GPU Programming in Julia - Workshop

This workshop demonstrates GPU programming in Julia using CUDA.jl, AMDGPU.jl, and oneAPI.jl. It covers vendor-neutral array and kernel programming, profiling and optimization, common issues, and applications including image processing, machine learning, random number generation, and solving the heat equation.

Syllabus

Welcome!.
Welcome.
Outline.
JuliaGPU packages.
JuliaGPU back-ends.
GPU Architecture.
Parallel programming models.
Follow along and links to notebooks, JuliaHub.
Start of tuturial with notebook.
Array programming.
Kernel programming.
Parallel programming + questions.
Profiling.
Profiling: NVIDIA Nsight Systems: live example.
Profiling: NVIDIA Nsight Compute: live example → optimize single kernel invocation.
Common issues: unsupported array operations.
Common issues: unsuppored kernel operations.
Parallel programming issues.
Tour of accompanying Github repo.
Case Study I: Image processing using AMDGPU.
Break.
Case Study II: Fun with arrays, Machine Learning.
Case Study III: Random number generators.
Kernel abstractions.
Example: Solving heat equation with GPU.
Sneak peek of Enzyme (automatic differentiation framework).
Questions and Future plans.