LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs

Abstract:Equivariant machine learning interatomic potentials (MLIPs) promise atomistic simulations with accuracy approaching that of quantum-mechanical calculations. However, the high computational cost of their core Clebsch--Gordan tensor product (CGTP) convolutions severely limits model scalability and efficiency. In this paper, we rethink the parallel execution of CGTP convolutions and introduce LunaTP. Built on node-centric execution, LunaTP exploits path-group parallelism and multi-level reuse to accelerate CGTP convolutions and higher-order derivative computation while preserving the underlying mathematical formulation. On an NVIDIA H100 GPU, LunaTP achieves kernel speedups of up to 250.0$\times$ over e3nn and 17.1$\times$ over OpenEquivariance. It also delivers end-to-end training throughput gains of up to 28.7$\times$ over e3nn and 3.11$\times$ over OpenEquivariance. Finally, LunaTP demonstrates scalability and robust performance across models, workload scales, and GPUs, delivering substantial acceleration for training and atomistic simulation with high-accuracy equivariant MLIPs.

Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)

arXiv:2610.18393 [cs.DC]

(or arXiv:2610.18393v1 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2610.18393 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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