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Profiling using Scalasca

Scalasca is installed on Cirrus, which is an open source performance profiling tool. Two versions are provided, using GCC 8.2.0 and the Intel 19 compilers; both use the HPE MPT library to provide MPI and SHMEM. An important distinction is that the GCC+MPT installation cannot be used to profile Fortran code as MPT does not provide GCC Fortran module files. To profile Fortran code, please use the Intel+MPT installation.

Loading the one of the modules will autoload the correct compiler and MPI library:

module load scalasca/2.6-gcc8-mpt225


module load scalasca/2.6-intel19-mpt225

Once loaded, the profiler may be run with the scalasca or scan commands, but the code must first be compiled first with the Score-P instrumentation wrapper tool. This is done by prepending the compilation commands with scorep, e.g.:

scorep mpicc -c main.c -o main
scorep mpif90 -openmp main.f90 -o main

Advanced users may also wish to make use of the Score-P API. This allows you to manually define function and region entry and exit points.

You can then profile the execution during a Slurm job by prepending your srun commands with one of the equivalent commands scalasca -analyze or scan -s:

scalasca -analyze srun ./main
scan -s srun ./main

You will see some output from Scalasca to stdout during the run. Included in that output will be the name of an experiment archive directory, starting with scorep_, which will be created in the working directory. If you want, you can set the name of the directory by exporting the SCOREP_EXPERIMENT_DIRECTORY environment variable in your job script.

There is an associated GUI called Cube which can be used to process and examine the experiment results, allowing you to understand your code's performance. This has been made available via a Singularity container. To start it, run the command cube followed by the file in the experiment archive directory ending in .cubex (or alternatively the whole archive), as seen below:

cube scorep_exp_1/profile.cubex

The Scalasca quick reference guide found here provides a good overview of the toolset's use, from instrumentation and use of the API to analysis with Cube.