Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules
Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing (i) reduction of data loading time up to 4.2 times compared with a conventional method and (ii) linear scaling performance for training up to 1024 GPUs on both Summit and Perlmutter.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
- Sponsoring Organization:
- USDOE Office of Science (SC); USDOE Laboratory Directed Research and Development (LDRD) Program
- Grant/Contract Number:
- AC05-00OR22725; CSC457; MAT250; AC02-05CH11231; ASCR-ERCAP-m4133
- OSTI ID:
- 1893210
- Alternate ID(s):
- OSTI ID: 1901638
- Journal Information:
- Journal of Cheminformatics, Journal Name: Journal of Cheminformatics Vol. 14 Journal Issue: 1; ISSN 1758-2946
- Publisher:
- Springer Science + Business MediaCopyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
Similar Records
DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets
MARBLE: A Multi-GPU Aware Job Scheduler for Deep Learning on HPC Systems