openmpi/ompi/mca/common/monitoring/README.md

Open MPI common monitoring module

Copyright (c) 2013-2015 The University of Tennessee and The University of Tennessee Research Foundation. All rights reserved. Copyright (c) 2013-2015 Inria. All rights reserved.

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:

1. the first column distinguishes internal (I) and external (E) tags.
2. the second column is the sender rank
3. the third column is the receiver rank
4. the fourth column is the number of bytes sent
5. 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 the e 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:

shell$ make monitoring_test

Helper scripts

Two perl scripts are provided in test/monitoring:

1. 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
   

2. 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.

For instance, the provided examples store phases output in ./prof:

shell$ mpirun -np 4 --mca pml_monitoring_enable 2 ./monitoring_test

Should provide 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 then parse the phases with:

shell$ /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:

shell$ ./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

Authors

Designed by George Bosilca bosilca@icl.utk.edu and Emmanuel Jeannot emmanuel.jeannot@inria.fr