Today's HPC platforms leverage GPU technologies from NVIDIA and AMD to maximize compute capabilities for advanced scientific and research applications. Developers utilize MPI in combination with CUDA, HIP, OpenMP and other parallel languages and must deal with the complexities of running code both on the CPU and GPU. Debugging these mixed environments can be a real challenge, especially when dealing with thousands of nodes and GPUs at a time.
This video highlights GPU debugging on each of these architectures with the TotalView for HPC debugger.
In this webinar you will learn:
- How CUDA debugging on NVIDIA GPUs compares with HIP debugging on AMD GPUs
- Significant architecture and terminology differences between the GPU environments
- How to easily debug multi-node and multi-GPU code in the same session
- What is the state of debugging OpenMP on GPUs
- How to combine debugging features to efficiently debug tough parallel problems
Learning how the TotalView for HPC debugger helps in each of these areas will enable you to understand your code faster, find bugs in your code quicker, and improve the quality of your code.
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