🤖 AI Summary
Molecular dynamics simulations are inherently serial, making it challenging to improve single-system throughput via parallel computation. This work proposes Langevin Speculative Dynamics (LSD), the first method to extend speculative sampling to second-order Langevin dynamics. LSD introduces a model-agnostic, distributed speculation mechanism that leverages a fast draft model to propose steps, which are then validated in parallel by a slower target model. By incorporating an inter-distribution transport map, the approach ensures unbiased acceleration while strictly preserving the target distribution across diverse systems and model pairings. Experimental results demonstrate speedups of 3–9×, offering both broad applicability and rigorous theoretical guarantees.
📝 Abstract
Molecular dynamics (MD) is a key tool for simulating the dynamical behavior of atomic systems. However, MD is inherently serial, which makes it difficult to increase single-system throughput with concurrent compute. To address this, we introduce Langevin Speculative Dynamics (LSD), a distributed and model-agnostic speculative sampler for accelerating MD without adding relative error. Inspired by speculative methods in language and diffusion modeling, LSD uses a draft model to propose fast simulation steps and verifies them in parallel with a slower target model, applying a transport map from the draft to the target distribution. We extend speculative sampling to second-order Langevin dynamics, derive the achievable speedup as a function of physical parameters, show that LSD generalizes across different systems and draft-target combinations with a 3-9x speedup, and confirm theoretically and empirically that LSD samples trajectories from its target model distribution.