Intel® MPI Library Developer Guide for Linux* OS

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ID 768728
Date 10/31/2024
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Document Table of Contents
Document Table of Contents x
Intel® MPI Library Developer Guide for Linux* OS
Intel® MPI Library 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 Java MPI applications Development Recommendations
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 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
Intel® MPI Library Developer Guide for Linux* OS

Java* MPI Applications Support

The Intel® MPI Library provides an experimental feature to enable support for Java MPI applications. Java bindings are available for a subset of MPI-2 routines. For a full list of supported routines, refer to the Developer Reference, section Miscellaneous > Java Bindings for MPI-2 Routines.

Running Java MPI applications

Follow these steps to set up the environment and run your Java MPI application:

  1. Source mpivars.sh from the Intel® MPI Library package to set up all required environment variables, including LIBRARY_PATH and CLASSPATH.
  2. Build your Java MPI application as usual.
  3. Update CLASSPATH with the path to the jar application or pass it explicitly with the -cp option of the java command.
  4. Run your Java MPI application using the following command: 
    $ mpirun <options> java <app>

    where:

    • <options> is a list of mpirun options
    • <app> is the main class of your Java application

    For example: 

    $ mpirun -n 8 -ppn 1 -f ./hostfile java mpi.samples.Allreduce

Development Recommendations

You can use the following tips when developing Java* MPI applications:

  • To reduce memory footprint, you can use Java direct buffers as buffer parameters of collective operations in addition to using Java arrays. This approach allows you to allocate the memory out of the JVM heap and avoid additional memory copying when passing the pointer to the buffer from JVM to the native layer.
  • When you create Java MPI entities such as Group, Comm, Datatype, and similar, memory is allocated on the native layer and is not tracked by the garbage collector. Therefore, this memory must be released explicitly. Pointers to the allocated memory are stored in a special pool and can be deallocated using one of the following methods:
    • entity.free(): frees the memory backing the entity Java object, which can be an instance of Comm, Group, etc.
    • AllocablePool.remove(entity): frees the memory backing the entity Java object, which can be an instance of Comm, Group, etc.
    • AllocablePool.cleanUp(): explicitly deallocates the memory backing all Java MPI objects created by that moment.
    • MPI.Finalize(): implicitly deallocates the memory backing all Java MPI objects and that has not been explicitly deallocated by that moment.
Parent topic: Running Applications
Level Two Title