IPDPS'23 - GraphTensor: Comprehensive GNN-Acceleration Framework for Efficient Parallel Processing смотреть онлайн

GraphTensor: Comprehensive GNN-Acceleration Framework for Efficient Parallel Processing of Massive Datasets

Junhyeok Jang, Miryeong Kwon, Donghyun Gouk, Hanyeoreum Bae, and Myoungsoo Jung KAIST

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 1.4× 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 2.4×, on average