图形神经网络（GNN）已被证明是分析非欧国人图数据的强大工具。但是，缺乏有效的分布图学习（GL）系统极大地阻碍了GNN的应用，尤其是当图形大且GNN相对深时。本文中，我们提出了GraphTheta，这是一种以顶点为中心的图形编程模型实现的新颖分布式和可扩展的GL系统。 GraphTheta是第一个基于分布式图处理的GL系统，其神经网络运算符以用户定义的功能实现。该系统支持多种培训策略，并在分布式（虚拟）机器上启用高度可扩展的大图学习。为了促进图形卷积实现，GraphTheta提出了一个名为NN-Tgar的新的GL抽象，以弥合图形处理和图形深度学习之间的差距。提出了分布式图引擎，以通过混合平行执行进行随机梯度下降优化。此外，除了全球批次和迷你批次外，我们还为新的集群批次培训策略提供了支持。我们使用许多网络大小的数据集评估GraphTheta，范围从小，适度到大规模。实验结果表明，GraphTheta可以很好地扩展到1,024名工人，用于培训内部开发的GNN，该工业尺度的Aripay数据集为14亿个节点和41亿个属性边缘，并带有CPU虚拟机（Dockers）群的小群。 （5 $ \ sim $ 12GB）。此外，GraphTheta比最先进的GNN实现获得了可比或更好的预测结果，证明其学习GNN和现有框架的能力，并且可以超过多达$ 2.02 \ tims $ $ 2.02 \ times $，具有更好的可扩展性。据我们所知，这项工作介绍了文献中最大的边缘属性GNN学习任务。

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training which distributes the workload of training across multiple computing nodes. However, the workflows, computational patterns, communication patterns, and optimization techniques of distributed GNN training remain preliminarily understood. In this paper, we provide a comprehensive survey of distributed GNN training by investigating various optimization techniques used in distributed GNN training. First, distributed GNN training is classified into several categories according to their workflows. In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work are introduced. Second, the software frameworks and hardware platforms of distributed GNN training are also introduced for a deeper understanding. Third, distributed GNN training is compared with distributed training of deep neural networks, emphasizing the uniqueness of distributed GNN training. Finally, interesting issues and opportunities in this field are discussed.

图形神经网络（GNNS）将深度神经网络（DNN）的成功扩展到非欧几里德图数据，实现了各种任务的接地性能，例如节点分类和图形属性预测。尽管如此，现有系统效率低，培训数十亿节点和GPU的节点和边缘训练大图。主要瓶颈是准备GPU数据的过程 - 子图采样和特征检索。本文提出了一个分布式GNN培训系统的BGL，旨在解决一些关键思想的瓶颈。首先，我们提出了一种动态缓存引擎，以最小化特征检索流量。通过协同设计缓存政策和抽样顺序，我们发现低开销和高缓存命中率的精美斑点。其次，我们改善了曲线图分区算法，以减少子图采样期间的交叉分区通信。最后，仔细资源隔离减少了不同数据预处理阶段之间的争用。关于各种GNN模型和大图数据集的广泛实验表明，BGL平均明显优于现有的GNN训练系统20.68倍。

开发用于训练图形的可扩展解决方案，用于链路预测任务的Neural网络（GNNS）由于具有高计算成本和巨大内存占用的高数据依赖性，因此由于高数据依赖性而具有挑战性。我们提出了一种新的方法，用于缩放知识图形嵌入模型的培训，以满足这些挑战。为此，我们提出了以下算法策略：自给自足的分区，基于约束的负采样和边缘迷你批量培训。两者都是分区策略和基于约束的负面采样，避免在训练期间交叉分区数据传输。在我们的实验评估中，我们表明，我们基于GNN的知识图形嵌入模型的缩放解决方案在基准数据集中实现了16倍的加速，同时将可比的模型性能作为标准度量的非分布式方法。

Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large data sizes of graphs and their vertex features make scalable training algorithms and distributed memory systems necessary. Since the convolution operation on graphs induces irregular memory access patterns, designing a memory- and communication-efficient parallel algorithm for GCN training poses unique challenges. We propose a highly parallel training algorithm that scales to large processor counts. In our solution, the large adjacency and vertex-feature matrices are partitioned among processors. We exploit the vertex-partitioning of the graph to use non-blocking point-to-point communication operations between processors for better scalability. To further minimize the parallelization overheads, we introduce a sparse matrix partitioning scheme based on a hypergraph partitioning model for full-batch training. We also propose a novel stochastic hypergraph model to encode the expected communication volume in mini-batch training. We show the merits of the hypergraph model, previously unexplored for GCN training, over the standard graph partitioning model which does not accurately encode the communication costs. Experiments performed on real-world graph datasets demonstrate that the proposed algorithms achieve considerable speedups over alternative solutions. The optimizations achieved on communication costs become even more pronounced at high scalability with many processors. The performance benefits are preserved in deeper GCNs having more layers as well as on billion-scale graphs.

Graph convolutional network (GCN) has been successfully applied to many graph-based applications; however, training a large-scale GCN remains challenging. Current SGD-based algorithms suffer from either a high computational cost that exponentially grows with number of GCN layers, or a large space requirement for keeping the entire graph and the embedding of each node in memory. In this paper, we propose Cluster-GCN, a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. Cluster-GCN works as the following: at each step, it samples a block of nodes that associate with a dense subgraph identified by a graph clustering algorithm, and restricts the neighborhood search within this subgraph. This simple but effective strategy leads to significantly improved memory and computational efficiency while being able to achieve comparable test accuracy with previous algorithms. To test the scalability of our algorithm, we create a new Amazon2M data with 2 million nodes and 61 million edges which is more than 5 times larger than the previous largest publicly available dataset (Reddit). For training a 3-layer GCN on this data, Cluster-GCN is faster than the previous state-of-the-art VR-GCN (1523 seconds vs 1961 seconds) and using much less memory (2.2GB vs 11.2GB). Furthermore, for training 4 layer GCN on this data, our algorithm can finish in around 36 minutes while all the existing GCN training algorithms fail to train due to the out-of-memory issue. Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy-using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99.36 on the PPI dataset, while the previous best result was 98.71 by [16]. Our codes are publicly available at https://github.com/google-research/google-research/ tree/master/cluster_gcn.

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, generalpurpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that Tensor-Flow achieves for several real-world applications.

Graph neural networks (GNNs) have received great attention due to their success in various graph-related learning tasks. Several GNN frameworks have then been developed for fast and easy implementation of GNN models. Despite their popularity, they are not well documented, and their implementations and system performance have not been well understood. In particular, unlike the traditional GNNs that are trained based on the entire graph in a full-batch manner, recent GNNs have been developed with different graph sampling techniques for mini-batch training of GNNs on large graphs. While they improve the scalability, their training times still depend on the implementations in the frameworks as sampling and its associated operations can introduce non-negligible overhead and computational cost. In addition, it is unknown how much the frameworks are 'eco-friendly' from a green computing perspective. In this paper, we provide an in-depth study of two mainstream GNN frameworks along with three state-of-the-art GNNs to analyze their performance in terms of runtime and power/energy consumption. We conduct extensive benchmark experiments at several different levels and present detailed analysis results and observations, which could be helpful for further improvement and optimization.

图形神经网络（GNNS）已成为处理机器学习任务的有效方法，它为构建推荐系统带来了一种新方法，其中可以将推荐任务作为用户 - 项目的链接预测问题提出， 。培训基于GNN的推荐系统（GNNRECSYS）在大图上会引起大型内存足迹，很容易超过典型服务器上的DRAM容量。现有的解决方案诉诸分布式子图培训，这是由于动态构建子图和各个子图的大量冗余的高成本而效率低下。新兴的Intel Optane持久记忆使一台机器以可承受的成本具有最多6 TB的存储器，从而使单机器Gnnrecsys训练可行，从而消除了分布式培训中的效率低下。与DRAM相比，将Optane用于Gnnrecsys的一个主要问题是Optane相对较低的带宽。由于其主要的计算内核稀疏且内存访问密集，因此这种限制可能对Gnnrecsys工作量的高性能特别有害。为了了解Optane是否适合Gnnrecsys培训，我们对Gnnrecsys工作负载进行了深入的表征和全面的基准测试研究。我们的基准测试结果表明，经过正确配置后，基于Optane的单机器GNNRECSYS训练优于大幅度的培训，尤其是在处理深度GNN模型时。我们分析了加速度的来源，提供有关如何为GNNRECSYS工作负载配置Optane的指导，并讨论进一步优化的机会。

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.

我们在大图中介绍了图形神经网络（GNNS）的分布式全批量培训的顺序聚合和换算（SAR）方案。最近，GNN的大规模培训是基于非学习消息传递的基于采样的方法和方法主导的。另一方面，SAR是一种分布式技术，可以直接在整个大图上培训任何GNN类型。 SAR中的关键创新是分布式顺序修补方案，其在后向通过期间依次重新构造，然后在后向通行证期间释放禁止的大型GNN计算图。这导致优异的记忆缩放行为，其中每个工作人员的内存消耗与工人的数量线性地下降，即使对于密集连接的图形。使用SAR，我们报告了最大的全批量GNN培训应用到目前为止，并随着工人数量的增加而展示了大的内存节省。我们还基于内核融合和注意力矩阵的一般技术提出了一种优化了基于关注的模型的运行时和内存效率。我们表明，与SAR相结合，我们的优化注意核导致了基于关注的GNN的显着加速和内存节省。

While many systems have been developed to train Graph Neural Networks (GNNs), efficient model inference and evaluation remain to be addressed. For instance, using the widely adopted node-wise approach, model evaluation can account for up to 94% of the time in the end-to-end training process due to neighbor explosion, which means that a node accesses its multi-hop neighbors. On the other hand, layer-wise inference avoids the neighbor explosion problem by conducting inference layer by layer such that the nodes only need their one-hop neighbors in each layer. However, implementing layer-wise inference requires substantial engineering efforts because users need to manually decompose a GNN model into layers for computation and split workload into batches to fit into device memory. In this paper, we develop Deep Graph Inference (DGI) -- a system for easy and efficient GNN model inference, which automatically translates the training code of a GNN model for layer-wise execution. DGI is general for various GNN models and different kinds of inference requests, and supports out-of-core execution on large graphs that cannot fit in CPU memory. Experimental results show that DGI consistently outperforms layer-wise inference across different datasets and hardware settings, and the speedup can be over 1,000x.

TensorFlow GNN（TF-GNN）是张量曲线的图形神经网络的可扩展库。它是从自下而上设计的，以支持当今信息生态系统中发生的丰富的异质图数据。Google的许多生产模型都使用TF-GNN，最近已作为开源项目发布。在本文中，我们描述了TF-GNN数据模型，其KERAS建模API以及相关功能，例如图形采样，分布式训练和加速器支持。

图形神经网络（GNN）的输入图的大小不断增加，突显了使用多GPU平台的需求。但是，由于计算不平衡和效率较低的通信，现有的多GPU GNN解决方案遭受了劣质性能。为此，我们提出了MGG，这是一种新型的系统设计，可以通过以GPU为中心的软件管道在多GPU平台上加速GNN。 MGG探讨了通过细粒度计算通信管道中隐藏GNN工作负载中远程内存访问延迟的潜力。具体而言，MGG引入了管​​道感知工作负载管理策略和混合数据布局设计，以促进通信局限性重叠。 MGG实现以优化的管道为中心的内核。它包括工作负载交织和基于经经的映射，以进行有效的GPU内核操作管道和专门的内存设计以及优化，以更好地数据访问性能。此外，MGG还结合了轻巧的分析建模和优化启发式方法，以动态提高运行时不同设置的GNN执行性能。全面的实验表明，MGG在各种GNN设置上的最先进的多GPU系统要比最先进的多GPU系统：平均比具有统一虚拟内存设计的多GPU系统快3.65倍，平均比DGCL框架快7.38倍。

嵌入式模型是高维数据的有效学习范例。但是，嵌入模型的一个开放问题是它们的表示（潜在因子）通常会导致大参数空间。我们观察到，现有的分布式训练框架面临嵌入模型的可伸缩性问题，因为从服务器的共享嵌入参数更新和检索共享嵌入参数通常占主导地位培训周期。在本文中，我们提出了一种新的系统框架，可显着提高巨大嵌入模型培训的可扩展性。我们拥抱嵌入的嵌入式作为绩效机会的倾斜流行分布，并利用它来解决具有嵌入缓存的通信瓶颈。为确保缓存跨越一致性，我们将新的一致性模型纳入HET设计，该模型提供了在每嵌入的基础上提供细粒度的一致性保证。与以前的工作相比，只允许读取操作的僵化，HET也利用了写入操作的血液性。六种代表性任务的评估表明，在最先进的基线上，HET达到高达88％的嵌入通信减少和高达20.68倍的性能加速。

最近提出了基于子图的图表学习（SGRL）来应对规范图神经网络（GNNS）遇到的一些基本挑战，并在许多重要的数据科学应用（例如链接，关系和主题预测）中证明了优势。但是，当前的SGRL方法遇到了可伸缩性问题，因为它们需要为每个培训或测试查询提取子图。扩大规范GNN的最新解决方案可能不适用于SGRL。在这里，我们通过共同设计学习算法及其系统支持，为可扩展的SGRL提出了一种新颖的框架Surel。 Surel采用基于步行的子图表分解，并将步行重新形成子图，从而大大降低了子图提取的冗余并支持并行计算。具有数百万个节点和边缘的六个同质，异质和高阶图的实验证明了Surel的有效性和可扩展性。特别是，与SGRL基线相比，Surel可以实现10 $ \ times $ Quad-Up，具有可比甚至更好的预测性能；与规范GNN相比，Surel可实现50％的预测准确性。

图形神经网络（GNN）在学习强大的节点表示中显示了令人信服的性能，这些表现在保留节点属性和图形结构信息的强大节点表示中。然而，许多GNNS在设计有更深的网络结构或手柄大小的图形时遇到有效性和效率的问题。已经提出了几种采样算法来改善和加速GNN的培训，但他们忽略了解GNN性能增益的来源。图表数据中的信息的测量可以帮助采样算法来保持高价值信息，同时消除冗余信息甚至噪声。在本文中，我们提出了一种用于GNN的公制引导（MEGUIDE）子图学习框架。 MEGUIDE采用两种新颖的度量：功能平滑和连接失效距离，以指导子图采样和迷你批次的培训。功能平滑度专为分析节点的特征而才能保留最有价值的信息，而连接失败距离可以测量结构信息以控制子图的大小。我们展示了MEGUIDE在多个数据集上培训各种GNN的有效性和效率。

基于深度学习的模型占主导地位的生产推荐系统的当前景观。此外，近年来目睹了模型规模的指数增长 - 从谷歌的2016年模型，最新的Facebook的型号有10亿个参数，具有12万亿参数。型号容量的每次跳跃都有显着的质量增强，这使我们相信100万亿参数的时代即将来临。然而，即使在工业规模数据中心内，这些模型的培训也在挑战。这种困难是从训练计算的惊人的异质性继承 - 模型的嵌入层可以包括总模型尺寸的99.99％，这是极其内存密集的;虽然其余的神经网络越来越多地计算密集型。为支持培训此类巨大模式，迫切需要有效的分布式培训系统。在本文中，我们通过仔细共同设计优化算法和分布式系统架构来解决这一挑战。具体而言，为了确保培训效率和训练精度，我们设计一种新型混合训练算法，其中嵌入层和密集的神经网络由不同的同步机制处理;然后，我们构建一个名为Persia的系统（短暂的并行推荐培训系统，其中包含混合加速），以支持这种混合培训算法。理论上的示范和实证研究均达到100万亿参数，以证明了波斯的系统设计和实施。我们将Pensia公开使用（在https://github.com/persiamml/persia），以便任何人都能够以100万亿参数的规模轻松培训推荐模型。

Machine Unerning是在收到删除请求时从机器学习（ML）模型中删除某些培训数据的影响的过程。虽然直接而合法，但从划痕中重新训练ML模型会导致高计算开销。为了解决这个问题，在图像和文本数据的域中提出了许多近似算法，其中SISA是最新的解决方案。它将训练集随机分配到多个碎片中，并为每个碎片训练一个组成模型。但是，将SISA直接应用于图形数据可能会严重损害图形结构信息，从而导致的ML模型实用程序。在本文中，我们提出了Grapheraser，这是一种针对图形数据量身定制的新型机器学习框架。它的贡献包括两种新型的图形分区算法和一种基于学习的聚合方法。我们在五个现实世界图数据集上进行了广泛的实验，以说明Grapheraser的学习效率和模型实用程序。它可以实现2.06 $ \ times $（小数据集）至35.94 $ \ times $（大数据集）未学习时间的改进。另一方面，Grapheraser的实现最高62.5美元\％$更高的F1分数，我们提出的基于学习的聚合方法可达到高达$ 112 \％$ $ F1分数。 github.com/minchen00/graph-unlearning}。}。}

最近，Graph神经网络（GNNS）已成为聚光灯作为强大的工具，可以有效地在图形结构化数据上执行各种推理任务。随着现实图表的大小继续扩展，GNN训练系统面临可扩展性挑战。分布式培训是一种流行的方法，可以通过扩展CPU节点来应对这一挑战。但是，对基于磁盘的GNN培训的关注不多，该培训可以通过利用NVME SSD等高性能存储设备来以更具成本效益的方式扩展单节点系统。我们观察到，主内存和磁盘之间的数据移动是基于SSD的训练系统中的主要瓶颈，并且常规的GNN训练管道是不错的选择，而无需考虑此开销。因此，我们提出了Ginex，这是第一个基于SSD的GNN训练系统，可以在单台计算机上处​​理数十亿个图形数据集。受到编译器优化的检查员执行模型的启发，Ginex通过分开样品和收集阶段来重组GNN训练管道。这种分离使Ginex能够实现一种可证明的最佳替换算法，即被称为Belady的算法，用于存储器中的Caching特征向量，该算法是I/O访问的主要部分。根据我们对40亿尺度图数据集的评估，Ginex平均比SSD扩展的Pytorch几何得出了2.11倍的训练吞吐量（最大最高2.67倍）。