Höchstleistungsrechenzentrum Stuttgart: Introduction to OpenMP Offloading with AMD GPUs

Prerequisites and content levels

Prerequisites

Basic experience in OpenMP programming, e.g. by attending the Parallel Programming Workshop. Participants should have an application developer's general knowledge of computer hardware, operating systems, and be familiar with C/C++ or Fortran.

See also the suggested prereading below (resources and public videos).

Content levels

Basic: 1 hours
Intermediate: 7 hours
Advanced: 6 hours

Learn more about course curricula and content levels

Instructors

Michael Klemm et al.

Agenda (subject to change)

All times are CEST.

AMD Programming Model, OpenMP and MPI

08:45 - 09:00 Drop in to Zoom

HLRS Intro
Introduction to GPU and APU
Introduction to OpenMP offload using unified shared memory
Introduction to OpenMP offload with explicit data management
Asynchronous offloading
Tools for performance anaylsis and optimizations
Hands-on with porting a small app

Registration information

Register via the button at the top of this page (will be available soon).

Fees

This course is free of charge.

Resources for additional reading

Book on HIP programming - Porting CUDA
- Accelerated Computing with HIP, Yifan Sun, Trinayan Baruah, David R Kaeli,
  ISBN-13: ‎ 979-8218107444
Book on OpenMP GPU programming
- Programming Your GPU with OpenMP, Tom Deakin and Tim Mattson,
  ISBN-13: ‎ 978-0262547536
Book of parallel and high performance computing topics
- Parallel and High Performance Computing, Manning Publications, Robert Robey and Yuliana Zamora,
  ISBN-13: ‎ 978-0262547536
ENCCS resourses
- Developing Applications with the AMD ROCm
AMD Lab Notes series on GPUOpen.com
- Finite difference method - Laplacian part 1
- Finite difference method - Laplacian part 2
- Finite difference method - Laplacian part 3
- Finite difference method - Laplacian part 4
- AMD matrix cores
- Introduction to profiling tools for AMD hardware
- AMD ROCm™ installation
- AMD Instinct™ MI200 GPU memory space overview
- Register pressure in AMD CDNA2™ GPUs
- GPU-Aware MPI with ROCm
- Creating a PyTorch/TensorFlow Code Environment on AMD GPUs
- Jacobi Solver with HIP and OpenMP offloading
- Sparse matrix vector multiplication - part 1
Quick start guides at Oak Ridge National Laboratory
- Crusher quick-start guide
- Frontier user guide

Contact

Khatuna Kakhiani phone 0711 685 65796, training(at)hlrs.de
Tobias Haas phone 0711 685 87223, training(at)hlrs.de

HLRS Training Collaborations in HPC

HLRS is part of the Gauss Centre for Supercomputing (GCS), together with JSC in Jülich and LRZ in Garching near Munich. EuroCC@GCS is the German National Competence Centre (NCC) for High-Performance Computing. HLRS is also a member of the Baden-Württemberg initiative bwHPC.

Further courses

See the training overview and the Supercomputing Academy pages.