The focus is on advanced programming with MPI and OpenMP. The course addresses participants who have already some experience with C/C++ or Fortran and MPI and OpenMP, the most popular programming models in high performance computing (HPC).
The course will teach newest methods in MPI-3.0/3.1/4.0 and OpenMP-4.5 and 5.0, which were developed for the efficient use of current HPC hardware. Topics with MPI are the group and communicator concept, process topologies, derived data types, the new MPI-3.0 Fortran language binding, one-sided communication and the new MPI-3.0 shared memory programming model within MPI. Topics with OpenMP are the OpenMP-4.0/4.5/5.0 extensions, as the vectorization directives, thread affinity and OpenMP places. (The GPU programming with OpenMP directives is not part of this course.) The course also contains performance and best practice considerations.
Hands-on sessions (in C and Fortran) will allow users to immediately test and understand the taught constructs of the Message Passing Interface (MPI) and the shared memory directives of OpenMP. Most MPI exercises will (in addition to C and Fortran) also be available for Python + mpi4py + numpy.
This course provides scientific training in Computational Science, and in addition, the scientific exchange of the participants among themselves. It is organized by JSC in cooperation with HLRS.
Online course Organizer: JSC Forschungszentrum Jülich, Germany
Nov 28, 2022
Nov 30, 2022
Online by JSC
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Unix / C or Fortran / familiar with the principles of MPI, e.g., to the extent of the introductory course MPI and OpenMP, i.e., at least the MPI process model, blocking point-to-point message passing and collective communication, and the single program concept of parallelizing applications, and for the afternoon session of the last day, to be familiar with OpenMP 3.0.
To be able to do the hands-on exercises of this course, you need a computer with an OpenMP capable C/C++ or Fortran compiler and a corresponding, up-to-date MPI library (in case of Fortran, the mpi_f08 module is required). Please note that the course organizers will not grant you access to an HPC system nor any other compute environment. Therefore, please make sure to have a functioning working environment / access to an HPC cluster prior to the course.
In addition, you can perform most MPI exercises in Python with mpi4py + numpy. In this case, an appropriate installation on your system is required (together with a C/C++ or Fortran installation for the other exercises).
Please tar -xvzf TEST.tar.gz using https://fs.hlrs.de/projects/par/events/TEST.tar.gz or unzip TEST.zip using https://fs.hlrs.de/projects/par/events/TEST.zip and verify your MPI and OpenMP installation with the tests described in TEST/README.txt within the archive.
The exercise about race-condition detection (at the end of the course) is optional. It would require an installation of a race-condition detection tool, e.g., the freely available Intel Inspector together with the Intel compiler. They can be installed with the Intel(R) oneAPI Base Toolkit plus the Intel(R) oneAPI HPC Toolkit.
Learn more about course curricula and content levels.
Dr. Rolf Rabenseifner (Stuttgart)
A few days before the course starts, you will receive pdf files from the slides and tar/zip files for installing the exercises on your system.
An older version of this course with most of the material (including the audio information) can also be viewed in the online Parallel Programming Workshop.
A detailed program can be found here (PDF) (preliminary).
Register via the button at the top of this page.
Thomas Breuer, phone 02461 61-96742, t.breuer(at)fz-juelich.de
https://www.hlrs.de/training/2022/JSC and course page at JSC: https://www.fz-juelich.de/en/ias/jsc/news/events/training-courses/2022/adv-mpi-2022
See the training overview and the Supercomputing Academy pages.
Online by VSC Vienna
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High-Performance Computing Center Stuttgart
Nobelstraße 19, 70569 Stuttgart, Germany
+49 (0) 711 / 685-87 209
A member of the Gauss Centre for Supercomputing, HLRS is one of three German national centers for high-performance computing.
HLRS is a central unit of the University of Stuttgart.