2025-03-01 –, Main Hall (LH 111)
In this presentation, aimed at Python beginners interested in parallel processing, we will explain the fundamental concepts of the Global Interpreter Lock (GIL) in Python and discuss its impact on programming. Specifically, we will touch upon the ability to disable the GIL in Python 3.13 and provide practical advice on how beginners can approach the GIL to achieve efficient concurrent processing.
(Currently, I am considering the following agenda)
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Introduction
• Understanding the GIL: Introduce the existence and importance of the GIL and clarify the purpose of the presentation. -
Impact of the GIL on Programs
• Constraints of Multithreading: Explain how the GIL restricts true concurrency between threads.
• Impact on Performance: Compare the effects of the GIL on I/O-bound and CPU-bound processes.
• Personal Experiences: Share actual challenges faced while accelerating simulations using Python during graduate research. -
Methods to Overcome GIL Constraints
• Utilizing Multiprocessing: Introduce process-based concurrent processing using the multiprocessing module.
• Leveraging Asynchronous Processing: Explain asynchronous programming techniques using asyncio.
• Third-Party Libraries: Propose ways to utilize libraries that are less affected by the GIL. -
GIL Disabling Option in Python 3.13
• Introduction of New Features: Explain the GIL disabling option introduced in Python 3.13.
• Advantages and Cautions: Discuss the benefits of disabling the GIL and important considerations such as compatibility. -
Conclusion
• Reaffirming Key Points: Deepen understanding of the GIL and reaffirm the importance of appropriately utilizing Python's concurrent processing capabilities. -
Q&A Session
• Interactive Discussion: Answer questions from participants to deepen understanding.
Beginner
Category:Advanced and Core Python
Yu Saito is currently working as a Technical Specialist for Azure at Microsoft Japan. Prior to joining Microsoft, Yu was a graduate student specializing in bioinformatics. During this time, he developed machine learning models to accelerate molecular dynamics simulations. This experience sparked his interest in Python, which continues to be a key area of expertise.