Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry

TL;DR

This paper presents a hardware-software co-design approach that significantly accelerates graph neural network training for molecular property prediction, reducing training time from days to under two hours.

Contribution

It introduces a novel batching algorithm and implementation on Graphcore IPUs, enabling efficient training of GNNs on large molecular datasets.

Findings

Training time reduced from days to under two hours

Improved throughput by eliminating redundant computation

Effective scaling across diverse molecular graph datasets

Abstract

Molecular property calculations are the bedrock of chemical physics. High-fidelity \textit{ab initio} modeling techniques for computing the molecular properties can be prohibitively expensive, and motivate the development of machine-learning models that make the same predictions more efficiently. Training graph neural networks over large molecular databases introduces unique computational challenges such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs such as social networks. This paper demonstrates a novel hardware-software co-design approach to scale up the training of graph neural networks for molecular property prediction. We introduce an algorithm to coalesce the batches of molecular graphs into fixed size packs to eliminate redundant computation and memory associated with alternative padding techniques and improve throughput via minimizing communication. We demonstrate the effectiveness of our co-design approach by providing an implementation of a well-established molecular property prediction model on the Graphcore Intelligence Processing Units (IPU). We evaluate the training performance on multiple molecular graph databases with varying degrees of graph counts, sizes and sparsity. We demonstrate that such a co-design approach can reduce the training time of such molecular property prediction models from days to less than two hours, opening new possibilities for AI-driven scientific discovery.