Overview
XLA (Accelerated Linear Algebra) is a compiler framework for machine learning that compiles high-level model graphs into efficient code for different hardware targets, reducing memory footprint and improving execution speed.
Key features
- Multi-backend support: generates optimized code for CPU, GPU, and accelerators.
- Operator fusion and optimizations: applies fusion and constant folding to improve runtime efficiency.
- Framework integrations: commonly used as a backend for TensorFlow and JAX to accelerate model execution.
Use cases
- Performance optimization for model training and inference by compiling graphs into hardware-specific code.
- Edge and heterogeneous deployments by generating efficient execution plans for various devices.
Technical details
- Uses compiler technologies like LLVM for backend code generation, focusing on numeric operation and memory access optimization.