Triton: Language for GPU Computing Primitives
Domain-specific language and compiler for writing GPU deep learning primitives with higher productivity than CUDA.
Learn more about Triton
Triton is a programming language and compiler infrastructure designed for writing high-performance GPU kernels for deep learning applications. The compiler translates Triton code into optimized GPU assembly through an MLIR-based compilation pipeline that includes automatic memory coalescing, shared memory management, and instruction scheduling. The language uses a Python-like syntax with explicit control over memory hierarchy and parallelization patterns, allowing developers to write GPU kernels without managing low-level CUDA details. Triton is commonly used for implementing custom neural network operators, matrix computations, and other compute-intensive primitives in machine learning frameworks.
MLIR-Based Compilation
Uses Multi-Level Intermediate Representation (MLIR) infrastructure for code generation and optimization. The compiler automatically handles memory coalescing, shared memory usage, and instruction scheduling.
Python-Like Syntax
Provides a high-level programming interface similar to Python while generating efficient GPU code. Developers can write kernels without managing CUDA's low-level memory and threading details.
Automatic Optimization
Performs automatic tiling, vectorization, and memory hierarchy optimization during compilation. The compiler analyzes memory access patterns and generates optimized GPU assembly code.
# Install the latest stable release
pip install triton
# Binary wheels are available for CPython 3.10-3.14Triton 3.6.0 release
- –Dialect & Frontend
- –Backend & Compiler
- –AMD/HIP Backend
- –NVIDIA Backend
- –Gluon & Layout Improvements
Triton 3.5.1 release, bug fix release
- –This release is meant to fix the following issue: Fix sm103 (GB300) support broken by Triton 3.5.0 release (
Triton 3.5.0 release
- –Dialect & Frontend
- –Backend & Compiler
- –AMD/HIP Backend
- –NVIDIA Backend
- –Gluon & Layout Improvements
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