triton-cuda-reduce

What problem does it solve?

Redesign and implement high-performance CUDA reduction kernels for multi-dimensional tensors using Triton, enabling scalable, numerically stable reductions in ML workloads.

Core Features & Use Cases

• Provide Triton-based reduction patterns for sum, mean, max/min, softmax, and layernorm-like reductions across arbitrary dimensions.
• Guidance on numerical stability, block-wise reductions, atomic aggregation, and per-row processing for large tensors.
• Use cases include fast attention score computations, normalization layers, and summary statistics in large-scale models.

Quick Start

Implement a reduce kernel for a given tensor using the Triton CUDA guide.