Profiling GEOS-Chem with the TAU performance system
- Parallelizing GEOS-Chem
- GEOS-Chem 7-day timing tests
- GEOS-Chem scalability
- GEOS-Chem 1-month benchmark timing results
- Profiling GEOS-Chem with the TAU performance system
- Speeding up GEOS-Chem
NOTE: This page is still under development. The Spack installation guide is still being validated.
The TAU Performance System is a profiling tool for performance analysis of parallel programs in Fortran, C, C++, Java, and Python. TAU uses a visualization tool, ParaProf, to create graphical displays of the performance analysis results.
The best way to build TAU is with Spack. Please see these instructions at Github issue geoschem/geos-chem #637.
Compiling and running GEOS-Chem with TAU
You can use the tau_exec command to profile GEOS-Chem in order to reveal computational bottlenecks. Below is a job script that you can use. The script assumes the SLURM scheduler, but if you don't have SLURM on your system (as on the AWS cloud) you can run this script interactively.
#!/bin/bash #SBATCH -c 24 #SBATCH -n 1 #SBATCH -p YOUR-QUEUE-NAME #SBATCH --mem=15000 source ~/.bashrc # Load bash settings source ~/gcc.env # Environment file that loads TAU module from Spack # Set the # of OpenMP threads to the # of cores we requested export OMP_NUM_THREADS=24 # Run GEOS-Chem Classic and have TAU profile it srun -c $OMP_NUM_THREADS tau_exec -T serial,openmp -ebs -ebs_resolution=function ./gcclassic > log 2>&1
Using ParaProf to create plots from the profiling data
In your run directory, there should be one or more profile.* files. The number of profile.* files will depend on the number of CPUs that you use for your GEOS-Chem simulation. To pack all of the profiling data into a single file, type:
paraprof --pack GEOS-Chem_Profile_Results.ppk
Then run paraprof on the packed format (.ppk) file using:
If you click on the the bar labeled "thread0" in the ParaProf manager window, you can generate a plot that looks like this:
The value displayed, the units, and the sort order can be changed from the Options menu. The time that each subroutine spent on the master thread is displayed as a histogram. By examining the histogram you can see which routines are taking the longest to execute. For example, the above plot shows that the COMPUTE_F routine (highlighted with the red box) is spending 444 seconds on the master thread, which is longer than the Rosenbrock solver takes to run. This is a computational bottleneck, which was ultimately caused by an unparallelized DO loop.
To save the plot, select "Save as Bitmap Image" from the File menu. In the Save Image File window, you may select the output type (JPEG File or PNG file) and specify the file name and location.