Triton

Purpose

Guide agents through writing GPU kernels in OpenAI Triton: the @triton.jit decorator, block-oriented tl.load/tl.store with masking, atomic operations, shared memory via tl.constexpr, benchmarking with triton.testing.Benchmark, PyTorch integration, and debugging with barriers.

When to Use

• Writing custom PyTorch ops faster than pure PyTorch but without raw CUDA
• Prototyping fused kernels (e.g., softmax + scale + bias)
• Comparing block sizes and warp counts with Triton's autotuner
• Porting NumPy-style elementwise ops to GPU
• Learning GPU programming with higher-level Python syntax
• Benchmarking kernel variants systematically

Workflow

1. Minimal Triton kernel