Profiling GEOS-Chem with the TAU performance system

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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.

Installing TAU

The best way to build TAU is with Spack.

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.

NOTE: The gcc.env file is the script that loads your Spack modules.


#SBATCH -c 24
#SBATCH -n 1
#SBATCH --mem=15000

### Job script to run GEOS-Chem with TAU profiling
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

# 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

If you use SLURM, you can submit this with


Otherwise you can just run it interactively:


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:

paraprof GEOS-Chem_Profile_Results.ppk

This will open two windows, the ParaProf Manager window and the Main Data window. For more information on how to interpret the profiling data, see the ParaProf User's Manual.

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.