LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs
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$\t...
| Authors | Yuanchang Zhou, Mingzhen Li, Chen Wang et al. |
|---|---|
| Primary category | cs.DC |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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