CODEWITHSUNDEEP
pythonmultithreadinggilflamegraph
comp-era
comp-era

Reputation: 21

Why does my multi-threaded Python script slow down when I add more threads?

I'm working on a Python application that performs a large number of I/O-bound operations. I decided to use the threading module to speed up the process by handling multiple I/O operations concurrently. However, I've noticed that instead of speeding up, my script actually slows down as I increase the number of threads beyond a certain point.

My Setup:

The Issue:

When I run the script with up to 10 threads, performance improves as expected. But when I go beyond 10 threads, the script starts to slow down significantly. By the time I reach 50 threads, the performance is worse than running with just a single thread!

Flame Graph Analysis:

I decided to visualize the CPU usage using Flame Graphs. Surprisingly, the Flame Graphs showed a significant amount of time being spent in what appears to be lock contention. This raised questions about whether the GIL is impacting my I/O-bound threads, or if there's another form of lock contention happening.

Here’s a example snippet of the Python code I’m using:

python
import threading
import requests

def download_page(url):
    response = requests.get(url)
    # Simulate processing
    return len(response.content)

urls = ["http://example.com"] * 1000

def worker():
    while urls:
        url = urls.pop()
        print(f"Downloaded {download_page(url)} bytes")

threads = []
for _ in range(50):  # Adjust thread count here
    thread = threading.Thread(target=worker)
    threads.append(thread)
    thread.start()

for thread in threads:
    thread.join()

### What I've Tried: -

GIL Awareness: I understand Python's Global Interpreter Lock (GIL) might be a bottleneck, but since this is an I/O-bound task, I expected threading to be beneficial.

Resource Limits: I checked system resource limits (ulimit) to ensure there are no restrictions on the number of threads.

Thread Pool Executor: I tried using `concurrent.futures.ThreadPoolExecutor` instead of manually managing threads, but the issue persists.

GIL or Something Else?: Given that this is I/O-bound, why does adding more threads cause a slowdown? Could the GIL still be a factor, or is there another form of lock contention happening?

Upvotes: 0

Views: 82

Answers (1)

user2988908
user2988908

Reputation:

It's super important to understand the difference between concurrency and parallelism. You said concurrent, but were shocked that the program didn't run faster... Well, why would it? a concurrency model simulates simultaneous tasks by sharing processing time across multiple threads. In some programming languages this does, in fact, translate to multiple processors working the threads in parallel, but this is implementation and hardware specific. By adding threads you should, in general, assume that you're adding overhead and slowing your program down though context switching. In python this is particularly true when dealing with I/O because of the cPython implementation of a Global Interpreter Lock, which is often simply referred to as the GIL. While the GIL has changed some over the years, essentially python is a single execution thread that runs. When you add more threads then they are switched between, but if they need to do any I/O at all then all the threads have to stop while waiting on the I/O.

To resolve your problem, there are a few choices.

  • One, the asyncio module was added in python 3.6 to help with I/O bound programs. Using asyncio may not speed up your program, but it should help eliminate the I/O / GIL issue.
  • Another choice is to use the multiprocessing module. It uses (about) the same interface as the threading module, and may be a drop-in replacement for the threading code you've already written. multiprocessing is a true parallelism paradigm that starts new interpreters as new processes to execute your code in parallel on different CPUs manages by your OS.
  • limit your connections. pre-fetch, cache, or batch networked I/O so that you can limit the (very slow) network connections made by your program. Then process your downloaded data in batches using local resources; even single threaded is faster when there's no I/O to bog things down.

Upvotes: 1

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