| # XLA |
| |
| XLA (Accelerated Linear Algebra) is an open-source compiler for machine |
| learning. The XLA compiler takes models from popular frameworks such as PyTorch, |
| TensorFlow, and JAX, and optimizes the models for high-performance execution |
| across different hardware platforms including GPUs, CPUs, and ML accelerators. |
| |
| As a part of the OpenXLA project, XLA is built collaboratively by |
| industry-leading ML hardware and software companies, including |
| Alibaba, Amazon Web Services, AMD, Apple, Arm, Google, Intel, Meta, and NVIDIA. |
| |
| ## Key benefits |
| |
| - **Build anywhere**: XLA is already integrated into leading ML frameworks |
| such as TensorFlow, PyTorch, and JAX. |
| |
| - **Run anywhere**: It supports various backends including GPUs, CPUs, and ML |
| accelerators, and includes a pluggable infrastructure to add support for |
| more. |
| |
| - **Maximize and scale performance**: It optimizes a model's performance with |
| production-tested optimization passes and automated partitioning for model |
| parallelism. |
| |
| - **Eliminate complexity**: It leverages the power of |
| [MLIR](https://mlir.llvm.org/) to bring the best capabilities into a single |
| compiler toolchain, so you don't have to manage a range of domain-specific |
| compilers. |
| |
| - **Future ready**: As an open source project, built through a collaboration |
| of leading ML hardware and software vendors, XLA is designed to operate at |
| the cutting-edge of the ML industry. |
| |
| ## Documentation |
| |
| To learn more about XLA, check out the links on the left. If you're a new XLA |
| developer, you might want to start with [XLA architecture](architecture.md) and |
| then read [Contributing](contributing.md). |
