Understanding why Go/Rust >> Python/Nodejs for CPU-bound tasks!
The Setup Picture this: you've got a CPU-intensive task—computing the sum of squares for 50 million numbers. You fire up Python with 4 threads, expecting to see your 4-core machine flex its muscles. But something's wrong. Your CPU usage hovers around 25%, and the program takes just as long (or longer) than if you'd used a single thread. What's happening? You've just met Python's Global Interpreter Lock (GIL), and it's not playing nice with your parallel dreams. The Experiment To understand this phenomenon, I built a simple benchmark: compute sum(i*i for i in range(N)) across multiple workers. This is pure CPU-bound work—no I/O, no waiting, just raw computation. The same task, implemented in three languages, tells a revealing story. Python (threads) : The GIL prevents multiple threads from executing Python bytecode simultaneously. Even with 4 threads, your program will mostly use 1 CPU core. You...