The course provides a structured, end-to-end approach for migrating GPU-accelerated applications from NVIDIA CUDA to the AMD ROCm™ platform using the Heterogeneous-computing Interface for Portability (HIP) programming model. Participants gain a comprehensive understanding of the AMD RDNA™ GPU architecture, the ROCm software stack, HIP programming, and the tools required to port, debug, profile, and optimize real-world GPU workloads.
The course emphasizes architectural mapping between CUDA and HIP, practical migration workflows, performance optimization, and correctness validation to ensure a smooth and efficient transition to supported AMD GPU platforms.
The emphasis of this course is on:
- Understanding the AMD RDNA GPU architecture and single instruction, multiple threads (SIMT) execution model
- Exploring the ROCm platform and its open GPU computing ecosystem
- Optimizing memory access, synchronization, and kernel execution on RDNA GPUs
- Applying the HIP programming model for portable GPU development
- Mapping CUDA concepts, APIs, and toolchains to their HIP/ROCm equivalents
- Executing a structured CUDA-to-HIP migration workflow utilizing validation and optimization strategies
- Debugging GPU kernels using ROCgdb at wavefront and lane granularity
- Profiling GPU and system performance using ROCm profiling tools
What's New:
Added new module: Porting CUDA to the AMD ROCm Platform with AI-Assisted Skills
Added new lab: Porting CUDA to the AMD ROCm Platform with AI-Assisted Skills
