TenGraph: A Tensor-Based Graph Query Engine

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Abstract

We propose a novel tensor-based approach to in-memory graph query processing. Tensors are multi-dimensional arrays, and have been utilized as data units in deep learning frameworks such as TensorFlow and PyTorch. Through tensors, these frameworks en- capsulate optimized hardware-dependent code for automatic performance improvement on modern processors. Inspired by this practice, we explore how to utilize tensors to e￿ciently process graph queries. Speci￿cally, we design a succinct storage format for tensors to represent graph topology e￿ectively and compose graph query operations using tensor computation on batches of vertices. We have developed TenGraph, our PyTorch-based prototype, and evaluated it on graph query benchmark workloads in comparison with a variety of CPU- and GPU-based systems. Our experimental results show that TenGraph not only achieves a speedup of 50-100 times on the GPU over the CPU but also outperforms the other CPU- and GPU-based systems signi￿cantly.