Accelerated Linear Algebra

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.

• x86-64
• ARM64
• NVIDIA GPU
• AMD GPU
• Intel GPU
• Apple GPU
• Google TPU
• AWS Trainium, Inferentia
• Cerebras
• Graphcore IPU

• TensorFlow
• PyTorch
• JAX