NHR@FAU Course Program in Spring 2024

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We are happy to announce the NHR@FAU course program for Spring 2024.

  • Parallel Programming of High-Performance Systems. Three-day on-site course at NHR@FAU, February 20–22.
    This introductory course, a collaboration of NHR@FAU and Leibniz Supercomputing Center (LRZ), is targeted at students and scientists with interest in programming programming modern HPC clusters on all scales. We give introductions to general computer architecture, parallel programming with MPI and OpenMP, and the basics of profiling.
  • Fundamentals of Accelerated Computing with CUDA C/C++. Full-day online course, February 29.
    The course covers fundamental tools and techniques for GPU-accelerated C/C++ applications with CUDA. The course and the hands-on exercises are part of the NVIDIA DLI program.
  • Fundamentals of Accelerated Computing with CUDA Python. Full-day online course, March 14.
    The course covers fundamental tools and techniques for GPU-accelerated Python applications with CUDA and Numba. The course and the hands-on exercises are part of the NVIDIA DLI program.
  • Introduction to Parallel Programming with OpenMP, Part 1. Full-day online course, March 5.
    OpenMP is a standard for parallelizing shared-memory C/C++ and Fortran applications. It is supported by major compilers and provides a simple, low-entry barrier for thread-based parallelization. This course with hands-on exercises gives an introduction to the basic workings and constructs used for parallelizing applications with OpenMP.
  • Introduction to Parallel Programming with OpenMP, Part 2. Full-day online course, March 12.
    OpenMP is a standard for parallelizing shared-memory C/C++ and Fortran applications. It is supported by major compilers and provides a simple, low-entry barrier for thread-based parallelization. This course with hands-on exercises introduces advanced topics for parallelizing applications with OpenMP, including thread and memory locality, tasking, SIMD, and accelerator offloading.
  • Performance Analysis on GPUs with NVIDIA Tools. Half-day online course, March 19.
    This course introduces NVIDIA’s profiler as a tool to spot common performance bugs that arise when porting code to GPUs. Attendees will be able to follow along the demos and conduct their own experiments on the NHR@FAU GPU cluster.
  • Multi-GPU Programming with CUDA C++ Part 1: Accelerating CUDA C++ Applications with Multiple GPUs. Full-day online course, April 5.
    This course covers techniques to accelerate single-GPU CUDA applications by overlapping computation with data transfers as well as by employing multiple GPUs within one compute node. The course and the hands-on exercises are part of the NVIDIA DLI program.
  • Multi-GPU Programming with CUDA C++ Part 2: Scaling CUDA C++ Applications to Multiple Nodes. Full-day online course, April 10.
    This course covers techniques to scale existing CUDA applications to multiple GPU-enabled nodes. The course and the hands-on exercises are part of the NVIDIA DLI program.
  • Introduction to Parallel Programming with MPI. Two-day online course, April 11/12.
    This course gives an introduction to the Message Passing Interface (MPI), the dominating distributed-memory programming paradigm in High Performance Computing.

Please click on the links for more information and registration. For a full overview, please have a look at: NHR@FAU Tutorials and Courses

 

Dr. Georg Hager

Head of Training & Support

Erlangen National High Performance Computing Center (NHR@FAU)
Training & Support Division