Trying No GIL on Scientific Programming

Get the slides at slides.com/cheukting_ho/trying-no-gil/

Do you know what is GIL?

What is GIL

• Global Interperter Lock
• Only a single operating system thread is used to run Python
• Limit only one thread can access an object at a time
• Imagine one thread is adding an object and another deleting it - lock is needed
• Other programs may have multiple locks to do it but it is more complicated than GIL

What is No-gil Python

• Clone of 3.9
• 4th attempt - Previous by Greg Stein (1996), Adam Olsen (2007) and Larry Hastings (2016)
• by Sam Gross
• Why no-gil => make use of multiple cores => SPEED

Design and challanges

• Need to be good at both single-thread and multi-threads
• Challenges - Reference counting - Bias reference counting
• Make commonly used objects immortal - no ref count
• Make some objects deferred ref counting - add counts at GC

Design and challanges

• Challenges - thread safety for objects like dict and list
• Using small locks
• Manually write the lock orders using CPython API
• replacement of Python’s built-in allocator pymalloc with mimalloc for thread safety
• Need to stop the world for GC

How does it perform for scitific programs?

• Most scientific packages have cpy modules, JIT compiler or Cython for speed up
• Do programs benefit from no GIL?
• Test it on some popular scientific processes

How to test it?

• Try on something using pure Python
• Try on something with Scikit-learn, NumPy and Scipy
• Try on something about neural network
• campare No Gil, original 3.9 and 3.11
• Code I tested are on GitHub
• Run experiment on GitHub action (reproducible)
• cProfile report for extra investigation

Test #0 - Fibonacci

Generate first 25 numbers in Fibonacci sequence

- Average over 50 times

* run on GitHub Action ubuntu-latest (Ubuntu 22.04) with 2 cores

Significant improvement from 3.9
A bit better than 3.11

Test #1 - SVM

We use Recognizing hand-written digits

- Average over 50 times

* run on GitHub Action ubuntu-latest (Ubuntu 22.04) with 2 cores

No significant difference

Test #2 - Clustering

We use A demo of K-Means clustering on the handwritten digits data - Average over 50 times

No significant difference (or worse)

* run on GitHub Action ubuntu-latest (Ubuntu 22.04) with 2 cores

Test #3 - Decision Tree

We use the Iris data set in Plot the decision surface of decision trees trained on the iris dataset 

- averaging all pairs of features

* run on GitHub Action ubuntu-latest (Ubuntu 22.04) with 2 cores

Test #4 - Linear algebra

We use Linear algebra on n-dimensional arrays

- Average over 50 times

* run on GitHub Action ubuntu-latest (Ubuntu 22.04) with 2 cores

Test #5 - Image filters

We use X-ray image processing

- Average over 50 times

* run on GitHub Action ubuntu-latest (Ubuntu 22.04) with 2 cores

Test #6 - MLPClassifier

We use Compare Stochastic learning strategies for MLPClassifier

- Average over 10 times

* run on GitHub Action ubuntu-latest (Ubuntu 22.04) with 2 cores

Does it mean that no GIL does not help?

Why we didn't see much differnece

• C extension processes already using multi-threads
• C extensions may still expecting a GIL
• It needs to adapt to no GIL mode
• Compatibility can be an issue
• Only comparing on dual-core (env dependent)
• no GIL Python fork is still a work in progress

So what do we learnt?

• Python is very versatile
• There are different tools for different jobs
• Creating a general strategy to solve all problems is impossible
• Thank you for Sam and all the maintainers who are making Python and the tools we used better

Reference

 Check these out!

• EuroPython 2022 Keynote by Sam Gross
• Blog post by Łukasz Langa

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