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Accelerated Linear Algebra
Advanced optimization framework for TensorFlow to enhance computational performance.

XLA (Accelerated Linear Algebra) is an open-source compiler for machine learning developed by the OpenXLA project. XLA is designed to improve the performance of machine learning models by optimizing the computation graphs at a lower level, making it particularly useful for large-scale computations and high-performance machine learning models. Key features of XLA include:

  • Compilation of Computation Graphs: Compiles computation graphs into efficient machine code.
  • Optimization Techniques: Applies operation fusion, memory optimization, and other techniques.
  • Hardware Support: Optimizes models for various hardware, including CPUs, GPUs, and NPUs.
  • Improved Model Execution Time: Aims to reduce machine learning models' execution time for both training and inference.
  • Seamless Integration: Can be used with existing machine learning code with minimal changes.

XLA represents a significant step in optimizing machine learning models, providing developers with tools to enhance computational efficiency and performance.

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Supported target devices

See also

References

  1. "OpenXLA Project". Retrieved December 21, 2024. https://openxla.org/ ↩

  2. Woodie, Alex (2023-03-09). "OpenXLA Delivers Flexibility for ML Apps". Datanami. Retrieved 2023-12-10. https://www.datanami.com/2023/03/08/openxla-delivers-flexibility-for-ml-apps/ ↩

  3. "TensorFlow XLA: Accelerated Linear Algebra". TensorFlow Official Documentation. Retrieved 2023-12-10. https://www.tensorflow.org/xla ↩

  4. Smith, John (2022-07-15). "Optimizing TensorFlow Models with XLA". Journal of Machine Learning Research. 23: 45–60. ↩

  5. "intel/intel-extension-for-openxla". GitHub. Retrieved December 29, 2024. https://github.com/intel/intel-extension-for-openxla/ ↩

  6. "Accelerated JAX on Mac - Metal - Apple Developer". Retrieved December 29, 2024. https://developer.apple.com/metal/jax/ ↩

  7. "Developer Guide for Training with PyTorch NeuronX — AWS Neuron Documentation". awsdocs-neuron.readthedocs-hosted.com. Retrieved 29 December 2024. https://awsdocs-neuron.readthedocs-hosted.com/en/latest/frameworks/torch/torch-neuronx/programming-guide/training/pytorch-neuron-programming-guide.html ↩

  8. Barsoum, Emad (13 April 2022). "Supporting PyTorch on the Cerebras Wafer-Scale Engine - Cerebras". Cerebras. Retrieved 29 December 2024. https://cerebras.ai/blog/supporting-pytorch-on-the-cerebras-wafer-scale-engine/ ↩

  9. Ltd, Graphcore. "Poplar® Software". graphcore.ai. Retrieved 29 December 2024. https://www.graphcore.ai/products/poplar ↩

  10. "PyTorch/XLA documentation — PyTorch/XLA master documentation". pytorch.org. Retrieved 29 December 2024. https://pytorch.org/xla/ ↩