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Mini Project (Parallel Number Processing)

Goal

Create a CPU-bound job and speed it up using a process pool.

Example job:

  • compute something expensive for many items

Step 1: Define an expensive function

expensive.py
def expensive(x: int) -> int:
    # intentionally slow operation
    total = 0
    for i in range(1, 200_000):
        total += (x * i) % 97
    return total
expensive.py
def expensive(x: int) -> int:
    # intentionally slow operation
    total = 0
    for i in range(1, 200_000):
        total += (x * i) % 97
    return total

Step 2: Run sequential

sequential.py
import time
from expensive import expensive
 
items = list(range(20))
 
start = time.time()
results = [expensive(x) for x in items]
print("sequential seconds:", round(time.time() - start, 2))
print(results[:5])
sequential.py
import time
from expensive import expensive
 
items = list(range(20))
 
start = time.time()
results = [expensive(x) for x in items]
print("sequential seconds:", round(time.time() - start, 2))
print(results[:5])

Step 3: Run in parallel (Pool)

parallel.py
import time
from multiprocessing import Pool
from expensive import expensive
 
items = list(range(20))
 
if __name__ == "__main__":
    start = time.time()
 
    with Pool() as pool:
        results = pool.map(expensive, items)
 
    print("parallel seconds:", round(time.time() - start, 2))
    print(results[:5])
parallel.py
import time
from multiprocessing import Pool
from expensive import expensive
 
items = list(range(20))
 
if __name__ == "__main__":
    start = time.time()
 
    with Pool() as pool:
        results = pool.map(expensive, items)
 
    print("parallel seconds:", round(time.time() - start, 2))
    print(results[:5])

Deliverable

  • Compare sequential vs parallel time.
  • Try different pool sizes.
  • Explain when multiprocessing helps (CPU-bound) vs not (I/O-bound).

🧪 Try It Yourself

Exercise 1 – Start a Process

Exercise 2 – Process Pool map()

Exercise 3 – Multiprocessing Queue

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