GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit

Abstract

Motivation: Molecular simulation has historically been a low-throughput technique, but faster computers and increasing amounts of genomic and structural data are changing this by enabling large-scale automated simulation of, for instance, many conformers or mutants of biomolecules with or without a range of ligands. At the same time, advances in performance and scaling now make it possible to model complex biomolecular interaction and function in a manner directly testable by experiment. These applications share a need for fast and efficient software that can be deployed on massive scale in clusters, web servers, distributed computing or cloud resources.

Results: Here, we present a range of new simulation algorithms and features developed during the past 4 years, leading up to the GROMACS 4.5 software package. The software now automatically handles wide classes of biomolecules, such as proteins, nucleic acids and lipids, and comes with all commonly used force fields for these molecules built-in. GROMACS supports several implicit solvent models, as well as new free-energy algorithms, and the software now uses multithreading for efficient parallelization even on low-end systems, including windows-based workstations. Together with hand-tuned assembly kernels and state-of-the-art parallelization, this provides extremely high performance and cost efficiency for high-throughput as well as massively parallel simulations.

Availability: GROMACS is an open source and free software available from http://www.gromacs.org.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

3D Domain decomposition in real space combined with 2D pencil domain decomposition in reciprocal space. The scaling in previous versions of GROMACS was limited by the reciprocal space PME set-up, and in particular the 1D decomposition of FFTs along the x-axis. The pencil grid decomposition improves reciprocal space scaling considerably and makes it easier to use arbitrary numbers of nodes. Colors in the plot refer to a hypothetical system with four cores per node, where three are used for direct-space and one for reciprocal-space calculations

Fig. 2.

Free-energy calculations using BAR. GROMACS 4.5 provides significantly enhanced tools to automatically create topologies describing decoupling of molecules from the system to calculate binding or hydration free energies. The Hamiltonian of the system is defined as H(λ) = (1−λ)H₀ + λ H₁, where H₀ and H₁ are the Hamiltonians for the two end states. The user specifies a sequence of lambda points and runs simulations where the phase space overlaps and Hamiltonian differences are calculated on the fly. Finally, all these files are provided to the new g_bar tool that automatically analyses the results and provides free energies as well as standard error estimates for the system change

Fig. 3.

Strong scaling of medium-to-large systems. Simulation performance is plotted as a function of number of cores for a series of simulation systems. Performance data were obtained on two clusters: one that is thinly connected using QDR Infiniband but not full bisectional bandwidth and a more expensive Cray XE6 with a Gemini interconnect. In increasing order of molecular size: the ion channel with virtual sites had 129 692 atoms, the ion channel without virtual sites had 141 677 atoms, this virus capsid had 1 091 164 atoms, the vesicle fusion system had 2 511 403 atoms and the methanol system had 7 680 000 atoms. See Supplementary Data for details

Fig. 4.

Efficient and portable parallel execution using POSIX or Windows threads. Performance is plotted as a function of number of cores using the thread\_MPI library and compared with using OpenMPI. Simulations were run on a single node with 24 AMD 8425HE cores running at 2.66 GHz. Performance is nearly identical between the two parallel implementations. Data are plotted for the Villin and POPC bilayer benchmark systems

Fig. 5.

Cost efficiency of GROMACS on small systems. Cost per microsecond is plotted for a series of small systems running on a single eight-core node at a major cloud provider. Simulation details are available in the Supplementary Data. The label on each bar indicates the performance (inversely proportional to cost). The ready availability of cloud compute instances enables extremely cost-efficient high-throughput simulation using individual nodes