XLA (Accelerated Linear Algebra) is anopen-sourcecompiler formachine 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.

XLA represents a significant step in optimizing machine learning models, providing developers with tools to enhance computational efficiency and performance.^[3][4]

• x86-64
• ARM64
• NVIDIA GPU
• AMD GPU
• Intel GPU^[5]
• Apple GPU^[6]
• GoogleTPU
• AWS Trainium, Inferentia^[7]
• Cerebras^[8]
• Graphcore IPU^[9]

• TensorFlow
• PyTorch^[10]
• JAX

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