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arXiv · 2610.18393

LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs

Yuanchang Zhou·Mingzhen Li·Chen Wang·Xiangyu Zhang·Guangming Tan·Weile Jia
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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.

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Yuanchang Zhou, Mingzhen Li, Chen Wang, Xiangyu Zhang, Guangming Tan, Weile Jia. 2026-10-13. LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs. https://arxiv.org/abs/2610.18393

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