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Copyright (c) 2013-2015 Inria. All rights reserved.
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===========================================================================
Low level communication monitoring interface in Open MPI
Introduction
------------
This interface traces and monitors all messages sent by MPI before they go to the
communication channels. At that levels all communication are point-to-point communications:
collectives are already decomposed in send and receive calls.
The monitoring is stored internally by each process and output on stderr at the end of the
application (during MPI_Finalize()).
Enabling the monitoring
-----------------------
To enable the monitoring add --mca pml_monitoring_enable x to the mpirun command line.
If x = 1 it monitors internal and external tags indifferently and aggregate everything.
If x = 2 it monitors internal tags and external tags separately.
If x = 0 the monitoring is disabled.
Other value of x are not supported.
Internal tags are tags < 0. They are used to tag send and receive coming from
collective operations or from protocol communications
External tags are tags >=0. They are used by the application in point-to-point communication.
Therefore, distinguishing external and internal tags help to distinguish between point-to-point
and other communication (mainly collectives).
Output format
-------------
The output of the monitoring looks like (with --mca pml_monitoring_enable 2):
I 0 1 108 bytes 27 msgs sent
E 0 1 1012 bytes 30 msgs sent
E 0 2 23052 bytes 61 msgs sent
I 1 2 104 bytes 26 msgs sent
I 1 3 208 bytes 52 msgs sent
E 1 0 860 bytes 24 msgs sent
E 1 3 2552 bytes 56 msgs sent
I 2 3 104 bytes 26 msgs sent
E 2 0 22804 bytes 49 msgs sent
E 2 3 860 bytes 24 msgs sent
I 3 0 104 bytes 26 msgs sent
I 3 1 204 bytes 51 msgs sent
E 3 1 2304 bytes 44 msgs sent
E 3 2 860 bytes 24 msgs sent
Where:
- the first column distinguishes internal (I) and external (E) tags.
- the second column is the sender rank
- the third column is the receiver rank
- the fourth column is the number of bytes sent
- the last column is the number of messages.
In this example process 0 as sent 27 messages to process 1 using point-to-point call
for 108 bytes and 30 messages with collectives and protocol related communication
for 1012 bytes to process 1.
If the monitoring was called with --mca pml_monitoring_enable 1 everything is aggregated
under the internal tags. With te above example, you have:
I 0 1 1120 bytes 57 msgs sent
I 0 2 23052 bytes 61 msgs sent
I 1 0 860 bytes 24 msgs sent
I 1 2 104 bytes 26 msgs sent
I 1 3 2760 bytes 108 msgs sent
I 2 0 22804 bytes 49 msgs sent
I 2 3 964 bytes 50 msgs sent
I 3 0 104 bytes 26 msgs sent
I 3 1 2508 bytes 95 msgs sent
I 3 2 860 bytes 24 msgs sent
Monitoring phases
-----------------
If one wants to monitor phases of the application, it is possible to flush the monitoring
at the application level. In this case all the monitoring since the last flush is stored
by every process in a file.
An example of how to flush such monitoring is given in test/monitoring/monitoring_test.c
Moreover, all the different flushed phased are aggregated at runtime and output at the end
of the application as described above.
Example
-------
A working example is given in test/monitoring/monitoring_test.c
It features, MPI_COMM_WORLD monitoring , sub-communicator monitoring, collective and
point-to-point communication monitoring and phases monitoring
To compile:
> make monitoring_test
Helper scripts
--------------
Two perl scripts are provided in test/monitoring
- aggregate_profile.pl is for aggregating monitoring phases of different processes
This script aggregates the profiles generated by the flush_monitoring function.
The files need to be in in given format: name_<phase_id>_<process_id>
They are then aggregated by phases.
If one needs the profile of all the phases he can concatenate the different files,
or use the output of the monitoring system done at MPI_Finalize
in the example it should be call as:
./aggregate_profile.pl prof/phase to generate
prof/phase_1.prof
prof/phase_2.prof
- profile2mat.pl is for transforming a the monitoring output into a communication matrix.
Take a profile file and aggregates all the recorded communicator into matrices.
It generated a matrices for the number of messages, (msg),
for the total bytes transmitted (size) and
the average number of bytes per messages (avg)
The output matrix is symmetric
Do not forget to enable the execution right to these scripts.
For instance, the provided examples store phases output in ./prof
If you type:
> mpirun -np 4 --mca pml_monitoring_enable 2 ./monitoring_test
you should have the following output
Proc 3 flushing monitoring to: ./prof/phase_1_3.prof
Proc 0 flushing monitoring to: ./prof/phase_1_0.prof
Proc 2 flushing monitoring to: ./prof/phase_1_2.prof
Proc 1 flushing monitoring to: ./prof/phase_1_1.prof
Proc 1 flushing monitoring to: ./prof/phase_2_1.prof
Proc 3 flushing monitoring to: ./prof/phase_2_3.prof
Proc 0 flushing monitoring to: ./prof/phase_2_0.prof
Proc 2 flushing monitoring to: ./prof/phase_2_2.prof
I 2 3 104 bytes 26 msgs sent
E 2 0 22804 bytes 49 msgs sent
E 2 3 860 bytes 24 msgs sent
I 3 0 104 bytes 26 msgs sent
I 3 1 204 bytes 51 msgs sent
E 3 1 2304 bytes 44 msgs sent
E 3 2 860 bytes 24 msgs sent
I 0 1 108 bytes 27 msgs sent
E 0 1 1012 bytes 30 msgs sent
E 0 2 23052 bytes 61 msgs sent
I 1 2 104 bytes 26 msgs sent
I 1 3 208 bytes 52 msgs sent
E 1 0 860 bytes 24 msgs sent
E 1 3 2552 bytes 56 msgs sent
you can parse the phases with:
> /aggregate_profile.pl prof/phase
Building prof/phase_1.prof
Building prof/phase_2.prof
And you can build the different communication matrices of phase 1 with:
> ./profile2mat.pl prof/phase_1.prof
prof/phase_1.prof -> all
prof/phase_1_size_all.mat
prof/phase_1_msg_all.mat
prof/phase_1_avg_all.mat
prof/phase_1.prof -> external
prof/phase_1_size_external.mat
prof/phase_1_msg_external.mat
prof/phase_1_avg_external.mat
prof/phase_1.prof -> internal
prof/phase_1_size_internal.mat
prof/phase_1_msg_internal.mat
prof/phase_1_avg_internal.mat
Credit
------
Designed by George Bosilca <bosilca@icl.utk.edu> and
Emmanuel Jeannot <emmanuel.jeannot@inria.fr>
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