  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.
