Description:

Audience: Beginner to intermediate Python has long been praised for its simplicity and ease of use, but its performance has often been a subject of debate. However, with the introduction of Python's new JIT (Just-In-Time) compilers, the landscape is rapidly changing. This talk will dive into the inner workings of NUMBA JIT compiler, exploring how it transforms Python code into optimized machine code at runtime, and the impact it has on improving the performance of Python applications.

Outline:

1. Introduction to JIT Compilation:
   • Understanding the concept of Just-In-Time compilation
   • Benefits and challenges of JIT compilation
2. Evolution of Python's JIT Compiler:
   • Overview of historical approaches to improving Python's performance
   • Introduction of the new JIT compiler in Python
3. How Python's NUMBA JIT Compiler Works:
   • Exploring the compilation process
   • Analyzing the optimizations performed by the JIT compiler
4. Performance Improvements with the JIT Compiler:
   • Benchmarks and comparisons showcasing the performance gains achieved
   • Real-world examples of applications benefitting from the JIT compiler
5. Limitations and Considerations:
   • Addressing potential drawbacks and trade-offs of using the JIT compiler
   • Understanding scenarios where the JIT compiler might not be suitable
6. Conclusion:
   • Recap of key takeaways
   • Inspiring developers to explore the possibilities of Python's new JIT compiler

By attending this talk, participants will gain a comprehensive understanding of Python's NUMBA JIT compiler and its potential to revolutionize the performance of Python applications. They will also learn practical tips and best practices for leveraging the JIT compiler in their own projects, ultimately unlocking the full power of Python.

Slides: Pycon2024Slides

Prerequisites:

1. Basic Knowledge of Python
2. Familiarity with Python Performance
3. Understanding of Compilation Concepts
4. Knowledge of Performance Profiling
5. Intermediate Python Experience

Speaker Info:

Hi my name is Milan Shet. I am a Sr. Tech Lead at Applied Materials, Bangalore. I have been in the Python Industry for the past 8 years with a total of 11 years experience. My journey into Image Processing and Machine Learning while doing my B.E at Ramaiah Institute of Technology. It started off as writing Journals and Research papers and once I had the taste of Image processing and machine learning, there was no going back. At Applied Materials, I enjoy being part of Algo and Software hackathons. The high that I get while working/ coding on a problem all night is unmatchable. And when I am not coding, I go on high altitude treks to the Himalayas, my second home :)

I have taken a keen interest in exploring the latest advancements in Python, including Python's new JIT compiler, and understanding how these technologies can revolutionize the performance of Python applications.

Speaker Links:

https://msrit.academia.edu/MilanShet

Journals and Research Papers:

https://www.academia.edu/77429640/DWT_based_Illumination_Normalization_and_Feature_Extraction_for_Enhanced_Face_Recognition https://www.academia.edu/77429638/ECG_Arrhythmia_Classification_Using_R_Peak_Based_Segmentation_Binary_Particle_Swarm_Optimization_and_Absolute_Euclidean_Classifier https://www.academia.edu/2015431/DWT_based_Feature_Extraction_using_Normalized_Magnitude_based_Thresholding_and_Multilevel_Illumination_Normalization_for_Enhanced_Face_Recognition