Write CUDA Reduction Kernel

What problem does it solve?

This skill provides a thorough, reusable approach to implementing correct and efficient CUDA reduction kernels, handling warp-level primitives, block-level reductions, and multi-block strategies to scale to large inputs.

Core Features & Use Cases

• Warp-level shuffle reductions with explicit masks and non-full-warp handling
• Block-level reductions using shared memory with correct synchronization
• Two-pass vs atomic strategies for large inputs and reproducible results
• Batched and axis-based reductions with proper memory access patterns

Quick Start

Implement a CUDA reduction kernel that reduces a 1D array of floats to a single value using a 256-thread block and a two-pass strategy.