# GPU Basics

This lab assumes you are learning GPU kernels as structured data-parallel programs.

## Core Ideas

- GPU throughput comes from massive parallelism, not a single fast thread.
- Launch geometry determines which logical elements each thread or program instance owns.
- Global memory is large and slow relative to on-chip storage.
- Kernel design is often about reducing memory traffic and increasing reuse.

## Terms To Keep Straight

- thread: the smallest execution entity in CUDA
- warp: a hardware scheduling group, usually 32 threads
- block: a cooperating group of threads with shared memory access
- grid: the full launch of all blocks
- program instance: Triton's block-level work abstraction

## Mental Model For This Repo

Each task asks the same questions in both Triton and CUDA:

- What data does one unit of work own?
- How is that ownership computed from launch indices?
- Which reads are coalesced or contiguous?
- Which intermediate values must be reduced?
- Which values should be reused on chip?

Keep a notebook. Write down the answers before you code.