# Task 01: Vector Add

## 1. Problem Statement

Implement `out[i] = x[i] + y[i]` in both Triton and CUDA, then compare both against the PyTorch reference.

## 2. Expected Input/Output Shapes

- Input: two tensors with identical 1D or flattened shapes
- Output: one tensor with the same shape

## 3. Performance Intuition

Vector add is simple enough that launch overhead and memory bandwidth dominate quickly. It is a good place to learn indexing before the math becomes interesting.

## 4. Memory Access Discussion

This kernel should read `x[i]` and `y[i]` once and write `out[i]` once. The main thing to inspect is whether neighboring threads or lanes access neighboring elements.

## 5. What Triton Is Abstracting

Triton lets you express one block of contiguous offsets with `program_id` and `tl.arange`, then apply a mask on the tail.

## 6. What CUDA Makes Explicit

CUDA makes you compute `global_idx` from block and thread indices yourself and write the boundary check explicitly.

## 7. Reflection Questions

- What is the exact correspondence between `program_id` and `blockIdx.x` here?
- Why is a mask or bounds check required on the final block?
- How would the ownership change if one thread handled multiple elements?

## 8. Implementation Checklist

- Confirm the reference implementation
- Fill in the Triton masked loads, add, and store
- Fill in the CUDA thread ownership and store
- Test small and non-multiple-of-block-size shapes
- Benchmark bandwidth on larger vectors