New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Integrate TrixiGPU.jl with Enzyme.jl for Differentiable Programming #43

Open

junyixu opened this issue Sep 17, 2024 · 1 comment

Labels

Collaborator

junyixu commented Sep 17, 2024

I propose integrating the GPU version of Trixi.jl with Enzyme.jl for differentiable programming.

Benefits:

Differentiable Programming: Allows for gradient-based optimizations in CFD simulations.
Unified Workflow: Provides a seamless experience for differentiable programming.

Note: Jacobian matrices computed on CPU and GPU may differ due to:

Precision Differences: GPUs often use lower precision (e.g., FP32) compared to CPUs (e.g., FP64), affecting numerical accuracy.
Parallelism Effects: GPUs handle parallel computations differently, potentially introducing slight numerical differences.

junyixu added the enhancement label

Collaborator

huiyuxie commented Sep 24, 2024 •

edited

Loading

Thanks for your advice @junyixu! You mentioned about Jacobian matrices - are they the only parts that could benefit from auto differentiation on GPU?

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment