This article briefly describes the steps for running GROMACS on a virtual machine (VM) that's deployed on Azure. It also presents the performance results of running GROMACS on Azure.
GROMACS (GROningen MAChine for Simulations) is a molecular dynamics package designed for simulations of proteins, lipids, and nucleic acids. It's used primarily for dynamic simulations of biomolecules and provides a rich set of calculation types and preparation and analysis tools. GROMACS:
- Supports compressed trajectory storage format and advanced techniques for free-energy calculations.
- Runs multiple simulations as part of a single program, which enables generalized ensemble methods like replica-exchange.
- Works within an elaborate multi-level parallelism that distributes computational work across ensembles of simulations and multiple program paths.
- Describes all systems with triclinic unit cells, so complex geometries like rhombic dodecahedron, truncated octahedron, and hexagonal boxes are supported.
GROMACS is used across the healthcare industry by biotechnology organizations, universities and research centers, education, pharmaceutical organizations, and hospitals and clinics.
Why deploy GROMACS on Azure?
- Modern and diverse compute options to align to your workload's needs
- The flexibility of virtualization without the need to buy and maintain physical hardware
- Rapid provisioning
- With a 120 vCPU, a performance increase of three to four times that of 16 CPUs
Architecture
Download a Visio file of this architecture.
Components
- Azure Virtual Machines is used to create a Linux VM. For information about deploying the VM and installing the drivers, see Linux VMs on Azure.
- Azure Virtual Network is
used to create a private network infrastructure in the cloud.
- Network security groups are used to restrict access to the VM.
- A public IP address connects the internet to the VM.
- A physical solid-state drive (SSD) is used for storage.
Compute sizing and drivers
Performance tests of GROMACS on Azure used HBv3-series VMs running the Linux Ubuntu operating system. The following table provides details about HBv3-series VMs.
| VM size | vCPU | Ram memory (GiB) | Memory bandwidth (GBps) | Base CPU frequency (GHz) | All-cores frequency (GHz, peak) | Single-core frequency (GHz, peak) | RDMA performance (GBps) | Maximum data disks |
|---|---|---|---|---|---|---|---|---|
| Standard_HB120rs_v3 | 120 | 448 | 350 | 1.9 | 3.0 | 3.5 | 200 | 32 |
| Standard_HB120-96rs_v3 | 96 | 448 | 350 | 1.9 | 3.0 | 3.5 | 200 | 32 |
| Standard_HB120-64rs_v3 | 64 | 448 | 350 | 1.9 | 3.0 | 3.5 | 200 | 32 |
| Standard_HB120-32rs_v3 | 32 | 448 | 350 | 1.9 | 3.0 | 3.5 | 200 | 32 |
| Standard_HB120-16rs_v3 | 16 | 448 | 350 | 1.9 | 3.0 | 3.5 | 200 | 32 |
HBv3-series VMs are optimized for HPC applications like fluid dynamics, explicit and implicit finite element analysis, weather modeling, seismic processing, reservoir simulation, and RTL simulation.
Required drivers
To use the AMD CPUs on HBv3-series VMs, you need to install AMD drivers.
To use InfiniBand, you need to enable InfiniBand drivers.
GROMACS installation
Before you install GROMACS, you need to deploy and connect a Linux VM and install the required AMD and InfiniBand drivers.
For information about deploying the VM and installing the drivers, see Run a Linux VM on Azure.
You can download GROMACS from the GROMACS download page. For information about installing the application, see the Installation guide.
For information about using GROMACS, see the User guide.
GROMACS performance results
The cell and water models described later in this section were used to test GROMACS. Elapsed times of runs on VMs with varying numbers of CPUs were recorded. The following table provides the details of the operating system that was used for testing.
| Operating system version | OS architecture |
|---|---|
| Ubuntu Linux 20.04 | x86-64 |
Results for water-cut1.0_GMX50_bare
| Model | Initial element atom count | Number of cores | Elapsed time (seconds) | Relative speed increase |
|---|---|---|---|---|
| water-cut1.0_GMX50_bare | 3,072,000 | 16 | 291.3 | 1.00 |
| water-cut1.0_GMX50_bare | 3,072,000 | 32 | 158.6 | 1.84 |
| water-cut1.0_GMX50_bare | 3,072,000 | 64 | 89.7 | 3.25 |
| water-cut1.0_GMX50_bare | 3,072,000 | 96 | 71.5 | 4.07 |
| water-cut1.0_GMX50_bare | 3,072,000 | 120 | 66.9 | 4.35 |
Results for water-cut1.0_bare_hbonds
| Model | Initial element atom count | Number of cores | Elapsed time (seconds) | Relative speed increase |
|---|---|---|---|---|
| water-cut1.0_bare_hbonds | 3,072,000 | 16 | 290.7 | 1.00 |
| water-cut1.0_bare_hbonds | 3,072,000 | 32 | 157.1 | 1.85 |
| water-cut1.0_bare_hbonds | 3,072,000 | 64 | 90.4 | 3.22 |
| water-cut1.0_bare_hbonds | 3,072,000 | 96 | 71.6 | 4.06 |
| water-cut1.0_bare_hbonds | 3,072,000 | 120 | 67.8 | 4.29 |
Results for rnase_bench_systems_old-allbond
| Model | Initial element atom count | Number of cores | Elapsed time (seconds) | Relative speed increase |
|---|---|---|---|---|
| rnase_cubic | 24,040 | 16 | 2.6 | 1.00 |
| rnase_cubic | 24,040 | 32 | 1.5 | 1.71 |
| rnase_cubic | 24,040 | 64 | 0.9 | 2.84 |
| rnase_cubic | 24,040 | 96 | 0.8 | 3.17 |
| rnase_cubic | 24,040 | 120 | 0.8 | 3.22 |
| rnase_dodec | 16,816 | 16 | 2.4 | 1.00 |
| rnase_dodec | 16,816 | 32 | N/A* | N/A* |
| rnase_dodec | 16,816 | 64 | N/A* | N/A* |
| rnase_dodec | 16,816 | 96 | 0.8 | 3.10 |
| rnase_dodec | 16,816 | 120 | N/A* | N/A* |
* The rnase_dodec model can't run on 32-core, 64-core, or 120-core configurations because of the size of the model and an MPI error.
Results for rnase_bench_systems
| Model | Initial element atom count | Number of cores | Elapsed time (seconds) | Relative speed increase |
|---|---|---|---|---|
| rnase_cubic | 24,040 | 16 | 2.5 | 1.00 |
| rnase_cubic | 24,040 | 32 | 1.4 | 1.76 |
| rnase_cubic | 24,040 | 64 | 0.9 | 2.95 |
| rnase_cubic | 24,040 | 96 | 0.7 | 3.41 |
| rnase_cubic | 24,040 | 120 | 0.7 | 3.50 |
| rnase_dodec | 16,816 | 16 | 2.2 | 1.00 |
| rnase_dodec | 16,816 | 32 | 1.3 | 1.73 |
| rnase_dodec | 16,816 | 64 | 0.8 | 2.77 |
| rnase_dodec | 16,816 | 96 | 0.7 | 3.29 |
| rnase_dodec | 16,816 | 120 | 0.7 | 3.26 |
Results for gmxbench-3.0
| Model | Initial element atom count | Number of cores | Elapsed time (seconds) | Relative speed increase |
|---|---|---|---|---|
| d.poly-ch2 | 6,000 atoms and 6,000 vsites | 16 | 0.6 | 1.00 |
| d.poly-ch2 | 6,000 atoms and 6,000 vsites | 32 | 0.4 | 1.70 |
| d.poly-ch2 | 6,000 atoms and 6,000 vsites | 64 | 0.2 | 2.85 |
| d.poly-ch2 | 6,000 atoms and 6,000 vsites | 96 | 0.2 | 3.22 |
| d.poly-ch2 | 6,000 atoms and 6,000 vsites | 120 | 0.2 | 3.20 |
Results for ADH_bench_systems_old-allbonds
| Model | Initial element atom count | Number of cores | Elapsed time (seconds) | Relative speed increase |
|---|---|---|---|---|
| adh_cubic | 134,177 | 16 | 13.5 | 1.00 |
| adh_cubic | 134,177 | 32 | 8.2 | 1.65 |
| adh_cubic | 134,177 | 64 | 4.6 | 2.94 |
| adh_cubic | 134,177 | 96 | 3.6 | 3.70 |
| adh_cubic | 134,177 | 120 | 3.2 | 4.28 |
| adh_dodec | 95,561 | 16 | 11.3 | 1.00 |
| adh_dodec | 95,561 | 32 | 6.5 | 1.72 |
| adh_dodec | 95,561 | 64 | 3.8 | 3.01 |
| adh_dodec | 95,561 | 96 | 3.0 | 3.78 |
| adh_dodec | 95,561 | 120 | 3.0 | 3.81 |
Results for ADH_bench_systems
| Model | Initial element atom count | Number of cores | Elapsed time (seconds) | Relative speed increase |
|---|---|---|---|---|
| adh_cubic | 134,177 | 16 | 13.0 | 1.00 |
| adh_cubic | 134,177 | 32 | 7.6 | 1.71 |
| adh_cubic | 134,177 | 64 | 4.3 | 3.03 |
| adh_cubic | 134,177 | 96 | 3.2 | 4.02 |
| adh_cubic | 134,177 | 120 | 3.0 | 4.28 |
| adh_dodec | 95,561 | 16 | 10.9 | 1.00 |
| adh_dodec | 95,561 | 32 | 6.1 | 1.78 |
| adh_dodec | 95,561 | 64 | 3.5 | 3.10 |
| adh_dodec | 95,561 | 96 | 2.8 | 3.85 |
| adh_dodec | 95,561 | 120 | 2.7 | 4.05 |
Azure cost
The following tables present wall-clock times that you can use to calculate Azure costs. You can multiply the times presented here by the Azure hourly rates for HBv3-series VMs to calculate costs. For the current hourly costs, see Linux Virtual Machines Pricing.
Only the wall-clock time for running the test cases is considered for these cost calculations. Application installation time isn't considered.
You can use the Azure pricing calculator to estimate the costs for your configuration.
| VM size | Number of CPUs | Elapsed time (hours) |
|---|---|---|
| Standard_HB120-16rs_v3 | 16 | 0.178 |
| Standard_HB120-32rs_v3 | 32 | 0.097 |
| Standard_HB120-64rs_v3 | 64 | 0.055 |
| Standard_HB120-96rs_v3 | 96 | 0.044 |
| Standard_HB120rs_v3 | 120 | 0.041 |
Summary
- Gromacs was tested successfully on Azure HBv3-series VMs.
- With a 120 vCPU, the performance is three to four times the performance with 16 CPUs.
Contributors
This article is maintained by Microsoft. It was originally written by the following contributors.
Principal authors:
- Hari Bagudu | Senior Manager
- Gauhar Junnarkar | Principal Program Manager
- Vinod Pamulapati | HPC Performance Engineer
Other contributors:
- Mick Alberts | Technical Writer
- Guy Bursell | Director Business Strategy
- Sachin Rastogi | Manager
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Next steps
- GPU-optimized virtual machine sizes
- Virtual machines on Azure
- Virtual networks and virtual machines on Azure
- Learning path: Run high-performance computing (HPC) applications on Azure