Developer Guide


MPI Tuning

The Intel® MPI Library includes the
tuning utility, which allows you to automatically adjust Intel MPI Library parameters, such as collective operation algorithms, to your cluster configuration or application. The tuner iteratively launches a benchmarking application with different configurations to measure performance and stores the results of each launch. Based on these results, the tuner generates optimal values for the parameters that are being tuned.
usage model changed in the 2018 release. Tuning parameters should be specified in configuration files rather than as command-line options.

Configuration File Format

All tuner parameters should be specified in two configuration files, passed to the tuner with the
option. A typical configuration file consists of the main section, specifying generic options, and search space sections for specific library parameters (for example, for specific collective operations). Configuration files differ in mode and dump-file fields only. To comment a line, use the hash symbol #.
You can also specify MPI options to simplify
usage. MPI options are useful for Intel® MPI Benchmarks that have special templates for
located at
. The templates require no changes in configuration files to be made.
For example, to tune the
collective algorithm, use the following option:
$ mpitune -np 2 -ppn 2 -hosts HOST1 -m analyze -c <path-to-Bcast.cfg>
Experienced users can change configuration files to use this option for other applications.

Output Format

The tuner presents results in a JSON tree view (since the 2019 release), where the
layer is added automatically for each tree:
{ "coll=Reduce": { "ppn=2": { "comm_size=2": { "comm_id=-1": { "msg_size=243": { "REDUCE=8": {} }, "msg_size=319": { "REDUCE=11": {} }, "msg_size=8192": { "REDUCE=8": {} }, "msg_size=28383": { "REDUCE=9": {} }, "msg_size=-1": { "REDUCE=1": {} } } } } } }
To add the resulting JSON tree to the library, use the
environment variable.

Old Output Format

The old output format is only valid for Intel MPI Library 2018 and prior versions:
Use the resulting variable value with the application launch to achieve performance gain.

See Also

For details on
configuration options, refer to the Developer Reference section mpitune.

Product and Performance Information


Performance varies by use, configuration and other factors. Learn more at