Julia has several packages for programming GPUs, each of which support various programming models. In this workshop, we will demonstrate the use of three major GPU programming packages: CUDA.jl for NVIDIA GPUs, AMDGPU.jl for AMD GPUs, and oneAPI.jl for Intel GPUs. We will explain the various approaches for programming GPUs with these packages, ranging from generic array operations that focus on ease-of-use, to hardware-specific kernels for when performance matters.

Most of the workshop will be vendor-neutral, and the content will be available for all supported GPU back-ends. There will also be a part on vendor-specific tools and APIs.

Attendees will be able to follow along, but are recommended to have access to a suitable GPU for doing so.

Materials
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Contents
00:00 Welcome! ???
00:24 Welcome ???
01:20 Outline
02:44 JuliaGPU packages
04:08 JuliaGPU back-ends
05:34 GPU Architecture
07:25 Parallel programming models
08:55 Follow along and links to notebooks, JuliaHub
12:37 Start of tutorial with notebook
16:00 Array programming
28:20 Kernel programming
34:32 Parallel programming + questions
41:50 Question about `@cuprintln`
43:00 Question about threads in CPU vs GPU
45:08 Question: iterating over threads and blocks, ordering
48:03 Question of accuracy in CPU vs GPU
56:48 Question: array structure, "CUDA by Example" book recommendation
58:40 Profiling
1:01:50 Profiling: NVIDIA Nsight Systems: live example
1:11:00 Profiling: NVIDIA Nsight Compute: live example → optimize single kernel invocation
1:19:05 Common issues: unsupported array operations
1:21:50 Common issues: unsuppored kernel operations
1:25:45 Question: portability of optimizations
1:27:40 Parallel programming issues
1:31:55 Tour of accompanying GitHub repo
1:32:40 Case Study I: Image processing using AMDGPU
1:57:00 Break
2:01:30 Case Study II: Fun with arrays, Machine Learning
2:10:47 Case Study III: Random number generators
2:22:10 Kernel abstractions
2:42:10 Example: Solving heat equation with GPU
2:56:30 Sneak peek of Enzyme (automatic differentiation framework)
2:59:18 Questions and Future plans

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