Comparing Multithreading vs. Multiprocessing in Python: CPU-bound vs. I/O-bound Tasks

When optimizing Python programs for performance, choosing between multithreading and multiprocessing is crucial. This post explores their differences in CPU-bound and I/O-bound workloads through experiments and data visualization.

1. CPU-bound Tasks: Heavy Computation

Experiment Summary

I performed an experiment where multiple threads and processes executed a computationally intensive task (e.g., calculating prime numbers or matrix multiplications). The execution times were measured for increasing workload sizes.

Observations

Threading struggled due to Python’s Global Interpreter Lock (GIL), which prevents multiple threads from executing Python bytecode in parallel but thrived in smaller tasks.
Multiprocessing was significantly faster because it utilized multiple CPU cores, running separate processes in true parallel execution but was slower doing smaller tasks due to larger process-spawning overhead.

CPU-bound Execution Time Comparison

2. I/O-bound Tasks: File Read/Write

Experiment Summary

In this experiment, I simulated an I/O-heavy workload by reading and writing large files in parallel using both multithreading and multiprocessing.

Observations

Multithreading was initially faster for smaller file sizes since it could interleave execution while waiting on disk operations.
Multiprocessing eventually outperformed threading for larger I/O tasks, as separate processes handled file I/O independently.
Process creation overhead made multiprocessing inefficient for small workloads.

I/O-bound Execution Time Comparison

3. But Why Choose Threading for I/O Bound Tasks?

Understanding "Interleaving"

The key reason threading works well for I/O-bound tasks is that these tasks spend a lot of time waiting (e.g., waiting for a file to be read from disk or a network request to complete).

For example:

Thread A starts reading a file but must wait for the disk to send data.
Instead of staying idle, Python switches to Thread B, which might be writing to a different file or reading another part of the disk.
When the disk is ready, Thread A resumes while other threads keep working.

Since the CPU isn’t doing much (just waiting), this switching makes I/O-bound tasks faster by keeping the program busy instead of just waiting.

Why Doesn’t Multiprocessing Win for I/O?

Threads Share Memory, Processes Don’t

Threads within the same process share memory and can quickly switch between I/O operations.
Processes have separate memory spaces, so they don’t communicate as efficiently.
When a process waits for I/O, it doesn’t automatically switch to another task unless explicitly managed.

Context Switching is Heavier for Processes

The OS has to fully swap out a process (memory, registers, execution state) before switching to another.
This is much slower than switching between threads, which are lighter and share the same execution space.

I/O Operations Are Often the Bottleneck Anyway

In an I/O-bound task (like file read/write), the bottleneck is usually the disk speed or network latency—not the CPU.
Since threading allows efficient switching between tasks without heavy process overhead, it wins.

When Would Multiprocessing Help in I/O?

If each process handles completely separate files and avoids communication overhead.
If I/O is mixed with heavy CPU work, where multiprocessing can handle CPU-bound parts in parallel.

So, for I/O tasks like file handling, web scraping, and database queries, threading is usually better than multiprocessing.

Final Takeaways

Multiprocessing is best for CPU-heavy tasks, but it comes with higher memory and process creation overhead.
Multithreading is great for I/O-heavy tasks, especially when waiting on external resources (disk, network).

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