We present an implementation of a general purpose GPU-Molecular Dynamics code named LAMMPScuda which is based on LAMMPS. It exhibits excellent scaling behavior, allowing for the efficient usage of hundreds of GPUs for a single simulation . At the same time each GPU provides the equivalent performance of approximately 5 modern Quad Core CPUs. By supporting a wide array of force fields LAMMPScuda allows to model many different condensed matter systems including URLanic glasses, polymer systems and molecular fluids. In the paper implementation details are presented as well as performance measurements with a number of different condensed systems .
We present a GPU implementation of LAMMPS, a widely-used parallel molecular dynamics (MD) software package, and show 5x to 13x single node speedups versus the CPU-only version of LAMMPS. This new CUDA package for LAMMPS also enables multi-GPU simulation on hybrid heterogeneous clusters, using MPI for inter-node communication, CUDA kernels on the GPU for all methods working with particle data, and standard LAMMPS C++ code for CPU execution. Cell and neighbor list approaches are compared for best performance on GPUs, with thread-per-atom and block-per-atom neighbor list variants showing best performance at low and high neighbor counts, respectively. Computational performance results of GPU-enabled LAMMPS are presented for a variety of materials classes (e.g. biomolecules, polymers, metals, semiconductors), along with a speed comparison versus other available GPU-enabled MD software. Finally, we show strong and weak scaling performance on a CPU/GPU cluster using up to 128 dual GPU nodes .
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an implementation of a general purpose GPU-Molecular Dynamics code named LAMMPScuda which is based on LAMMPS. It exhibits excellent scaling behavior, allowing for the efficient usage of hundreds of GPUs for a single simulation . At the same time each GPU provides the equivalent performance of approximately 5 modern Quad Core CPUs. By supporting a wide array of force fields LAMMPScuda allows to model many different condensed matter systems including URLanic glasses, polymer systems and molecular fluids. In the paper implementation details are presented as well as performance measurements with
a GPU implementation of LAMMPS,
R
number of different condensed systems
widely-used parallel molecular dynamics (MD) software package, and show 5x to 13x single node speedups versus the CPU-only version of LAMMPS. This new CUDA package for LAMMPS also enables multi-GPU simulation on hybrid heterogeneous clusters, using MPI for inter-node communication, CUDA kernels on the GPU for all methods working with particle data, and standard LAMMPS C++ code for CPU execution. Cell and neighbor list approaches are compared for best performance on GPUs, with thread-per-atom and block-per-atom neighbor list variants showing best performance at low and high neighbor counts, respectively. Computational performance results of GPU-enabled LAMMPS are presented for a variety of materials classes (e.g. biomolecules, polymers, metals, semiconductors), along with a speed comparison versus other available GPU-enabled MD software. Finally, we show strong and weak scaling performance on a CPU/GPU cluster using up to 128 dual GPU nodes