Python offers three parallelization interfaces: concurrent.futures, threading/multiprocessing, and asyncio. How do they differ in abstraction and capability?

You submit two tasks; the first is slow, the second fast. You print f1.result() then f2.result(). Which result appears first?

You have a CPU-bound function (heavy number crunching). You run 4 copies across 4 threading.Threads on a 4-core machine. Does it finish ~4x faster?

If the GIL makes threads useless for speeding up CPU work, what ARE threads actually good for in CPython?

You need to share a large mutable dict between parallel workers and have them all update it live. Threads or processes?

What does this print? ``` import threading class C(threading.Thread): counter = 0 def run(self): for _ in range(2000000): C.counter += 1 a, b = C(), C() a.start(); b.start() a.join(); b.join() print(C.counter) ```

How do you fix the lost-update race on a shared counter incremented by multiple threads?