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Gaussian

Gaussian is a versatile program package providing various capabilities for electronic structure modeling.

Available

  • Puhti: G16RevC.01
  • Mahti: G16RevC.02

License

CSC has acquired a full commercial license for Gaussian. Gaussian is available for use by all approved account holders, subject to some license restrictions.To be able to use Gaussian at CSC your user-id has to be added to Gaussian users group. Send a request to CSC Service Desk, servicedesk@csc.fi .

Usage

Initialise the Gaussian environment:

module load gaussian/G16RevC.01
Standard jobs are then conveniently submitted by using the subg16 script:
subg16 time jobname <billing project id>
(run the plain subg16 command for details) For optimal performance of Gaussian jobs on CSC's servers it is beneficial to make some efficiency considerations. Some hints on how to estimate memory and disk requirements can be found here.

Using local disk on Puhti

Particularly some of the wavefunction-based electron correlation methods can be very disk I/O intensive. Such jobs benefit from using the fast NMVE local disk on Puhti. Using local disk for such jobs will also reduce the overall load on the Lustre parallel file system.

On Puhti you can request your Gaussian job to use local disk by submitting the job with the 'subg16_nvme' script:

subg16_nvme time jobname <billing project id> diskspace
The requested disk space is given in GB.

Performance considerations

Here we give a brief example on what type of resources can affect the performance of Gaussian and how these should be taken into consideration. We are using α-Tocopherol (a type of vitamin E) as input structure.

For a b3lyp/cc-pVDZ, %mem=10GB single-point calculation the results are:

platform/cores wall time(hh:mm:ss) billing units
Puhti/10 00:04:19 0.73
mem=20/10 00:04:11 0.85
/20 00:02:16 0.80
/40 00:01:14 0.85
Mahti/128 00:00:53 1.47

For this particular case the scaling is reasonable up to a full Puhti node. Increasing the memory reservation from 10GB to 20GB, doesn't speed up the calculation but only increases its cost. The job is slightly faster on Mahti using 128 cores compared to 40 cores on Puhti but the cost is significantly higher.

If we do the same calculation but increase the size of the basis set to b3lyp/cc-pVTZ the results are:

platform/cores wall time(hh:mm:ss) billing units
Puhti/40 00:12:48 10.27
Mahti/128 00:06:30 10.83

Here we notice that the calculation on Mahti is twice as fast as on Puhti but the cost is about the same.

For a wave function-based method like MP2/cc-pVDZ, the reserved memory (mem=), as well as use of local disk (nvme) significantly affects the performance:

platform/cores wall time(hh:mm:ss) billing units
Puhti, mem=40/10 00:37:55 8.94
mem=80/10 00:19:32 5.91
mem=80/20 00:16:08 7.57
mem=160/20 00:16:25 9.89
nvme, mem=80/20 00:12:40 7.57
mem=160/40 00:11:21 10.62
mem=80/40 00:11:59 9.62
nvme, mem=160/40 00:09:29 9.06
nvme, mem=80/40 00:09:23 7.72
Mahti/128 00:14:30 24.17

From these results we conclude that 80GB seems to be the optimal memory allocation and that the use of local disk clearly improves the performance. The speedup when going from 20 to 40 cores, and using local disk is 1.35, that is below the recommended minimum of 1.5. Hence the most efficient resource usage would correspond to 20 cores, 80GB of memory and local disk on Puhti. For this type of calculation Mahti isn't the optimal choice.

References

More information

Last edited Tue May 3 2022