  This paper introduces a random-batch molecular dynamics (RBMD) package for
fast simulations of particle systems at the nano/micro scale. Different from
existing packages, the RBMD uses random batch methods for nonbonded
interactions of particle systems. The long-range part of Coulomb interactions
is calculated in Fourier space by the random batch Ewald algorithm, which
achieves linear complexity and superscalability, surpassing classical
lattice-based Ewald methods. For the short-range part, the random batch list
algorithm is used to construct neighbor lists, significantly reducing both
computational and memory costs. The RBMD is implemented on GPU-CPU
heterogeneous architectures, with classical force fields for all-atom systems.
Benchmark systems are used to validate accuracy and performance of the package.
Comparison with the particle-particle particle-mesh method and the Verlet list
method in the LAMMPS package is performed on three different NVIDIA GPUs,
demonstrating high efficiency of the RBMD on heterogeneous architectures. Our
results also show that the RBMD enables simulations on a single GPU with a CPU
core up to 10 million particles. Typically, for systems of one million
particles, the RBMD allows simulating all-atom systems with a high efficiency
of 8.20 ms per step, demonstrating the attractive feature for running
large-scale simulations of practical applications on a desktop machine.
