Intel® MPI Library

Developer Guide for Linux* OS

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ID 768728
Date 10/31/2024
Public
Document Table of Contents
Document Table of Contents x
Developer Guide for Linux* OS
Developer Guide for Linux* OS x
Introduction Installation and Prerequisites Compiling and Linking Running Applications Debugging Applications Analysis and Tuning Troubleshooting Additional Supported Features Examples Notices and Disclaimers
Introduction x
Introducing Intel® MPI Library Conventions and Symbols Related Information
Installation and Prerequisites x
Installation Prerequisite Steps
Compiling and Linking x
Compiling an MPI Program Compilers Support ILP64 Support
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 Interoperability with Other Tools Through -gtool MPI Tuning
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: High Memory Consumption Readings Problem: MPI Application Hangs Problem: Password Required Problem: Cannot Execute Binary File Problem: MPI limitation for Docker*
Additional Supported Features x
Asynchronous Progress Control Example See Also Device-Initiated Communications Notified One-Sided Communications Multiple Endpoints Support
Multiple Endpoints Support x
MPI_THREAD_SPLIT Programming Model Threading Runtimes Support Program Examples Code Change Guide
Examples x
async_progress_sample.c thread_split.cpp thread_split_omp_for.c thread_split_omp_task.c thread_split_pthreads.c
Developer Guide for Linux* OS

Asynchronous Progress Control

Intel® MPI Library supports asynchronous progress threads that allow you to manage communication in parallel with application computation and, as a result, achieve better communication/computation overlapping.

NOTE:
Asynchronous progress has a full support for MPI point-to-point operations, blocking collectives, and a partial support for non-blocking collectives (MPI_Ibcast, MPI_Ireduce, and MPI_Iallreduce).

To enable asynchronous progress, pass 1 to the I_MPI_ASYNC_PROGRESS environment variable. You can define the number of asynchronous progress threads by setting the I_MPI_ASYNC_PROGRESS_THREADS environment variable. The I_MPI_ASYNC_PROGRESS_ID_KEY variable sets the MPI info object key that is used to define the progress thread_id for a communicator.

Setting the I_MPI_ASYNC_PROGRESS_PIN environment variable allows you to control the pinning of the asynchronous progress threads. In case of N progress threads per process, the first N logical processors from the list will be assigned to the threads of the first local process, while the next N logical processors - to the second local process and so on.

Example

For example, If the thread affinity is 0,1,2,3 with 2 progress threads per process and 2 processes per node, then the progress threads of the first local process are pinned to logical processors 0 and 1, while the progress threads of the second local process are pinned to processors 2 and 3.

The code example is available below or in the async_progress_sample.c file in the doc/examples  subdirectory of the package.

See Also

async_progress_sample.c

For more information on environment variables, refer to the Intel® MPI Library Developer Reference, section Environment Variable Reference for Asynchronous Progress Control.

Parent topic: Additional Supported Features
Level Two Title