Index

Dr. Sebastian Kuckuk

Sebastian holds a PhD in computer science from FAU, where he currently works as a researcher. His primary interests revolve around enhancing performance portability and programmer productivity through the use of domain-specific programming languages, code generation techniques, automatic parallelization, and GPU programming. He applies these methodologies in the context of massively parallel numerical solvers for computational fluid applications and other related fields.

In 2021, Sebastian joined NHR@FAU as a liaison scientist for the Chair of System Simulation. He simultaneously became a member of the Training and Support division, which he joined as a full member in spring of 2024. In addition, he serves as an NVIDIA Deep Learning Institute (DLI) university ambassador and is certified to teach DLI courses covering both introductory and advanced concepts in GPU programming. Sebastian contributes his expertise by engaging in smaller project-driven consultation activities, conducting lecture exercises, and delivering single- and multi-day tutorials.

During his free time, Sebastian finds pleasure in cooking and biking. He also pursues his interests in learning the Japanese language and exploring topics in psychology and philosophy.

Sebastian provides support and consulting for KONWIHR and NHR projects revolving around GPU programming, performance analysis and optimization.

Sebastian completed multiple courses offered by the NVIDIA Deep Learning Institute (DLI) on GPU programming and CUDA-accelerated applications that scale across multiple GPUs. He attained certification as DLI ambassador and was certified to teach the following courses:

  • Fundamentals of Accelerated Computing with CUDA C/C++
  • Fundamentals of Accelerated Computing with CUDA Python
  • Accelerating CUDA C++ Applications with Multiple GPUs
  • Scaling CUDA C++ Applications to Multiple Nodes

A list of upcoming and past courses can be found here.

Sebastian contributes his expertise to the following lectures:

  • High-End Simulation in Practice (HESP)
  • Programming Techniques for Supercomputers (PTfS)

Sebastian is a maintainer and developer of

  • the ExaStencils code generation framework for massively parallel multigrid solvers, and
  • the GHODDESS module for quadrature free higher-order discretizations of the shallow water equations.

Further information can be found on the official page https://www.exastencils.fau.de/ .

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2013

Automatic Code Generation for Massively Parallel Applications in Computational Fluid Dynamics

An open access version of the thesis is available here (PDF).

Abstract

Solving partial differential equations (PDEs) is a fundamental challenge in many application domains in industry and academia alike. With increasingly large problems, efficient and highly scalable implementations become more and more crucial. Today, facing this challenge is more difficult than ever due to the increasingly heterogeneous hardware landscape. One promising approach is developing domain‐specific languages (DSLs) for a set of applications. Using code generation techniques then allows targeting a range of hardware platforms while concurrently applying domain‐specific optimizations in an automated fashion. The present work aims to further the state of the art in this field. As domain, we choose PDE solvers and, in particular, those from the group of geometric multigrid methods. To avoid having a focus too broad, we restrict ourselves to methods working on structured and patch‐structured grids.

We face the challenge of handling a domain as complex as ours, while providing different abstractions for diverse user groups, by splitting our external DSL ExaSlang into multiple layers, each specifying different aspects of the final application. Layer 1 is designed to resemble LaTeX and allows inputting continuous equations and functions. Their discretization is expressed on layer 2. It is complemented by algorithmic components which can be implemented in a Matlab‐like syntax on layer 3. All information provided to this point is summarized on layer 4, enriched with particulars about data structures and the employed parallelization. Additionally, we support automated progression between the different layers. All ExaSlang input is processed by our jointly developed Scala code generation framework to ultimately emit C++ code. We particularly focus on how to generate applications parallelized with, e.g., MPI and OpenMP that are able to run on workstations and large‐scale cluster alike.

We showcase the applicability of our approach by implementing simple test problems, like Poisson’s equation, as well as relevant applications from the field of computational fluid dynamics (CFD). In particular, we implement scalable solvers for the Stokes, Navier‐Stokes and shallow water equations (SWE) discretized using finite differences (FD) and finite volumes (FV). For the case of Navier‐Stokes, we also extend our implementation towards non‐uniform grids, thereby enabling static mesh refinement, and advanced effects such as the simulated fluid being non‐Newtonian and non‐isothermal.

Jan Laukemann

Short bio

Jan Laukemann is a PhD student at the University of Erlangen-Nürnberg (FAU) Erlangen and works for the National High Performance Computing Center (NHR@FAU). Previously he finished his Master’s at FAU and worked as a Research Scientist at Intel Parallel Computing Labs (Intel PCL). He focuses on application optimization and performance engineering for HPC systems and novel algorithms for scalable linear algebra, tensor decomposition and graph computations. His research interests primarily include x86 and non-x86 computer architectures, their performance behavior on the node level, and vectorization techniques. He is the main developer of the Open Source Architecture Code Analyzer (OSACA), a static in-core kernel analysis tool, and is part of the organization committee of the annual HPC-AI Advisory Council Student Cluster Competition at ISC High Performance.

Research fields

List of publications

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List of activities

2023

2021

Dr. Alireza Ghasemi

Alireza was born in Iran, where he also completed his undergraduate studies in physics. In 2008, he received his PhD in computational physics from Basel University in Switzerland. After that, he continued his research in atomic-scale material modeling in different roles. In 2019, he moved to Paderborn and subsequently worked as an Alexander Von Humboldt Senior Fellow. On February 1, Alireza Ghasemi joined the NHR@FAU Training and Support division. He will support our customers as an expert for atomistic simulations and quantum chemistry. He has contributed to several software packages in this area and is familiar with high performance computing both as a user and as an administrator.

Sebastian Wind

Sebastian is a PhD candidate and technology professional with a profound background in enterprise computing and AI research. Before joining NHR, he specialized in IBM Systems during his time at Datev and collaborated with the Pattern Recognition Lab on large language model (LLM) initiatives. Currently, Sebastian provides NHR training and support for HPC AI users, leveraging his AI training expertise and driving innovation in high-performance computing.

Rasa Mabande

Rafael Ravedutti Lucio Machado

Aditya Ujeniya

Jairo Andres Buitrago Franco

Michael Panzlaff

Martin Mayr


From August 2019 and October 2024, Martin was a doctoral researcher in the Computer Vision group at the Pattern Recognition Lab (PRL) of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). During this time, he developed innovative approaches for image segmentation, retrieval, generation, text recognition, and metadata extraction, contributing to multiple research projects. His work also included advanced retrieval models for document analysis and pioneering methods for text generation, imitation (Best Student Paper Award at ICDAR 2021), and recognition (Best Poster Award at ICDAR 2023). For a complete overview of his research, find the publications listed below.

In January 2025, Martin moved to NHR Erlangen, where he specializes in AI-focused high-performance computing solutions, providing training, custom workflows, and user support to researchers across diverse scientific disciplines.

  • 01/2025 – current:
    Training & User Support AI at NHR Erlangen
  • 08/2019 – 10/2024:
    Ph.D. Student at the Pattern Recognition Lab
  • 10/2016 – 07/2019:
    M.Sc. in Computer Science:
    Friedrich-Alexander University Erlangen-Nürnberg
  • 03/2012 – 07/2016:
    B.Sc. in Business Information Technology:
    Regensburg University of Applied Sciences

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2020

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2019

Conference Contributions