Thread-Safe Max Tracker

Given a list of values, spawn one thread per value; each thread proposes its value as the new running maximum. Print the final maximum.

Why this is gradable. Concurrent code can legitimately produce different interleavings on every run, so this practice bank only includes problems whose correctness reduces to one deterministic final value or invariant — never a specific thread ordering. If your solution is correct, it will print the exact same output on every run, no matter how the scheduler interleaves your threads. The max of a fixed set of values is the same no matter what order it's computed in, which is exactly why this is a good first "read shared state, maybe write shared state" synchronization exercise.

Example

Input: 3 7 2 9 4 1 8 5 6 0 15 12 Output: 15

Why the naive version is wrong

"Check if my value is bigger, then update" is a classic check-then-act race: two threads can both read the old max, both decide they're bigger, and one update can clobber the other — or a thread can read a stale max mid-update. The bug doesn't show up with small lists; it shows up under load, which is exactly why interviewers ask this.

Approach

Guard both the read and the write with the same lock, so "check, then act" happens as one atomic step.

Input format

Line 1: space-separated integers (one per thread)

Print the final maximum.

import threading values = list(map(int, input().split())) current_max = values[0] lock = threading.Lock() # TODO: make check-then-act atomic — reading and writing current_max must happen under the same lock def worker(v): global current_max if v > current_max: current_max = v threads = [threading.Thread(target=worker, args=(v,)) for v in values] for t in threads: t.start() for t in threads: t.join() print(current_max)

Thread-Safe Max Tracker

Thread-Safe Max Tracker

Example

Why the naive version is wrong

Approach

Input format

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