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Scalasca is an open-source software tool that supports the performance optimization of parallel programs by measuring and analyzing their runtime behavior via event traces. The analysis identifies potential performance bottlenecks - in particular those concerning communication and synchronization - and offers guidance in exploring their causes. Scalasca supports applications using MPI, OpenMP, POSIX threads, or MPI+OpenMP/Pthreads parallelization.


  • Puhti: 2.6
  • Mahti: 2.6


Usage is possible for both academic and commercial purposes.


Using Scalasca involves three steps:

  1. Instrumentation
  2. Execution measurement collection & analysis
  3. Analysis report examination


Scalasca uses the Score-P measurement infrastructure for instrumentation of the target application. Score-P can be used also as a stand-alone tool without Scalasca.

In order to instrument an application, you need to recompile the application using the Score-P instrumentation command scorep, which is added as a prefix to the original compile and link commands:

module load scorep
scorep mpicc -o my_prog my_prog.c
or setting in a Makefile

CC=scorep mpicc
for C/C++ codes or similarly
F90=scorep mpif90
for Fortran codes.

Measurement collection and analysis

While applications instrumented by Score-P can be executed directly with a measurement configuration defined via environment variables, the scan convenience command provided by Scalasca can be used to control certain aspects of the Score-P measurement environment during the execution of the target application. To produce a performance measurement using an instrumented executable, prefix srun with the scan command in the batch job script:

#SBATCH --ntasks=40

module load scalasca
scan srun ./my_app
By default, a flat profile is collected. Upon completion, measurement results are stored in the experiment directory, which by default is composed of the prefix "scorep_", the target application executable name, the run configuration (e.g., number of MPI ranks and/or OpenMP threads), and a few other parameters of the measurement configuration. For example, in the above example scorep_my_app_40_sum.

One can also collect event trace data. As tracing can produce huge amounts of data, it is recommended to first estimate the size of trace, and possibly filter out some functions from the measurement. Estimate can be obtained with scorep-score command:

scorep-score -r scorep_my_app_40_sum/profile.cubex

Estimated aggregate size of event trace:                   1022kB
Estimated requirements for largest trace buffer (max_buf): 129kB
Estimated memory requirements (SCOREP_TOTAL_MEMORY):       4097kB
(hint: When tracing set SCOREP_TOTAL_MEMORY=4097kB to avoid intermediate flushes
 or reduce requirements using USR regions filters.)

flt     type max_buf[B] visits time[s] time[%] time/visit[us]  region
         ALL    131,431 20,196   12.81   100.0         634.28  ALL
         MPI     95,054  8,076    8.65    67.5        1071.04  MPI
         USR     24,168  8,056    3.38    26.3         418.96  USR
         COM     12,168  4,056    0.78     6.1         193.47  COM
      SCOREP         41      8    0.00     0.0          48.00  SCOREP

         MPI     94,000  8,000    0.17     1.3          20.97  MPI_Sendrecv
         USR     12,000  4,000    0.00     0.0           0.25  swap_fields
         COM     12,000  4,000    0.00     0.0           0.53  exchange
         USR     12,000  4,000    3.33    26.0         832.89  evolve
         MPI        826     14    0.01     0.1         823.21  MPI_Recv

In order to filter out the measurement of swap_fields and evolve, one can create a file scorep.filter with the contents:


and check the effect of filtering with -f option:

scorep-score -f scorep.filter -r scorep_my_app_40_sum/profile.cubex

Estimated aggregate size of event trace:                   835kB
Estimated requirements for largest trace buffer (max_buf): 105kB

One could now proceed with the trace collection by setting SCOREP_FILTERING_FILE environment variable and by passing options -q and -t to scan command:

#SBATCH --ntasks=40

module load scalasca

export SCOREP_FILTERING_FILE=scorep.filter

scan -q -t srun ./my_app

After the trace collection is finished, Scalasca will carry out trace-analysis for identifying various performance bottlenecks. With tracing enabled, the experiment directory would be scorep_my_app_40_trace.

Analysis report examination

The Scalasca analysis report explorer square cannot currently be run in CSC supercomputers. However, user may install Scalasca on their local workstation, and copy the experiment directory there for analysis, e.g.

rsync -r .
square scorep_my_app_40_trace
For large traces, one may copy only the post-processed trace analysis result file scorep_my_app_40_trace/scout.cubex.

The OTF2 formatted event trace scorep_my_app_40_trace/trace.otf2 can be analyzed also with Intel Trace Analyzer.

More information

Last update: October 13, 2022