Intel® MPI Library Developer Guide for Windows* OS

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ID 768730
Date 12/16/2022
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Document Table of Contents
Document Table of Contents x
Intel® MPI Library Developer Guide for Windows* OS
Intel® MPI Library Developer Guide for Windows* OS x
Introduction Installation and Prerequisites Compiling and Linking Running Applications Debugging Applications Analysis and Tuning Troubleshooting Notices and Disclaimers
Introduction x
Introducing Intel® MPI Library Conventions and Symbols Related Information
Installation and Prerequisites x
Installation Prerequisite Steps User Authorization
Compiling and Linking x
Compiling an MPI Program Configuring a Microsoft Visual Studio* Project
Running Applications x
Running Intel® MPI Library in Containers Selecting a Library Configuration Running an MPI Program Running an MPI/OpenMP* Program MPMD Launch Mode Fabrics Control Job Schedulers Support Controlling Process Placement Java* MPI Applications Support
Running Intel® MPI Library in Containers x
Build a Singularity* Container for an MPI Application Run the Application with a Container
Fabrics Control x
Selecting Fabrics Libfabric* Support OFI* Providers Support
Debugging Applications x
Debugging Using -gtool for Debugging
Analysis and Tuning x
Displaying MPI Debug Information Tracing Applications MPI Tuning Autotuner mpitune
Troubleshooting x
Error Message: Bad Termination Error Message: No such file or Directory Error Message: Permission Denied Error Message: Fatal Error Error Message: Bad File Descriptor Error Message: Too Many Open Files Problem: MPI Application Hangs Problem: Password Required Problem: Cannot Execute Binary File Problem: MPI limitation for Docker*
Intel® MPI Library Developer Guide for Windows* OS

MPI Tuning

Intel® MPI Library provides the following tuning utilities:

Autotuner

Autotuner is the recommended utility for the application-specific tuning. If an application is spending significant time in MPI collective operations, autotuning might improve its performance. Autotuner is easy-to-use, and its overhead is close to zero.

The autotuning utility's tuning scope is I_MPI_ADJUST_<opname> family of environment variables, which are MPI collective operation algorithms. Autotuner limits tuning to the current cluster configuration (fabric, number of ranks, number of ranks per node). It works while an application is running, so performance could be potentially improved just by enabling the autotuner. It is also possible to generate new tuning file with MPI collective operations adjusted to application needs, and this file can be further passed to the I_MPI_TUNING_BIN variable.

mpitune

mpitune is useful If the search space of the autotuner is not sufficient for your needs. mpitune 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 parameters that are being tuned. mpitune has an ability to search for optimal values of variables other than I_MPI_ADJUST_<opname>, and it could be used for application-specific and cluster-wide tuning. For example, it could tune parameters (like radix) of collective operations.

Differences between the tuning utilities:

Parameter Autotuner mpitune
Low tuning overhead + -
Ease of use + -
Application tuning + +
Microbenchmark tuning + +
Tuning beyond collective operations - +
Parent topic: Analysis and Tuning
Level Two Title