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https://github.com/numba/nvidia-cuda-tutorial

Nvidia contributed CUDA tutorial for Numba
https://github.com/numba/nvidia-cuda-tutorial

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Nvidia contributed CUDA tutorial for Numba

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README 

        
# Numba for CUDA Programmers



Author: Graham Markall, NVIDIA .



## What is this course?



This is an adapted version of one delivered internally at NVIDIA - its primary

audience is those who are familiar with CUDA C/C++ programming, but perhaps less

so with Python and its ecosystem. That said, it should be useful to those

familiar with the Python and PyData ecosystem.



It focuses on using CUDA concepts in Python, rather than going over basic CUDA

concepts - those unfamiliar with CUDA may want to build a base understanding by

working through Mark Harris's [An Even Easier Introduction to

CUDA](https://developer.nvidia.com/blog/even-easier-introduction-cuda/) blog

post, and briefly reading through the [CUDA Programming

Guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html)

Chapters 1 and 2 (*Introduction* and *Programming Model*). Other concepts

discussed in the course (such as shared memory) are discussed in later chapters.

For expediency, it is recommended to look up concepts in those sections when

necessary, rather than reading all the reference material in detail.



## What is in this course?



The course is broken into 5 sessions, designed for a session to be presented

then the examples and exercises worked through before participants move to the

next session. This could be presented at a cadence of one session per week with

an hour of presentation time to fit the course around other tasks. Alternatively

it could be delivered as a tutorial session over the course of 2-3 days.



### Session 1: An introduction to Numba and CUDA Python



Session 1 files are in the [session-1](session-1) folder. Contents:



- *Presentation*: The presentation for this session, along with notes.

- *Mandelbrot example*: See [the README](session-1/mandelbrot/README.md) for 

  exercises.

- *CUDA Kernels notebook*: In the [exercises](session-1/exercises) folder. Open the 

  notebook using Jupyter.

- *UFuncs notebooks* In the [exercises](session-1/exercises) folder. Open the 

  notebooks using Jupyter. Contains two notebooks on `vectorize` and 

  `guvectorize` on the CPU (as it's a little easier to experiment with them on

  the CPU target) and one notebook on CUDA ufuncs and memory management.



### Session 2: Typing



Session 2 files are in the [session-2](session-2) folder. Contents:



- *Presentation*: The presentation for this session, along with notes.

- *Exercises*: In the [exercises](session-2/exercises) folder. Open the notebook

  using Jupyter.



### Session 3: Porting strategies, performance, interoperability, debugging



Session 3 files are in the [session-3](session-3) folder. Contents:



- *Presentation*: The presentation for this session, along with notes.

- *Exercises*: In the [exercises](session-3/exercises) folder. Open the notebook

  using Jupyter.

- *Examples*: In the [examples](session-3/examples) folder. These are mostly

  executable versions of the examples given in the slides.



### Session 4: Extending Numba



Session 4 files are in the [session-4](session-4) folder. Contents:



- *Presentation*: The presentation for this session, along with notes.

- *Exercises*: In the [exercises](session-4/exercises) folder. Open the notebook

  using Jupyter. A solution to the exercise is also provided.

- *Examples*: In the [examples](session-4/examples) folder. This contains a

  notebook working through the Interval example presented in the slides.



### Session 5: Memory Management



Session 5 files are in the [session-5](session-5) folder. Contents:



- *Presentation*: The presentation for this session, along with notes.

- *Exercises*: In the [exercises](session-5/exercises) folder. Open the notebook

  using Jupyter.

- *Examples*: In the [examples](session-5/examples) folder. This contains

  examples of a simple EMM Plugin wrapping cudaMalloc, and an EMM Plugin for

  using the CuPy pool allocator with Numba.



## Sources



Some of the material in this course is derived from various sources. These

sources, are:



- [PyData Amsterdam 2019 Numba

  Tutorial](https://github.com/ContinuumIO/pydata-amsterdam2019-numba). This

  material was used under the [Creative Commons Attribution 4.0 International

  license](https://github.com/ContinuumIO/pydata-amsterdam2019-numba/blob/c5944f1cf0a5244dcd43d690d3676ade19ce6e16/LICENSE).

  Minor edits were made to this source material to make the wording more

  consistent with the rest of this tutorial.

- [Numba tutorials I've previously presented](https://github.com/gmarkall/tutorials)

- [An example extending Numba's CUDA target](https://github.com/gmarkall/extending-numba-cuda)

- The Life of a Numba Kernel: [Notebook](https://github.com/gmarkall/life-of-a-numba-kernel/blob/master/Life%20of%20a%20Numba%20Kernel%20-%20with-%20output.ipynb) and [blog post](https://medium.com/rapids-ai/the-life-of-a-numba-kernel-a-compilation-pipeline-taking-user-defined-functions-in-python-to-cuda-71cc39b77625).



## References



The following references can be useful for studying CUDA programming in general,

and the intermediate languages used in the implementation of Numba:



- [The CUDA C/C++ Programming

  Guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html).

  Early chapters provide some background on the CUDA parallel execution model

  and programming model.

- [LLVM 7.0.0 Language reference

  manual](https://releases.llvm.org/7.0.0/docs/LangRef.html). Documents the

  instructions in LLVM IR for version 7, the current version used in Numba for

  CUDA.

- [NVVM IR Specification](https://docs.nvidia.com/cuda/nvvm-ir-spec/index.html).

  Provides details of additions to, and deviations from, the LLVM 7 IR

  specification.

- [PTX ISA

  documentation](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html).

  The Parallel Thread Execution ISA, which targets CUDA GPUs. This is a useful

  reference when dumping the assembly of CUDA code from Numba.