Graph neural networks (GNNs) are machine learning models specialized for
graph data and widely used in many applications. To train GNNs on large graphs
that exceed CPU memory, several systems store data on disk and conduct
out-of-core processing. However, these systems suffer from either read
amplification when reading node features that are usually smaller than a disk
page or degraded model accuracy by treating the graph as disconnected
partitions. To close this gap, we build a system called DiskGNN, which achieves
high I/O efficiency and thus fast training without hurting model accuracy. The
key technique used by DiskGNN is offline sampling, which helps decouple graph
sampling from model computation. In particular, by conducting graph sampling
beforehand, DiskGNN acquires the node features that will be accessed by model
computation, and such information is utilized to pack the target node features
contiguously on disk to avoid read amplification. Besides, \name{} also adopts
designs including four-level feature store to fully utilize the memory
hierarchy to cache node features and reduce disk access, batched packing to
accelerate the feature packing process, and pipelined training to overlap disk
access with other operations. We compare DiskGNN with Ginex and MariusGNN,
which are state-of-the-art systems for out-of-core GNN training. The results
show that DiskGNN can speed up the baselines by over 8x while matching their
best model accuracy.

研究使用离线采样的 DiskGNN 系统，在训练大规模图神经网络时达到高的 I/O 效率和快速训练，同时保持模型的准确性。与其他现有系统相比，DiskGNN 能够将性能提高 8 倍以上，并且具有相同的模型精确度。

DiskGNN：为离核 GNN 训练提供 I/O 效率和模型准确性的桥接

DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN  Training

Exemplar-based face sketch synthesis plays an important role in both digital
entertainment and law enforcement. It generally consists of two parts: neighbor
selection and reconstruction weight representation. The most time-consuming or
main computation complexity for exemplar-based face sketch synthesis methods
lies in the neighbor selection process. State-of-the-art face sketch synthesis
methods perform neighbor selection online in a data-driven manner by $K$
nearest neighbor ($K$-NN) searching. Actually, the online search increases the
time consuming for synthesis. Moreover, since these methods need to traverse
the whole training dataset for neighbor selection, the computational complexity
increases with the scale of the training database and hence these methods have
limited scalability. In this paper, we proposed a simple but effective offline
random sampling in place of online $K$-NN search to improve the synthesis
efficiency. Extensive experiments on public face sketch databases demonstrate
the superiority of the proposed method in comparison to state-of-the-art
methods, in terms of both synthesis quality and time consumption. The proposed
method could be extended to other heterogeneous face image transformation
problems such as face hallucination. We release the source codes of our
proposed methods and the evaluation metrics for future study online:
this http URL

本文提出了一种基于离线采样的示例化面部素描合成方法，以提高合成效率并减少计算复杂度。经过在公共面部素描数据库上进行广泛实验，证明该方法在合成质量和时间消耗方面优于现有方法。该方法可以扩展到其他异构面部图像转换问题，例如面部幻觉。