Tech Exeter Conference: Intro to GPU Development in Python

Writing code for GPUs has come a long way over the last few years and it is now easier than ever to get started. You can even do it in Python! This talk will cover setting up your Python environment for GPU development. How coding for GPUs differs from CPUs, and the kind of problems GPUs excel at solving. We will dive into some real examples using Numba and also touch on a suite of Python Data Science tools called RAPIDS.

Session takeaways

* You don't need to learn C++ to develop on GPUs
* GPUs are useful for more than just machine learning
* hardware accelerators like GPUs are going to be more important than ever in order to scale our current workloads

Transcript

1. Jacob Tomlinson | Tech Exeter Conference 2020 Intro to GPU development in Python
3. 3 RAPIDS Github https://github.com/rapidsai
4. 4 GPU-Accelerated ETL The Average Data Scientist Spends 90+% of Their Time in ETL as Opposed to Training Models
5. 5 Lightning-fast performance on real-world use cases Up to 350x faster queries; Hours to Seconds! TPCx-BB is a data science benchmark consisting of 30 end-to-end queries representing real-world ETL and Machine Learning workflows, involving both structured and unstructured data. It can be run at multiple “Scale Factors”. ▸ SF1 - 1GB ▸ SF1K - 1 TB ▸ SF10K - 10 TB RAPIDS results at SF1K (2 DGX A100s) and SF10K (16 DGX A100s) show GPUs provide dramatic cost and time-savings for small scale and large-scale data analytics problems ▸ SF1K 37.1x average speed-up ▸ SF10K 19.5x average speed-up (7x Normalized for Cost)
6. 6 Dask DEPLOYABLE ▸ HPC: SLURM, PBS, LSF, SGE ▸ Cloud: Kubernetes ▸ Hadoop/Spark: Yarn PYDATA NATIVE ▸ Easy Migration: Built on top of NumPy, Pandas Scikit-Learn, etc ▸ Easy Training: With the same APIs ▸ Trusted: With the same developer community EASY SCALABILITY ▸ Easy to install and use on a laptop ▸ Scales out to thousand node clusters POPULAR ▸ Most Common parallelism framework today in the PyData and SciPy community
7. 7 What are GPUs?
8. 8 Gaming Hardware For pwning n00bs
9. 9 Mysterious Machine Learning Hardware For things like GauGAN Semantic Image Synthesis with Spatially-Adaptive Normalization Taesung Park, Ming-Yu Liu, Ting-Chun Wang, Jun-Yan Zhu arXiv:1903.07291 [cs.CV]
10. 10 CPU GPU
11. 11 https://youtu.be/-P28LKWTzrI
12. 12 GPU vs CPU https://docs.nvidia.com/cuda/cuda-c-programming-guide/
13. 13 Using a GPU is like using two computers Icons made by Freepik from Flaticon Network SSH VNC RD SCP FTP SFTP Robocopy
14. 14 Using a GPU is like using two computers Icons made by Freepik from Flaticon PCI CUDA
15. 15 What is CUDA?
16. 16 CUDA
17. 17 I don’t write C/C++
18. 18 What does CUDA do? Construct GPU code with CUDA C/C++ language extensions Copy data from RAM to GPU Copy compiled code to GPU Execute code Copy data from GPU to RAM How do we run stuff on the GPU?
19. 19 Let’s do it in Python
22. 22 Writing a Kernel Differences between a kernel and a function • A kernel cannot return anything, it must instead modify memory • A kernel must specify its thread hierarchy (threads and blocks) A kernel is a GPU function
23. 23 Threads, blocks, grids and warps https://docs.nvidia.com/cuda/cuda-c-programming-guide/
24. 24 What? Rules of thumb for threads per block: • Should be a round multiple of the warp size (32) • A good place to start is 128-512 but benchmarking is required to determine the optimal value.
25. 25 Imports
26. 26 Data arrays
27. 27 Example kernel
28. 28 Running the kernel
29. 29 Absolute positions
30. 30 Thread and block positions
31. 31 Example kernel (again)
32. 32 How did the GPU update our numpy array? If you call a Numba CUDA kernel with data that isn’t on the GPU it will be copied to the GPU before running the kernel and copied back after. This isn’t always ideal as copying data can waste time.
33. 33 Create a GPU array
34. 34 Simplified position kernel
35. 35 GPU Array
36. 36 Copy to host
37. 37 Higher level APIs
39. 39 cuDF
40. 40 cuDF cuIO Analytics Data Preparation Visualization Model Training cuML Machine Learning cuGraph Graph Analytics PyTorch, TensorFlow, MxNet Deep Learning cuxfilter, pyViz, plotly Visualization Dask GPU Memory RAPIDS End-to-End GPU Accelerated Data Science
41. 41 Interoperability for the Win DLPack and __cuda_array_interface__ mpi4py
42. 42 __cuda_array_interface__
43. 43 Recap
44. 44 Recap GPUs run the same function (kernel) many times in parallel When being called each function gets a unique index CUDA/C++ is used to write kernels, but high level languages like Python can also compile to it Memory must be copied between the CPU (host) and GPU (device) Many familiar Python APIs have GPU accelerated implementations to abstract all this away Takeaways on GPU computing
45. THANK YOU Jacob Tomlinson @_jacobtomlinson