Accelerated Linear Algebra
| Developer(s) | |
|---|---|
| Repository | www |
| Written in | C++, Python |
| Operating system | Linux, macOS, Windows |
| Platform | TensorFlow |
| Type | Machine learning, Optimization |
| License | Apache License 2.0 |
Accelerated Linear Algebra (XLA) is an advanced optimization framework within TensorFlow, a popular machine learning library developed by Google.[1] XLA is designed to improve the performance of TensorFlow models by optimizing the computation graph at a lower level, making it particularly useful for large-scale computations and high-performance machine learning models. Key features of TensorFlow XLA include:[2]
- Compilation of TensorFlow Graphs: Compiles TensorFlow computation graphs into efficient machine code.
- Optimization Techniques: Applies operation fusion, memory optimization, and other techniques.
- Hardware Support: Optimizes models for various hardware including GPUs and TPUs.
- Improved Model Execution Time**: Aims to reduce TensorFlow models' execution time for both training and inference.
- Seamless Integration: Can be used with existing TensorFlow code with minimal changes.
TensorFlow 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 TensorFlow operations.
- vmap: Vectorization capabilities.
- pmap: Parallelization over multiple devices.
See also[edit]
References[edit]
- ^ Hampton, Jaime (2022-10-12). "Google Announces Open Source ML Compiler Project, OpenXLA". EnterpriseAI. Archived from the original on 2023-12-10. Retrieved 2023-12-10.
- ^ 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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