PGPROF User's Guide - The Portland Group

Using PGPROF
Table 2 MPI Profiling Options
This MPI distribution... Requires compiling and linking with these options ...
MPICH1
Deprecated. -Mprof =mpich1,{func|lines|time}
MPICH2
Deprecated. -Mprof =mpich2,{func|lines|time}
MPICH v3 -Mprof =mpich,{func|lines|time}
MVAPICH1
Deprecated. -Mprof =mvapich1,{func|lines|time}
MVAPICH2
MS-MPI
Open MPI
SGI MPI
Use MVAPICH2 compiler wrappers:
-profile={profcc|proffer}
-Mprof ={func|lines|time}
-Mprof =msmpi,{func|lines}
Use Open MPI compiler wrappers:
-Mprof ={func|lines|time}
-Mprof =sgimpi,{func|lines|time}
For more details about how to compile an MPI program for profiling, refer to the ‘Using MPI’
section of the PGI Compiler User‘s Guide.
Once you have built an instrumented version of your MPI application, running it as you normally
would produces the MPI profile data.
On successful program termination, one profile data file is created for each MPI process.
The master profile data file is named pgprof.out. The other files have names similar to
pgprof.out, but they are numbered.
PGPROF MPI profiling collects counts of the number of messages and bytes sent and received.
You can then use this information to analyze a program's message passing behavior.
Analyzing the Performance of MPI Programs
Figure 9 illustrates an MPI profile.
This sample shows an example MPI profile with maximum times and counts in the Statistics
Table, and per-process measurements in the Parallelism tab. The Parallelism tab for MPI
programs is used in the same way that it is used for multi-threaded programs, as described in
Analyzing the Performance of Multi-Threaded Programs.
You can use the send and receive counts for messages, the byte counts to identify potential
communication bottlenecks, and the process-specific data to find load imbalances.
PGI Profiler User Guide 18