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PPCES 2023

Monday, 13. March ► 9:00 - Friday, 17. March ► 17:00


Parallel Programming in Computational Engineering and Science (PPCES) 2023

Logo PPCES Event


This one week event will continue the tradition of previous annual week-long events that take place in Aachen every spring since 2001. We will cover the basics of parallel programming using OpenMP and MPI in Fortran and C/C++ and a first step towards performance tuning as well as current topics in AI/machine learning. Hands-on exercises for each topic will be included.


This is an in-person event and will be held on the RWTH IT-Center premises (see below).


The contents of the courses are generally applicable but will be specialized towards CLAIX the compute cluster which is the current system installed at the RWTH’s IT Center. It might be helpful to read through the information which was provided during the HPC introduction on March 7 this year. This is especially true if you want to actively use CLAIX after this event.

OpenMP is a widely used approach for programming shared memory architectures, supported by most compilers nowadays. We will cover the basics of the programming paradigm as well as some advanced topics such as programming NUMA machines. The nodes of the RWTH Compute Cluster contain an increasing number of cores and thus we consider shared memory programming a vital alternative for applications that cannot be easily parallelized with MPI. We also expect a growing number of application codes to combine MPI and OpenMP for clusters of nodes with a growing number of cores.

The Message Passing Interface (MPI) is the de-facto standard for programming large HPC systems. We will introduce the basic concepts and give an overview of some advanced features. Also covered is hybrid parallelization, i.e., the combination of MPI and shared memory programming, which is gaining popularity as the number of cores per cluster node grows.

Machine Learning: We provide an overview to end-to-end deep learning with the latest version of Tensorflow/Keras. It covers the basic concepts to define models with Keras  and data pipelines with Tensorflow’s “Dataset”, and to visualize the results with Tensorboard while training. If training on one node or GPU is not enough,  we show how to scale up/out distributed training onto multiple compute nodes  and GPUs with Horovod. Furthermore, we provide an introduction to scikit-learn, with an overview of  different machine learning algorithms it provides and how to utilize it on GPUs  with H2O4GPU. There will also be a small section on PyTorch. The training courses consist of a hands-on exercises to be run directly on  RWTH infrastructure.

Guest Speaker

We are happy to announce two guest speakers:


Day 1+2: OpenMP

Monday, March 13 Day 1: OpenMP Part I
09:00 – 09:10 Organization PPCES 2023 Philipp Martin
09:10 – 10:30 OpenMP Basics part 1 Christian Terboven
11:00 – 12:00 OpenMP Basics part 2 (incl. Lab) Christian Terboven
13:30 – 15:00 OpenMP Basics part 3 (incl. Lab) Christian Terboven
15:30 – 16:30 OpenMP Basics part 4 (incl. Lab) Christian Terboven
Tuesday, March 14 Day 2: OpenMP Part II
09:00 – 10:30 Speed up Your OpenMP Application Without Doing Much Ruud van der Pas
11:00 – 12:00 OpenMP SIMD Christian Terboven
13:30 – 15:00 OpenMP Advanced Tasking (incl. Lab) Christian Terboven
15:15 – 16:15 OpenMP for Accelerators Christian Terboven

Day 3+4: MPI

Wednesday, March 15 Day 3: MPI Part I
09:30 – 11:00 Introduction to MPI Marc-André Hermanns
11:30 – 12:30 Blocking Point-to-Point Communication I Marc-André Hermanns
14:00 – 15:30 Blocking Point-to-Point Communication II Marc-André Hermanns
16:00 – 17:00 Non-blocking Point-to-Point Communication Marc-André Hermanns


Thursday, March 16 Day 4: MPI Part II
09:30 – 11:00 Derived Datatypes Marc-André Hermanns
11:30 – 12:30 Blocking Collective Communication Marc-André Hermanns
14:00 – 15:30 Communicator Basics Marc-André Hermanns
16:00 – 17:00 Hybrid Programming Marc-André Hermanns

Day 5: Machine Learning

Friday, March 17 Day 5: Machine Learning
09:00 – 09:45 Introduction to sci-kit learn Georg Zitzlsberger
09:45 – 10:00 Getting started on the Cluster Jannis Klinkenberg
10:15 – 11:00 Hands-on scikit-learn examples Georg Zitzlsberger
11:00 – 12:00 Introduction to Deep Neural Networks Georg Zitzlsberger
13:00 – 14:00 Tensorflow/Keras Exercises (short intro + Hands-on exercise)

  • Define Data Pipeline with Dataset
  • Build a Model
  • Train & Visualize with Tensorboard
Georg Zitzlsberger
14:15 – 15:30 Multi-GPU with Horovod (incl. short Hands-on) Georg Zitzlsberger
15:30 – 16:00 Q & A Georg Zitzlsberger

Material Downloads


Attendees should be comfortable with C/C++ or Fortran programming in a Linux environment and interested in learning about the technical details of parallelization and application tuning.

Participants of Part 3 (Machine Learning) will require basic knowledge of Python.

Gained Skills

Participants will learn about parallelization and application tuning in the context of High Performance Computing.


Registration is now closed, but was available here.


This event is supported by NEC and EuroCC



Monday, 13. March ► 9:00
Friday, 17. March ► 17:00


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Seminarraum 003/004, IT Center
Kopernikusstraße 6
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