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Argonne Leadership Computing Facility

Gromacs on ThetaGPU

What is Gromacs?

GROMACS is a versatile package to perform molecular dynamics, i.e. simulate the Newtonian equations of motion for systems with hundreds to millions of particles. It is primarily designed for biochemical molecules like proteins, lipids, and nucleic acids that have a lot of complicated bonded interactions, but since GROMACS is extremely fast at calculating the nonbonded interactions (that usually dominate simulations) many groups are also using it for research on non-biological systems, e.g. polymers.


ALCF offers assistance with building binaries and compiling instructions for GROMACS. For questions, contact us at

Building Gromacs

  1. Download latest source code:
  2. tar -xzf gromacs-2022.1.tar.gz
  3. Submit an interactive job to a ThetaGPU compute node from Theta login node:
    user@thetalogin4:~>module load cobalt/cobalt-gpu
    user@thetalogin4:~>qsub -I -n 1 -t 60 -q single-gpu -A PROJECT --attrs filesystems=home
    Job routed to queue "single-gpu".
    Wait for job 10108666 to start...
    Opening interactive session to thetagpu06-gpu0
  4. cd gromacs-2022.1
  5. mkdir build
  6. module load cmake
  7. cmake -DCMAKE_C_COMPILER=mpicc -DCMAKE_CXX_COMPILER=mpicxx \
          -DCMAKE_INSTALL_PREFIX=/path-to/gromacs-2022.1/build \
  8. make –j 16
  9. make install
  10. The installed binary is build/bin/gmx_mpi.

Running Gromacs on ThetaGPU

Prebuilt Gromacs binaries can be found in the directory /soft/applications/gromacs/gromacs_cuda.

A sample qsub script follows that will run GROMACS on a full node using all eight GPUs available.

#!/bin/bash -l
#COBALT -n 1
#COBALT -t 30 
#COBALT -q full-node 
#COBALT -project catalyst 
#COBALT --attrs filesystems=home,theta-fs0


mpirun -hostfile $COBALT_NODEFILE --np 8 \
      /soft/applications/gromacs/gromacs_cuda/gmx_mpi.2022.1 \
      mdrun -ntomp 8 -gputasks 01234567 -nb gpu -pme gpu -npme 1 \
      -dlb yes -resethway -pin on -v deffnm step5_1 -g test.log

We strongly suggest that users try combinations of different numbers of nodes, MPI ranks per node, number of GPU tasks/devices, GPU task decomposition between nonbonded and PME kernels, and OMP threads per rank to find the optimal throughput for their particular workload.

The following is a representative benchmark for a system with 30,000 atoms generated on a single ThetaGPU node with above example.

Core time(sec) Wall time(sec) (%)
Time 691.769 10.810 6399.6
ns/day hour/ns
Performance 399.661 0.060