Most people think GPU programming is about throwing more cores at a problem.

It's not. It's about understanding how those cores actually work and writing code that cooperates with the hardware instead of fighting it.

Parallel reduction is the perfect example. It's just a sum. An array in, a number out. Dead simple.

But the difference between a naive CUDA kernel and an optimized one is an 8× speedup , not from better math, but from:

• Fixing an addressing pattern that was silently serializing warps
• Letting the compiler eliminate dead code at compile time
• Skipping synchronization barriers that weren't needed
• Moving from shared memory to register-level communication