There is no single best way to port code to GPUs. Examine the various approaches described in this course to determine which is the best match for your research team’s goals, expertise, existing codebase, and long-term maintenance needs.
Developed in collaboration with NVIDIA and UCL, 1st Step in GPU Programming provides a practical introduction to GPU programming for High-Performance Computing (HPC) environments. By combining Jupyter notebooks, worked examples, and self-assessment activities, the course will help build your confidence through reinforced learning and hands-on practical experience.
Gain a solid understanding of the AMD MI300 GPU architecture and the programming models available for AMD GPUs and APUs.
MI300 Architecture is ideal for researchers and developers looking to accelerate existing applications using a practical and accessible approach to GPU programming within HPC environments.
Harness the power of GPU acceleration through a directive-based programming approach to offload your CPU workloads onto GPUs using a practical and accessible approach to GPU programming within High-Performance Computing (HPC) environments.
Directive-Based Programming for GPUs will teach you how to offload computationally intensive workloads to GPUs using OpenMP. Through carefully designed content and hands-on practical exercises, you'll learn how to improve application performance while minimising the complexity traditionally associated with GPU programming.
Explore the concepts and techniques required to develop efficient GPU software using a language-extension approach with Language-Extension Programming for GPUs.
Designed for researchers and developers looking to move beyond directive-based programming approaches and gain deeper control over GPU performance and optimisation on AMD hardware, this course builds on the introductory GPU programming course and provides a practical introduction to HIP for AMD GPU architectures.
