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Determining exactly how well an MPI application is performing on current HPC systems is a challenging task. Analysis of the cputime, system time and IO time of a serial application can provide basic performance information but for a parallel application, the (wasted) time spent waiting on communication is not visible from “outside the application”. MPI performance analysis tools provide insight into this “internal” computation versus communication behaviour and, as a result, understanding of the application’s parallel performance. They can reveal potential issues such as load imbalance, synchronization contentions and much more. As well as pointing out the limitations of an MPI application, access to this profiling information can assist user in optimizing the application to achieve greater scalability.   


This document describes how to use MPI performance analysis tools including profilers and tracers which are available on NCI NF compute systems. For further help with using MPI profilers and tracers, please send email to .   



NCI compute systems.


MPI Profiling

A MPI profiler aggregates “whole run” statistics at run time , e.g. , total amount of time spent in MPI, total number of messages or bytes sent, etc. As this information is available on a per-rank basis, issues such as load imbalance are exposed.   


Note that (currently) no profiling information will be produced if the execution does not complete normally (i.e. does not call MPI_Finalize()).   

On NCI NF compute systems, two different lightweight MPI profilers are installed. They are IPM and mpiP. Both of these tools require minimal actions to invoke – we recommend that you use them regularly. Note that their use is only applicable to Open MPI applications.   


IPM is a widely-used MPI profiler with many sites using it by default for every MPI job. The level of detail is selectable at runtime and presented through a variety of text and web reports. IPM can also provide information from the CPU HW counters giving basic CPU as well as MPI statistics.   

For version 1.6.5 or less, please use ipm/0.983-nci.For version 1.7 or more, please use ipm/2.0.2. Please note this is different to NCI version and we are working on our own version for 2.0.2.  


Using IPM does not require code recompilation. Instead, LD_PRELOAD is used to dynamically load the IPM library ( as a wrapper to the MPI runtime.   

A simple PBS job script using the IPM profiler with an MPI executable (prog.exe) is as shown below:   


  • #PBS -l ncpus=2  module load openmpi  module load ipm  

    mpirun prog.exe > output  

}}}  Currently, IPM is available for openmpi version 1.4.1 and above.   

User can also define the IPM log directory and log file name via setting following environment variables in the PBS job script before mpirun, an good example of IPM log directory and file name are:   

export IPM_LOGDIR=/short/$PROJECT/ipm_logs

NOTE: For some applications, defining IPM_LOGDIR in .bashrc or .cshrc is compulsory to successfully generate IPM profile data file. These applications usually have following features:   

  • different MPI ranks working in different directories,  

  • PBS job scripts are automatically generated by some sense,  

  • more to be added …     


By default IPM produces a summary of the performance information for the application on stdout. IPM also generates an XML data file which will be named something like (if user hasn’t define IPM_LOGFILE envorinment variable):   

your_username.1231369287.321103.0 eg. jxc900.1231369287.321103.0  


The XML data file can be used to generate a graphical webpage in one of two ways.   

Use lightweight browser on NCI NF machine

To visualize the IPM XML data on Raijin, you need to login to Raijin with an X display, eg. using ssh -X or ssh -Y, or with VNC. The detailed sample instruction on Raijin is listed below.   

$ ssh -X raijin
$ module load openmpi
$ module load ipm
$ ipm_view IPM_XML_file


Use your favourite browser on your laptop/desktop

Alternatively, the IPM XML data file can be parsed to HTML format. The detailed sample instruction on raijin are:   

$ ssh raijin
$ module load openmpi
$ module load ipm
$ module load ploticus
$ ipm_parse -html <IPM_XML_file>

The ipm_parse command will generate a directory containing parsed IPM profile data with graphs. The directory will be named something like:   


You can secure copy the directory to your local disk. A sample instruction is as follows.   

$ scp -r a.out_1_your_username.1231369287.321103.0_ipm_${jobid} user@your_local_machine:path_to_store/

Then you can view it with your favourite web browser, e.g firefox, on your desktop:   

$ firefox path_to_store/a.out_1_your_username.1231369287.321103.0_ipm_${jobid}/index.html



IPM can be integrated with hardware performance counters and profile useful information such as GFlops, cache misses, etc. PAPI is used for this purpose. Currently, IPM-HPM are only avaialble for OpenMPI version 1.4.3.   

To usage IPM with HPM, please do following:   

module load ipm/0.983-hpm

A sample PBS job script will be similar to following:   

  #PBS -l ncpus=2
  module load openmpi
  module load ipm/0.983-cache


  mpirun prog.exe > output


Pre-defined PAPI Eventsets

The environment variable IPM_HPM can be selected from following predefined PAPI event sets for Raijin.   

For Raijin:  



Customize Your Own PAPI Eventsets

You can also customize your own PAPI eventsets for IPM_HPM environment variable via using papi_event_chooser command.   

$ module load papi
$ papi_event_chooser -help

Both PAPI pre-define events, and native hardware events can be used.   



Message sizes rounding to power of 2

setenv IPM_ROUNDED yes

This feature significantly reduces the memory usage and postprocessing time during both runtime and parsing time.   

IPM Check-pointing


This feature allow IPM to checkpoint profile status in $IPM_CHKPT_INTERVAL seconds intervals. The checkpointed profile data will be stored in a.out_1_your_username.1231369287.321103.0_ipm_${jobid}.${rank} files.   

These files can be merged (using cat command) into a single xml file, and parsed with ipm_parse.   


mpiP is a lightweight profiling library for MPI applications. In addition to the MPI summary profiling provided by IPM, mpiP can provide “call site” statistics showing which calls in the code are dominating MPI execution time.   


Using mpiP does not require code recompilation. Instead, LD_PRELOAD is used to dynamically load the mpiP library ( as a wrapper to the MPI runtime.   

A sample PBS job script using mpiP profiler with MPI executable (prog.exe) is as shown below:   

  #PBS -l ncpus=2
  module load openmpi/1.3.3
  module load mpiP
  mpirun prog.exe > output

Currently, mpiP is available for openmpi version 1.3.3 and above.   


The mpiP profiler generates a text based output file named something like:   



To visualize the mpiP profile data on Raijin, you need to login to Raijin with a X display, eg. using ssh -X or ssh -Y, or with VNC. The detailed sample instruction on raijin is listed below.   

$ ssh -X raijin
$ module load openmpi
$ module load mpiP
$ mpipview prog.exe.${np}.XXXXX.1.mpiP

mpipview is able to map MPI callsites in the profile data to source code. This requires the MPI program to be compiled with -g option and linked with libunwind, as follows.   

$ module load openmpi
$ module load mpiP
$ mpicc -g -o prog.exe prog.c -lmpiP -lm -lbfd -liberty -lunwind



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Cooperation with General Profilers 

Due to MPI profilers only profile for MPI function calls, it is not sufficient to reveal other details of the application. To get a better knowledge of users program, for example:   

  • which portion of the user program spent the most time,


  • what is the memory behaviour of this program, including number of load/store instructions, cache misses, etc. 

  • how many bus transactions has been made in this program,   

it is necessary to use a general purpose profiler.   

Please refer  General profiling for the details of the general profilers available at NF compute systems.   


MPI Tracing

An MPI tracer collects an event history. It is common to display such event history on a timeline display. Tracing data can provide much interesting detail, but data volumes are large and the overhead of collection may be non-trivial. Often the collection of traces has to be limited in both duration and number of cpus to be feasible. The use of MPI tracing is strongly encouraged during the development or tuning of parallel applications but should not be used in production runs.   







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In raijin:   

In script of PBS,   

module load openmpi/1.6.5-mlx
module load darshan //create the log directories for specific users with
/apps/openmpi/1.6.5-mlx/bin/mpirun ..

After running, generate pdf summary report from logs. /logdir/<JobID>***.darshan.gz

The NCI NF is investigating install the following MPI tracing tools:   

More information will be provided shortly.     


The Allinea DDT&MAP and its performance report products are installed on Raijin for evaluation with a trial license.   

A brief homemade Usage Guide for Raijin (requested by Muhammad):   

  • login raijin with -X or -Y,  
  • module load allinea  
  • For DDT or MAP,  
cp /apps/allinea/4.2/tools/templates/pbs.qtf ~
edit pbs.qtf to be suitable for raijin
like adding #PBS -P z00 , #PBS -l mem 30GB
  • Compile your MPI program with “-g -fno-inline” for GNU, “-g -fno-inline-functions” for Intel compilers  
  • DDT or MAP  
    • Type “ddt” or “map” and click “Run” button  
    • Complete the Run window, something to remind,  
      • Implementation: choose openmpi with ticking “override default mpirun path” and using full path like “/apps/openmpi/1.6.5/bin/mpirun”  Make sure you tick “Memory Debugging” for DDT 

    • Tick “submit to queue”, click “configure” to select your template file, and Regexp for job id changed to “(\d+).r-man2”,  
    • Edit Queue parameters, edit the walltime and queue (to express or normal)  
    • Click “Submit” and you can debug/profile your program.  
  • perf-report  
    • Edit your PBS script with “module load allinea”  
    • Replace your mpirun command line as  perf-report /apps/openmpi/1.*/bin/mpirun .. (Note: full path of mpirun must be used)  

    • Then a HTML report will be generated (Note: it needs javascript so dillo doesn’t work. the best way may be copying it to your computer)   

More on usage can be found in raijin:/apps/allinea/4.2/tools/doc/userguide.pdf