Presentation
TENSORMD: Accelerating Molecular Dynamics with a High-Performance Machine Learning Interatomic Potential
DescriptionAI has been integrated into HPC across various scientific fields, significantly enhancing performance. In molecular dynamics simulations, HPC+AI facilitates the investigation of atomic-scale physical properties using machine-learning interatomic potentials (MLIPs). However, general-purpose ML tools (e.g., TensorFlow) used in MLIPs are not optimally matched, leading to missed optimization opportunities due to the higher computational complexity and greater diversity of HPC+AI applications compared to pure AI scenarios. To address this, we introduce TENSORMD, an MLIP independent of existing ML tools, enabling flexible optimizations that standard ML frameworks cannot support. TENSORMD outperforms a state-of-the-art MLIP—winner of the 2020 Gordon Bell Prize and built on an ML tool—by 1.88x on NVIDIA A100 GPU. Additionally, TENSORMD was evaluated on two supercomputers with different architectures, achieving significantly reduced time-to-solution and supporting molecular dynamics simulations at scales beyond 50 billion atoms.
Event Type
Paper
TimeThursday, 20 November 202510:30am - 10:52am CST
Location263-264
Applications