Implementing M:N Threading with Goroutines in Go

This challenge focuses on understanding and implementing M:N threading, a concurrency model where M user-level threads are managed by N kernel-level threads. In Go, this is elegantly handled by the runtime using goroutines and the Go scheduler. Your task is to simulate a system where multiple "tasks" are processed concurrently, showcasing how Go's built-in concurrency primitives efficiently manage these tasks.

Problem Description

You need to create a Go program that simulates a scenario where a fixed number of worker goroutines process a stream of incoming tasks. The goal is to demonstrate M:N threading by managing multiple tasks (M) with a limited pool of worker goroutines (N).

Requirements:

1. Task Generation: A "producer" goroutine should generate a predefined number of tasks. Each task can be represented by a simple integer or a struct containing some data.
2. Worker Pool: A fixed number of "worker" goroutines should be created to process these tasks. This number (N) should be configurable.
3. Task Distribution: Tasks generated by the producer should be distributed to the available worker goroutines. A channel is the idiomatic Go way to achieve this.
4. Task Processing: Each worker goroutine should simulate processing a task by performing a brief operation (e.g., sleeping for a short duration, printing a message).
5. Completion Synchronization: The main program should wait until all tasks have been generated and processed before exiting.
6. Graceful Shutdown: Workers should gracefully shut down once all tasks are completed, and the producer should signal when it's done producing.

Expected Behavior:

The program should output messages indicating task generation, task assignment to workers, and task completion. The output should show that multiple tasks are being processed concurrently by the limited pool of workers.

Edge Cases to Consider:

• What happens if the number of tasks is very small compared to the number of workers?
• What happens if the number of tasks is very large?
• How do you ensure all workers terminate correctly?

Examples

Example 1:

Let's say we have 10 tasks and 4 worker goroutines.

Input (Conceptual):

• Number of tasks (M): 10
• Number of workers (N): 4

Expected Output (Illustrative - order may vary due to concurrency):

Producer: Generating task 1
Producer: Generating task 2
Producer: Generating task 3
Producer: Generating task 4
Producer: Generating task 5
Worker 1: Started processing task 1
Worker 2: Started processing task 2
Worker 3: Started processing task 3
Worker 4: Started processing task 4
Worker 1: Finished processing task 1
Producer: Generating task 6
Worker 1: Started processing task 5
Worker 2: Finished processing task 2
Producer: Generating task 7
Worker 2: Started processing task 6
... (continues until all 10 tasks are processed)
Producer: All tasks generated.
Worker 1: Finished processing task 10
Worker 2: Finished processing task 9
Worker 3: Finished processing task 8
Worker 4: Finished processing task 7
All workers have finished. Program exiting.

Explanation: The producer generates tasks and sends them through a channel. The four worker goroutines read from this channel, process tasks concurrently, and finish. The main goroutine waits for all workers to complete before exiting.

Example 2:

A scenario with more tasks than workers.

Input (Conceptual):

• Number of tasks (M): 100
• Number of workers (N): 5

Expected Output (Illustrative): Similar to Example 1, but demonstrating how the 5 workers handle a larger workload, with tasks being picked up as soon as a worker becomes free.

Constraints

• The number of tasks (M) will be between 10 and 1000.
• The number of worker goroutines (N) will be between 2 and 10.
• Task processing simulation should involve a small delay (e.g., 50-100 milliseconds) to make concurrency visible.
• Your solution should avoid race conditions and deadlocks.

Notes

• Think about how to signal completion from the producer to the workers. A sync.WaitGroup is a valuable tool for managing goroutine completion.
• Channels are crucial for communication between the producer and workers, and for signaling that workers can shut down.
• Consider using context.Context for more robust cancellation and timeout handling in a real-world scenario, though it might be overkill for this specific challenge if not explicitly asked.
• The core of this problem is understanding the Go scheduler's role in mapping your M goroutines to N OS threads implicitly. Your code provides the M "units of work" (goroutines), and the Go runtime efficiently schedules them.