Accelerated Linear Algebra (XLA) is an open-source compiler for machine learning developed by the OpenXLA project.[1] 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:[2]
- 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.
| Developer(s) | OpenXLA |
|---|---|
| Repository | xla on GitHub |
| Written in | C |
| Operating system | Linux, macOS, Windows |
| Type | compiler |
| License | Apache License 2.0 |
| Website | openxla |
XLA represents a significant step in optimizing machine learning models, providing developers with tools to enhance computational efficiency and performance.[3][4]
Features
edit- grad: Supports automatic differentiation.
- jit: Just-in-time compilation for optimizing operations.
- vmap: Vectorization capabilities.
- pmap: Parallelization over multiple devices.
See also
editReferences
edit- ^ "OpenXLA Project". Retrieved December 21, 2024.
- ^ Woodie, Alex (2023-03-09). "OpenXLA Delivers Flexibility for ML Apps". Datanami. Retrieved 2023-12-10.
- ^ "TensorFlow XLA: Accelerated Linear Algebra". TensorFlow Official Documentation. Retrieved 2023-12-10.
- ^ Smith, John (2022-07-15). "Optimizing TensorFlow Models with XLA". Journal of Machine Learning Research. 23: 45–60.
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