Cpp – FASRC DOCS https://docs.rc.fas.harvard.edu Thu, 27 Feb 2025 15:40:08 +0000 en-US hourly 1 https://wordpress.org/?v=6.8.1 https://docs.rc.fas.harvard.edu/wp-content/uploads/2018/08/fasrc_64x64.png Cpp – FASRC DOCS https://docs.rc.fas.harvard.edu 32 32 172380571 MPI (Message Passing Interface) & OpenMPI https://docs.rc.fas.harvard.edu/kb/mpi-message-passing-interface/ Fri, 31 Jan 2025 18:49:19 +0000 https://docs.rc.fas.harvard.edu/?post_type=epkb_post_type_1&p=28106

Introduction

The Message Passing Interface (MPI) library allows processes in your parallel application to communicate with one another by sending and receiving messages. There is no default MPI library in your environment when you log in to the cluster. You need to choose the desired MPI implementation for your applications. This is done by loading an appropriate MPI module. Currently the available MPI implementations on our cluster are OpenMPI and Mpich. For both implementations the MPI libraries are compiled and built with either the Intel compiler suite or the GNU compiler suite. These are organized in software modules.

Installation

MPI has many forms, we’ll list a few here, and also look at User_Codes/Parallel_Computing/MPI.

mpi4py with Python

To use mpi4py you need to load an appropriate Python software module. We have the Anaconda Python distribution from Continuum Analytics. In addition to mpi4py, it includes hundreds of the most popular packages for large-scale data processing and scientific computing.

You can load python in your user environment by running in your terminal:

module load python/2.7.14-fasrc01

For example code, see Parallel_Computing/Python/mpi4py

OpenMPI with GNU Compiler

If you want to use OpenMPI compiled with the GNU compiler you need to load appropriate compiler and MPI modules. Below are some possible combinations, check module spider MODULENAME to get a full listing of possibilities.

# GCC + OpenMPI, e.g.,
module load gcc/13.2.0-fasrc01 openmpi/5.0.2-fasrc01

# GCC + Mpich, e.g.,
module load gcc/13.2.0-fasrc01 mpich/4.2.0-fasrc01

# Intel + OpenMPI, e.g.,
module load intel/24.0.1-fasrc01 openmpi/5.0.2-fasrc01

# Intel + Mpich, e.g.,
module load intel/24.0.1-fasrc01 mpich/4.2.0-fasrc01

# Intel + IntelMPI (IntelMPI runs mpich underneath), e.g.
module load intel/24.0.1-fasrc01 intelmpi/2021.11-fasrc01

For reproducibility and consistency it is recommended to use the complete module name with the module load command, as illustrated above. Modules on the cluster get updated often so check if there are more recent ones. The modules are set up so that you can only have one MPI module loaded at a time. If you try loading a second one it will automatically unload the first. This is done to avoid dependencies collisions.

There are four ways you can set up your MPI on the cluster:

  • Put the module load command in your startup files.
    Most users will find this option most convenient. You will likely only want to use a single version of MPI for all your work. This method also works with all MPI modules currently available on the cluster.

  • Load the module in your current shell.
    For the current MPI versions you do not need to have the module load command in your startup files. If you submit a job the remote processes will inherit the submission shell environment and use the proper MPI library. Note this method does not work with older versions of MPI.

  • Load the module in your job script.
    If you will be using different versions of MPI for different jobs, then you can put the module load command in your script. You need to ensure your script can execute the module load command properly.

  • Do not use modules and set environment variables yourself.
    You obviously do not need to use modules but can hard code paths. However, these locations may change without warning so you should set them in one location only and not scatter them throughout your scripts. This option could be useful if you have a customized local build of MPI you would like to use with your applications.

Video Training

Examples

For associated MPI examples, head over to  User_Codes/Parallel_Computing/MPI.

  • Example 1: Monte-Carlo calculation of π
  • Example 2: Integration of x2 in interval [0, 4] with 80 integration points and the trapezoidal rule
  • Example 3: Parallel Lanczos diagonalization with reorthogonalization and MPI I/O

Resources

]]> 28106 Cpp, C++ Programming Language https://docs.rc.fas.harvard.edu/kb/cpp-programming-language/ Tue, 30 Apr 2024 13:44:17 +0000 https://docs.rc.fas.harvard.edu/?post_type=epkb_post_type_1&p=26934 Description

C++ (C plus plus) is an object-oriented high-level programing language. C++ files typically have .cpp as the file extension. You can compile C++ codes with either GNU compilers (gcc) or Intel compilers (intel).

Best Practice

We recommend requesting an interactive job to compile a C++ program on a compute node (instead of a login node). The compilation could take up to few seconds to a minute and depending on the complexity of the code. Additionally, it is best to utilize the test partition to compile and test a program before executing its production run on the cluster as a batch-job.

It is best practice to compile a C++ code separately and then use the executable, generated during compilation, in the production run using the sbatch script. If possible, avoid including the compilation command in the sbatch script, which will recompile the program every time the job is submitted. If any changes are made to the source code, compile the source code separately, and then submit the production run as a batch-job.

Compilers

You can compile a C++ code using either a GNU or an Intel compiler.

GNU compiler

To use C++ with gcc on the FASRC clusters, load gcc compiler via our module system. For example, this command will load the latest gcc version:

module load gcc

If you need a specific version of R, you can search with the command

module spider gcc

To load a specific version

module load gcc/10.2.0-fasrc01

For more information on modules, see the Lmod Modules page.

To compile a code using a specific version of the GNU compiler and the O2 optimization flag, you can do the following:

module load gcc 
g++ -O2 -o sum.x sum.cpp

Intel compiler

To use C++ with Intel on the FASRC clusters, load intel compiler via our module system. For example, this command will load the latest intel version:

module load intel

If you need a specific version of R, you can search with the command

module spider intel

To load a specific version

module load intel/24.0.1-fasrc01

For more information on modules, see the Lmod Modules page.

Intel recommendations and notes

  • Intel released Intel OneAPI 23.2 with icpx, however, this version does not contain all the features, so we highly recommend using Intel 24 for icpx
  • Intel 17 is quite old. Avoid using it as it can have many incompatibilities with the current operating system
  • Intel has changed its compiler in the past few years and each module may need different flags. Below is a table of executables and possible flags
Intel module versionCommandAdditional flag
intel/17.0.4-fasrc01icpc-std=gnu++98
intel/23.0.0-fasrc01icpc
intel/23.2.0-fasrc01icpx
intel/24.0.1-fasrc01icpx

To compile using a specific version of the Intel compiler, execute:

module load intel/24.0.1-fasrc01
icpx -O2 -o sum.x sum.cpp

Examples

FASRC User Codes

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