DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Large-scale atomistic simulations are essential to bridge computational materials and chemistry to realistic materials and drug discovery applications. In the past few years, rapid developments of machine learning interatomic potentials (MLIPs) have offered a solution to scale up quantum mechanical calculations. Parallelizing these interatomic potentials across multiple devices poses a challenging, but promising approach to further extending simulation scales to real-world applications. In this work, we present DistMLIP, an efficient distributed inference platform for MLIPs based on zero-redundancy, graph-level parallelization. In contrast to conventional space-partitioning parallelization, DistMLIP enables efficient MLIP parallelization through graph partitioning, allowing multi-device inference on flexible MLIP model architectures like multi-layer graph neural networks. DistMLIP presents an easy-to-use, flexible, plug-in interface that enables distributed inference of pre-existing MLIPs. We demonstrate DistMLIP on four widely used and state-of-the-art MLIPs: CHGNet, MACE, TensorNet, and eSEN. We show that existing foundational potentials can perform near-million-atom calculations at the scale of a few seconds on 8 GPUs with DistMLIP.
Code (1)
Tasks
Drug Discoverygraph partitioningSimilar Papers 제목 키워드 기반
High-performance training and inference for deep equivariant interatomic potentials
Machine learning interatomic potentials, particularly those based on deep equivariant neural networks, have demonstrated state-of-the-art accuracy and computational efficiency in atomistic modeling tasks like molecular d…
Computational EfficiencyEasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition
We present EasyASR, a distributed machine learning platform for training and serving large-scale Automatic Speech Recognition (ASR) models, as well as collecting and processing audio data at scale. Our platform is built …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)BIG-bench Machine LearningGPU+2Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials
Universal Machine Learning Interatomic Potentials (uMLIPs), pre-trained on massively diverse datasets encompassing inorganic materials and organic molecules across the entire periodic table, serve as foundational models …
Physical SimulationsSmooth Dynamic Cutoffs for Machine Learning Interatomic Potentials
Machine learning interatomic potentials (MLIPs) have proven to be wildly useful for molecular dynamics simulations, powering countless drug and materials discovery applications. However, MLIPs face two primary bottleneck…
BLIPs: Bayesian Learned Interatomic Potentials
Machine Learning Interatomic Potentials (MLIPs) are becoming a central tool in simulation-based chemistry. However, like most deep learning models, MLIPs struggle to make accurate predictions on out-of-distribution data …
Active Learning