{"id":7331,"date":"2025-01-21T10:14:55","date_gmt":"2025-01-21T09:14:55","guid":{"rendered":"https:\/\/blog.rwth-aachen.de\/itc-events\/?post_type=tribe_events&p=7331"},"modified":"2026-01-16T14:09:46","modified_gmt":"2026-01-16T13:09:46","slug":"ppces-2025","status":"publish","type":"tribe_events","link":"https:\/\/blog.rwth-aachen.de\/itc-events\/en\/event\/ppces-2025\/","title":{"rendered":"PPCES 2025"},"content":{"rendered":"

in cooperation with<\/p>\n\n\n\n
\"HPC.NRW<\/a><\/th>\n\"European<\/a><\/th>\n\"KI-Servicezentrum<\/a><\/th>\n<\/tr>\n<\/tbody>\n<\/table>\n

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Parallel Programming in Computational Engineering and Science (PPCES)<\/h1>\n

March 10th – 14th, 2025<\/h1>\n

\"Logo<\/p>\n

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This one week event will continue the tradition of previous annual week-long events<\/a> 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. Furthermore, we will embrace current topics in machine & deep learning. Hands-on exercises for each topic will be included.<\/p>\n

The contents of the courses are generally applicable but will be specialized towards the compute cluster CLAIX which is the current system installed at RWTH\u2019s IT Center. It might be helpful to read through the information which is provided during the HPC introduction on March 4th 2025.<\/a> This is especially true if you want to actively use CLAIX after this event.<\/span><\/span><\/p>\n

Topics<\/h2>\n

The Message Passing Interface (MPI)<\/strong> is the de-facto standard for programming large HPC systems. We will introduce the basic concepts and give an overview of some advanced features.<\/p>\n

OpenMP<\/strong> 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 compute nodes of the RWTH HPC Cluster (aka CLAIX) 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.<\/p>\n

Machine & Deep Learning<\/strong>: We provide a fundamental introduction into machine and deep learning approaches as well as data processing techniques that support dataset preparation or model selection for training and inference. It covers the basic concepts for supervised and unsupervised learning such as classification, regression, and clustering to get a feeling which technique is appropriate for a particular problem. Additionally, we conduct several hands-on exercises with common frameworks such as scikit-learn and PyTorch on our recent HPC Cluster CLAIX. In these exercises, we demonstrate how to define models, construct dataset and training pipelines or utilize monitoring and visualization of the training results, e.g., with Tensorboard. For the deep learning exercises, we start by training on a single GPU first. If that is not enough, we also show how to scale up distributed training onto multiple compute nodes or GPUs.<\/p>\n

Organization<\/h2>\n