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Difference between revisions of "Scalasca"

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(Update Scalasca for latest scalasca release and HAWK system)
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== Usage ==
 
== Usage ==
  
To use Scalasca first load the respective software module:
+
To use Scalasca start by loading the respective software modules:
 
 
 
{{Command|command =
 
{{Command|command =
module load performance/scalasca
+
module load scalasca scorep cube # on HAWK<br>
 +
module load performance/scalasca performance/scorep # on vulcan
 
}}
 
}}
  
First instrument your application using the scin (scalasca -instrument) command and the MPI compiler
+
Then, instrument your application using Score-P by prefixing all MPI compiler and linker commands with the ''scorep'' command:
 
{{Command|command =  
 
{{Command|command =  
skin mpicc myapp.c -o myapp
+
scorep mpicc myapp.c -o myapp
 
}}
 
}}
Then analyze your application using the scan (scalasca -analyze) command and ''mpirun''
+
Next analyze your application using the ''scalasca -analyze'' command with your usual ''mpirun'' command:
 
{{Command|command=
 
{{Command|command=
scan mpirun -np ${NP} myapp
+
export MPI_SHEPHERD=true # this is mandatory when running with mpt on HAWK<br>
 +
scalasca -analyze mpirun -n ${NP} myapp
 
}}
 
}}
The last step is to explore the generated reports by rerunning your application using the square (scalasca -examine) command:
+
After the run there will be a folder prefixed with "scorep_*" containing the messurment results.
 +
{{Warning|text=The scalasca -analyze command evaluates the provided MPI command line and will try to determine the used MPI implementation, number of MPI ranks (based on '-n' option), additional MPI options, the application name, and the application's arguments. If you encounter problems at this point, please consult the scalasca documentation for details.}}
 +
 
 +
The last step is to explore the generated reports by using the ''scalasca -examine'' command, with the previously created "scorep_*" folder as the experiment name:
 
{{Command|command=
 
{{Command|command=
square mpirun -np ${NP} myapp
+
scalasca -examine <experiment_name>
 +
}}
 +
This will open Cube with the generated report automatically if you have enabled X forwarding.
 +
You can also inspect the generated ''*.cubex'' report files, manually starting the Cube GUI:
 +
{{Command|command=
 +
cube <file>.cubex
 
}}
 
}}
  
To view the final results you then can use the Cube3 GUI
+
=== Usage on Hawk ===
{{Command|command=
+
{{Warning|text=
cube3
+
It is necessary to set {{command|command=export MPI_SHEPHERD=true}} for the ""scalasca -analyze"" step.
 
}}
 
}}
  

Revision as of 10:51, 29 April 2020

Scalasca is an open-source toolset for the performance analyzes of parallel applications helping to identify optimization opportunities. It has been specifically designed for the use on large-scale systems including Cray XT/XE, but is also well-suited for small- and medium-scale HPC platforms. Scalasca supports an incremental performance-analysis procedure that integrates runtime summaries with in-depth studies of concurrent behavior via event tracing, adopting a strategy of successively refined measurement configurations. A distinctive feature is the ability to identify wait states that occur, for example, as a result of unevenly distributed workloads.
Scalasca-logo.png
Developer: JSC
Platforms:
Category: Performance Analyzer
License: New BSD
Website: Scalasca homepage


Usage

To use Scalasca start by loading the respective software modules:

module load scalasca scorep cube # on HAWK
module load performance/scalasca performance/scorep # on vulcan


Then, instrument your application using Score-P by prefixing all MPI compiler and linker commands with the scorep command:

scorep mpicc myapp.c -o myapp

Next analyze your application using the scalasca -analyze command with your usual mpirun command:

export MPI_SHEPHERD=true # this is mandatory when running with mpt on HAWK
scalasca -analyze mpirun -n ${NP} myapp

After the run there will be a folder prefixed with "scorep_*" containing the messurment results.

Warning: The scalasca -analyze command evaluates the provided MPI command line and will try to determine the used MPI implementation, number of MPI ranks (based on '-n' option), additional MPI options, the application name, and the application's arguments. If you encounter problems at this point, please consult the scalasca documentation for details.


The last step is to explore the generated reports by using the scalasca -examine command, with the previously created "scorep_*" folder as the experiment name:

scalasca -examine <experiment_name>

This will open Cube with the generated report automatically if you have enabled X forwarding. You can also inspect the generated *.cubex report files, manually starting the Cube GUI:

cube <file>.cubex


Usage on Hawk

Warning: It is necessary to set
export MPI_SHEPHERD=true
for the ""scalasca -analyze"" step.


See also

External links