Advanced GPU Computing training

This training focusses on advanced aspects of the Julia GPU computing stack, using CUDA.jl. It covers:

Advanced usage of CUDA.jl
- library integrations and wrappers (CUDA driver API, CUBLAS, etc)
- programming models (array abstractions, kernels)
- memory management
- task-based concurrent GPU computing
Performance deep-dive
- application analysis and optimization (using NSight Systems)
- kernel analysis and optimization (using NSight Compute)

Name		Name	Last commit message	Last commit date
Latest commit History 15 Commits
.gitignore		.gitignore
1-0-introduction.ipynb		1-0-introduction.ipynb
1-1-array_programming.ipynb		1-1-array_programming.ipynb
1-2-application_optimization.ipynb		1-2-application_optimization.ipynb
1-3-kernel_programming.ipynb		1-3-kernel_programming.ipynb
1-4-kernel_optimization.ipynb		1-4-kernel_optimization.ipynb
2-1-cuda_libraries.ipynb		2-1-cuda_libraries.ipynb
2-2-memory_management.ipynb		2-2-memory_management.ipynb
2-3-concurrent_computing.ipynb		2-3-concurrent_computing.ipynb
Manifest.toml		Manifest.toml
Project.toml		Project.toml
README.md		README.md
julia_nsys		julia_nsys

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