the fast NVLink connections among the GPUs, which stores the graph topology and popular node features in GPU memory. For efficient graph sampling with multiple GPUs, we introduce a collective sampling primitive (CSP), which pushes the sampling tasks to data to reduce communication. We also design a producer-consumer-based pipeline, which allows tasks from different mini-batches to run congruently to improve GPU utilization. We compare DSP with state-of-the-art GNN training frameworks, and the results show that DSP consistently outperforms the baselines under different datasets, GNN models and GPU counts. The speedup of DSP can be up to 26x and is over 2x in most cases.

DSP: Efficient GNN Training with Multiple GPUs

Graph neural networks (GNNs) are a type of deep learning models that learning over graphs, and have been successfully applied in many domains. Despite the effectiveness of GNNs, it is still challenging for GNNs to efficiently scale to large graphs. As a remedy, distributed computing becomes a promising solution of training large-scale GNNs, since it is able to provide abundant computing resources. However, the dependency of graph structure increases the difficulty of achieving high-efficiency distributed GNN training, which suffers from the massive communication and workload imbalance. In recent years, many efforts have been made on distributed GNN training, and an array of training algorithms and systems have been proposed. Yet, there is a lack of systematic review on the optimization techniques from graph processing to distributed execution. In this survey, we analyze three major challenges in distributed GNN training that are massive feature communication, the loss of model accuracy and workload imbalance. Then we introduce a new taxonomy for the optimization techniques in distributed GNN training that address the above challenges. The new taxonomy classifies existing techniques into four categories that are GNN data partition, GNN batch generation, GNN execution model, and GNN communication protocol.We carefully discuss the techniques in each category. In the end, we summarize existing distributed GNN systems for multi-GPUs, GPU-clusters and CPU-clusters, respectively, and give a discussion about the future direction on scalable GNNs.

pdf/distributed-graph-neural-network-training-a-survey-2l1un2e8.pdf

Distributed Graph Neural Network Training: A Survey

Graph neural networks (GNNs) are a type of deep learning models that are trained on graphs and have been successfully applied in various domains. Despite the effectiveness of GNNs, it is still challenging for GNNs to efficiently scale to large graphs. As a remedy, distributed computing becomes a promising solution of training large-scale GNNs, since it is able to provide abundant computing resources. However, the dependency of graph structure increases the difficulty of achieving high-efficiency distributed GNN training, which suffers from the massive communication and workload imbalance. In recent years, many efforts have been made on distributed GNN training, and an array of training algorithms and systems have been proposed. Yet, there is a lack of systematic review of the optimization techniques for the distributed execution of GNN training. In this survey, we analyze three major challenges in distributed GNN training: massive feature communication, the loss of model accuracy, and workload imbalance. Then, we introduce a new taxonomy for the optimization techniques in distributed GNN training that address the above challenges. The new taxonomy classifies existing techniques into four categories: GNN data partition, GNN batch generation, GNN execution model, and GNN communication protocol. We carefully discuss the techniques in each category. In the conclusion, we summarize existing distributed GNN systems for multi–graphics processing units (GPUs), GPU-clusters and central processing unit (CPU)-clusters, respectively, and present a discussion about the future direction of distributed GNN training. 

Datasets, tasks, and training methods for large-scale hypergraph learning

While many systems have been developed to train graph neural networks (GNNs), efficient model evaluation, which computes node embedding according to a given model, remains to be addressed. For instance, using the widely adopted node-wise approach, model evaluation can account for over 90% of the time in the end-to-end training process due to neighbor explosion, which means that a node accesses its multi-hop neighbors. The layer-wise approach avoids neighbor explosion by conducting computation layer by layer in GNN models. However, layer-wise model evaluation takes considerable implementation efforts because users need to manually decompose the GNN model into layers, and different implementations are required for GNN models with different structures. In this paper, we present DGI -a framework for easy and efficient GNN model evaluation, which automatically translates the training code of a GNN model for layer-wise evaluation to minimize user effort. DGI is general for different GNN models and evaluation requests (e.g., computing embedding for all or some of the nodes), and supports out-of-core execution on large graphs that cannot fit in CPU memory. Under the hood, DGI traces the computation graph of GNN model, partitions the computation graph into layers that are suitable for layer-wise evaluation according to tailored rules, and executes each layer efficiently by reordering the computation tasks and managing device memory consumption. Experiment results show that DGI matches hand-written implementations of layer-wise evaluation in efficiency and consistently outperforms node-wise evaluation across different datasets and hardware settings, and the speedup can be over 1,000x.

DGI: An Easy and Efficient Framework for GNN Model Evaluation

Graph neural networks (GNNs) have shown excellent performance in a wide range of applications such as recommendation, risk control, and drug discovery. With the increase in the volume of graph data, distributed GNN systems become essential to support efficient GNN training. However, existing distributed GNN training systems suffer from various performance issues including high network communication cost, low CPU utilization, and poor end-to-end performance. In this paper, we propose ByteGNN, which addresses the limitations in existing distributed GNN systems with three key designs: (1) an abstraction of mini-batch graph sampling to support high parallelism, (2) a two-level scheduling strategy to improve resource utilization and to reduce the end-to-end GNN training time, and (3) a graph partitioning algorithm tailored for GNN workloads. Our experiments show that ByteGNN outperforms the state-of-the-art distributed GNN systems with up to 3.5-23.8 times faster end-to-end execution, 2-6 times higher CPU utilization, and around half of the network communication cost.

ByteGNN: Efficient Graph Neural Network Training at Large Scale

An out-of-core stencil computation code handles large data whose size is beyond the capacity of GPU memory. Whereas, such an code requires streaming data to and from the GPU frequently. As a result, data movement between the CPU and GPU usually limits the performance. In this work, compression-based optimizations are proposed. First, an on-the-ﬂy compression technique is applied to an out-of-core stencil code, reducing the CPU-GPU memory copy. Secondly, a single working buﬀer technique is used to reduce GPU memory consumption. Experimental results show that the stencil code using the proposed techniques achieved 1.1 × speed and reduced GPU memory consumption by 33.0% on an NVIDIA Tesla V100 GPU.

Compression-Based Optimizations for Out-of-Core GPU Stencil Computation

Graph neural networks (GNNs) have shown excellent performance in a wide range of applications such as recommendation, risk control, and drug discovery. With the increase in the volume of graph data, distributed GNN systems become essential to support efficient GNN training. However, existing distributed GNN training systems suffer from various performance issues including high network communication cost, low CPU utilization, and poor end-to-end performance. In this paper, we propose ByteGNN, which addresses the limitations in existing distributed GNN systems with three key designs: (1) an abstraction of mini-batch graph sampling to support high parallelism, (2) a two-level scheduling strategy to improve resource utilization and to reduce the end-to-end GNN training time, and (3) a graph partitioning algorithm tailored for GNN workloads. Our experiments show that ByteGNN outperforms the state-of-the-art distributed GNN systems with up to 3.5--23.8 times faster end-to-end execution, 2--6 times higher CPU utilization, and around half of the network communication cost.

ByteGNN

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ByteGNN: Efficient Graph Neural Network Training at Large Scale

Compression-Based Optimizations for Out-of-Core GPU Stencil Computation

ByteGNN