Strategies for Molecular Dynamics using Hybrid Systems: LAMMPS Use Case
2026-06-01 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster Computing
AI summaryⓘ
The authors studied how well the LAMMPS software runs biomolecular simulations, focusing on a small protein called Tritrpticin. They tested two ways of running the program on supercomputers: one using just MPI and another combining MPI with OpenMP. They found that using MPI alone worked great on one computer node but slowed down when using many nodes due to communication delays. The combined MPI+OpenMP method worked better when using many nodes by reducing communication time and using memory more efficiently. Their study shows that mixing these techniques is better for big biomolecular simulations on modern computer systems.
LAMMPScoarse-grained modelingmolecular dynamicsMPIOpenMPparallel efficiencyHPCTritrpticinNUMAscalability
Authors
Paulo Henrique Leme Ramalho, Dennis Alves Pedersen, Fábio Andrijauskas
Abstract
The complexity of biomolecular simulations has substantially increased the demand for High-Performance Computing (HPC) infrastructures, particularly in molecular dynamics and coarse-grained modeling. This work presents a systematic performance and scalability analysis of the LAMMPS simulator for coarse-grained biomolecular simulations, using the antimicrobial peptide Tritrpticin (PDB ID: 1D6X) as the experimental workload. Pure MPI and hybrid MPI+OpenMP executions were evaluated in HPC environments comprising up to 8 compute nodes and 1024 simultaneous cores. Metrics of execution time, speedup, parallel efficiency, statistical variability, and internal time decomposition were investigated. Results showed that pure MPI executions deliver excellent performance in single-node environments but suffer scalability degradation in multi-node executions due to communication overhead and inter-process synchronization. Hybrid MPI+OpenMP configurations proved more efficient at large scale, reducing communication costs and better exploiting the NUMA memory hierarchy. The computational breakdown revealed that communication and electrostatic interaction routines accounted for the largest fraction of execution time at the largest pure-MPI scales. These results reinforce that performance of biomolecular HPC applications depends directly on the balance among parallelization granularity, spatial decomposition, and distributed communication costs. Hybrid MPI+OpenMP strategies represent a more sustainable alternative for coarse-grained biomolecular simulations on modern many-core architectures.