We present GraphTensor, a comprehensive open-source framework that supports efficient parallel neural network processing on large graphs. GraphTensor offers a set of easy-to-use programming primitives that appreciate both graph and neural network execution behaviors from the beginning (graph sampling) to the end (dense data processing). Our framework runs diverse graph neural network (GNN) models in a destination-centric, feature-wise manner, which can significantly shorten training execution times in a GPU. In addition, GraphTensor rearranges multiple GNN kernels based on their system hyperparameters in a self-governing manner, thereby reducing the processing dimensionality and the latencies further. From the end-to-end execution viewpoint, GraphTensor significantly shortens the service-level GNN latency by applying pipeline parallelism for efficient graph dataset preprocessing. Our evaluation shows that GraphTensor exhibits 1.4x better training performance than emerging GNN frameworks under the execution of large-scale, real-world graph workloads. For the end-to-end services, GraphTensor reduces training latencies of an advanced version of the GNN frameworks (optimized for multi-threaded graph sampling) by 2.4x, on average.

翻译：我们提出GraphTensor，一个支持大规模图高效并行神经网络处理的全面开源框架。GraphTensor提供一组易用的编程原语，这些原语从起点（图采样）到终点（稠密数据处理）同时兼顾图与神经网络的执行行为。该框架以目标中心、特征关联的方式运行多种图神经网络（GNN）模型，可显著缩短GPU中的训练执行时间。此外，GraphTensor基于系统超参数自主重组多个GNN内核，从而进一步降低处理维度与延迟。从端到端执行视角看，GraphTensor通过应用流水线并行性实现高效图数据集预处理，显著缩短服务级GNN延迟。实验评估表明，在执行大规模真实图工作负载时，GraphTensor相比新兴GNN框架展现出1.4倍的训练性能提升。针对端到端服务，GraphTensor将优化后的多线程图采样版GNN框架的训练延迟平均降低2.4倍。