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I have not made it more public since there are still a few missing bits. For example we can currently only work on functions which are ffi safe (although those can call not ffi-safe code). My current time schedule is therefore analyzing an open issue, adding a few examples and then "publishing" this one for people to get familiar with Enzyme, while working on a new implementation which should not be limited by ffi anymore and should also be able to support things like https://github.com/Rust-GPU/Rust-CUDA
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Scout APM
Less time debugging, more time building. Scout APM allows you to find and fix performance issues with no hassle. Now with error monitoring and external services monitoring, Scout is a developer's best friend when it comes to application development.
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To give a little bit of context here, this is a Rust frontend for Enzyme itself, which is a leading Auto-Diff tool. The key advantage is that unlike most of the existing tools it does generate gradient functions after applying a lot of (LLVM's) optimizations, which leads to very efficient gradients (benchmarks here: https://enzyme.mit.edu/). Working on LLVM level also allows it to work across language barriers. Finally it is also the first AD library to support generic AMD-HIP / NVIDIA-CUDA code and works also with OpenMP and MPI. https://c.wsmoses.com/papers/EnzymeGPU.pdf I have intentions to add rayon support, since that is more likely to be used on our Rust side :)
Related posts
- Enzyme: towards state-of-the-art AutoDiff in Rust
- Oxide-Enzyme: Integrating LLVM's Static Automatic Differentiation Plugin
- What's everyone working on this week (7/2022)?
- Wsmoses/Enzyme: High-performance automatic differentiation of LLVM
- Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and PyTorch to Jax and Julia