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