| # | Exercise | Link | Solution |
|---|---|---|---|
| 1 | NumPy Intro: ndarray Basics |
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| 2 | NumPy Linear Algebra: SVD Reconstruction | ||
| 3 | NumPy to CuPy: ndarray Basics |
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| 4 | NumPy to CuPy: SVD Reconstruction | ||
| 5 | Memory Spaces: Power Iteration | ||
| 6 | Asynchrony: Power Iteration | ||
| 7 | Kernel Authoring: Copy | ||
| 8 | MPI | ||
| 9.1 | nvmath-python: Interoperability with CPU and GPU tensor libraries |
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| 9.2 | nvmath-python: Kernel fusion |
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| 9.3 | nvmath-python: stateful APIs: Amortizing task preparation costs |
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| 9.4 | nvmath-python: scaling to many GPUs |
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