10. Summary: What I Finally Understood
After years of trial and error, here's what crystallized for me:
- Concurrency: Structural. Dealing with multiple tasks. (Works on single-core)
- Parallelism: Physical. Executing multiple tasks simultaneously. (Requires multi-core)
- I/O Bound: Concurrency (async) is the answer. Node.js/Nginx approach.
- CPU Bound: Parallelism (multi-core) is the answer. Multiprocessing/GPU.
- GIL: Python threading's Achilles heel. Bypass with multiprocessing.
- Context Switching: Not free. Too many threads = more switching than working.
- Modern Trend: Lightweight abstractions (Goroutines, Virtual Threads, async/await) over raw OS threads.
As Rob Pike, creator of Go, famously said:
"Concurrency is about dealing with lots of things at once. Parallelism is about doing lots of things at once."
The key insight is understanding your workload. Is it waiting (I/O) or computing (CPU)? Choose the right tool. More threads don't automatically mean faster code. Understanding the context and applying the appropriate concurrency/parallelism model — that's real engineering skill.