7 HPC user libraries #

To install Lmod, run zypper in lua-lmod.

Before you can use Lmod, you must source an init file into the initialization file of your interactive shell. The following init files are available for various common shells:

/usr/share/lmod/lmod/init/bash
/usr/share/lmod/lmod/init/ksh
/usr/share/lmod/lmod/init/tcsh
/usr/share/lmod/lmod/init/zsh
/usr/share/lmod/lmod/init/sh

Pick the appropriate file for your shell, then add the following line into your shell's init file:

source /usr/share/lmod/lmod/init/INIT-FILE

The init script adds the command module.

To list all the available modules, run module spider. To show all modules which can be loaded with the currently loaded modules, run module avail. A module name consists of a name and a version string, separated by a / character. If more than one version is available for a certain module name, the default version is marked by a * character. If there is no default, the module with the highest version number is loaded. To reference a specific module version, you can use the full string NAME/VERSION.

module list shows all currently loaded modules. Refer to module help for some short help on the module command, and module help MODULE-NAME for help on the particular module. The module command is only available when you log in after installing lua-lmod.

To get information about a particular module, run module whatis MODULE-NAME. To load a module, run module load MODULE-NAME. This will ensure that your environment is modified (that is, the PATH and LD_LIBRARY_PATH and other environment variables are prepended) so that binaries and libraries provided by the respective modules are found. To run a program compiled against this library, the appropriate module load commands must be issued beforehand.

The module load MODULE command must be run in the shell from which the module is to be used. Some modules require a compiler toolchain or MPI flavor module to be loaded before they are available for loading.

If the respective development packages are installed, build-time environment variables like LIBRARY_PATH, CPATH, C_INCLUDE_PATH, and CPLUS_INCLUDE_PATH are set up to include the directories containing the appropriate header and library files. However, some compiler and linker commands might not honor these. In this case, use the appropriate options together with the environment variables -I PACKAGE_NAME_INC and -L PACKAGE_NAME_LIB to add the include and library paths to the command lines of the compiler and linker.

For more information on Lmod, see https://lmod.readthedocs.org.

This package requires lua-lmod to supply environment module support.

To install gnu-compilers-hpc, run the following command:

> sudo zypper in gnu-compilers-hpc

To make libraries built with the base compilers available, you must set up the environment appropriately and select the GNU toolchain. To do so, run the following command:

> module load gnu

To use the GNU compiler collection to build your own libraries and applications, gnu-compilers-hpc-devel must be installed. It ensures that all compiler components required for HPC (that is, C, C++, and Fortran compilers) are installed.

The environment variables CC, CXX, FC and F77 will be set correctly and the path will be adjusted so that the correct compiler version can be found.

The Development Tools Module might provide later versions of the GNU compiler suite. To determine the available compiler suites, run the following command:

> zypper search '*-compilers-hpc'

If you have more than one version of the compiler suite installed, Lmod picks the latest one by default. If you require an older version, or the base version, append the version number:

> module load gnu/7

For more information, see Section 7.1, “Lmod — Lua-based environment modules”.

NumPy is a general-purpose array-processing package designed to efficiently manipulate large multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional arrays.

NumPy is built on the Numeric code base and adds features introduced by the discontinued NumArray project, as well as an extended C API, and the ability to create arrays of arbitrary type, which also makes NumPy suitable for interfacing with general-purpose database applications.

There are also basic facilities for discrete Fourier transform, basic linear algebra, and random number generation.

This package is available both for Python 2 and 3. The specific compiler toolchain module must be loaded for this library. The correct library module for the Python version used needs to be specified when loading this module. To load this module, run the following command:

> module load TOOLCHAIN pythonVERSION-numpy

For information about the toolchain to load see: Section 7.2, “GNU Compiler Toolchain Collection for HPC”.

List of master packages:

SciPy is a collection of mathematical algorithms and convenience functions built on the NumPy extension of Python. It provides high-level commands and classes for manipulating and visualizing data. With SciPy, an interactive Python session becomes a data-processing and system-prototyping environment.

This package is available both for Python 2 (up to version 1.2.0 only) and 3. The specific compiler toolchain modules must be loaded for this library. The correct library module for the Python version used must be specified when loading this module. To load this module, run the following command:

> module load TOOLCHAIN pythonVERSION-scipy

For information about the toolchain to load, see Section 7.2, “GNU Compiler Toolchain Collection for HPC”.

List of master packages:

The memkind library is a user-extensible heap manager built on top of jemalloc. It enables control over memory characteristics and a partitioning of the heap between kinds of memory. The kinds of memory are defined by operating system memory policies that have been applied to virtual address ranges. Memory characteristics supported by memkind without user extension include control of NUMA and page size features.

For more information, see:

This tool is only available for AMD64/Intel 64.

PMIx abstracts the internals of MPI implementations for workload managers and unifies the way MPI jobs are started by the workload manager. With PMIx, there is no need to use the individual MPI launchers on Slurm, because srun will take care of this. In addition, the workload manager can determine the topology of the cluster, so you do not need to specify topologies manually.

OpenBLAS is an optimized BLAS (Basic Linear Algebra Subprograms) library based on GotoBLAS2 1.3, BSD version. It provides the BLAS API. It is shipped as a package enabled for environment modules, so it requires using Lmod to select a version. There are two variants of this library: an OpenMP-enabled variant, and a pthreads variant.

The OpenMP variant covers the following use cases:

When linking statically, ensure that libgomp.a is included by adding the linker flag -lgomp.

The pthreads variant of the OpenBLAS library can improve the performance of single-threaded programs. The number of threads used can be controlled with the environment variable OPENBLAS_NUM_THREADS.

This module requires loading a compiler toolchain beforehand. To select the latest version of this module provided, run the following command:

For information about the toolchain to load, see Section 7.2, “GNU Compiler Toolchain Collection for HPC”.

List of master packages:

HDF5 is a data model, library, and file format for storing and managing data. It supports an unlimited variety of data types, and is designed for flexible and efficient I/O and for high-volume and complex data. HDF5 is portable and extensible, allowing applications to evolve in their use of HDF5.

There are serial and MPI variants of this library available. All flavors require loading a compiler toolchain module beforehand. The MPI variants also require loading the correct MPI flavor module.

To load the highest available serial version of this module, run the following command:

> module load TOOLCHAIN hdf5

When an MPI flavor is loaded, you can load the MPI version of this module by running the following command:

> module load TOOLCHAIN MPI_FLAVOR phdf5

For information about the toolchain to load, see Section 7.2, “GNU Compiler Toolchain Collection for HPC”. For information about available MPI flavors, see Section 7.5, “MPI libraries”.

List of master packages:

MPI_FLAVOR must be one of the supported MPI flavors described in Section 7.5, “MPI libraries”.

For general information about Lmod and modules, see Section 7.1, “Lmod — Lua-based environment modules”.

The Intel* MPI Benchmarks package provides a set of elementary benchmarks that conform to the MPI-1, MPI-2, and MPI-3 standards. You can run all of the supported benchmarks, or a subset specified in the command line, using a single executable file. Use command line parameters to specify various settings, such as time measurement, message lengths, and selection of communicators. For details, see the Intel* MPI Benchmarks User's Guide: https://software.intel.com/en-us/imb-user-guide.

For the IMB binaries to be found, a compiler toolchain and an MPI flavor must be loaded beforehand. To load this module, run the following command:

> module load TOOLCHAIN MPI_FLAVOR imb

For information about the toolchain to load, see Section 7.2, “GNU Compiler Toolchain Collection for HPC”. For information on available MPI flavors, see Section 7.5, “MPI libraries”.

PAPI provides a tool with a consistent interface and methodology for the performance counter hardware found in most major microprocessors.

This package works with all compiler toolchains and does not require a compiler toolchain to be selected. Load the latest version provided by running the following command:

> module load TOOLCHAIN papi

For information about the toolchain to load, see Section 7.2, “GNU Compiler Toolchain Collection for HPC”.

List of master packages:

For general information about Lmod and modules, see Section 7.1, “Lmod — Lua-based environment modules”.

mpiP is a lightweight profiling library for MPI applications. Because it only collects statistical information about MPI functions, mpiP generates considerably less overhead and much less data than tracing tools. All the information captured by mpiP is task-local. It only uses communication during report generation, typically at the end of the experiment, to merge results from all of the tasks into one output file.

For this library a compiler toolchain and MPI flavor must be loaded beforehand. To load this module, run the following command:

> module load TOOLCHAIN MPI_FLAVOR mpip

For information about the toolchain to load, see Section 7.2, “GNU Compiler Toolchain Collection for HPC”. For information on available MPI flavors, see Section 7.5, “MPI libraries”.

List of master packages:

MPI_FLAVOR must be one of the supported MPI flavors described in Section 7.5, “MPI libraries”.