Intel® MPI Library

Developer Guide for Windows* OS

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ID 768730
Date 10/31/2024
Public
Document Table of Contents
Document Table of Contents x
Developer Guide for Windows* OS
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
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*
Developer Guide for Windows* OS

MPI Tuning

Intel® MPI Library provides an Autotuner utility: Autotuner

Autotuner

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 very low.

The autotuner utility tuning scope is the 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.

Parent topic: Analysis and Tuning
Level Two Title