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. Numba, an open-source project that helps improve Python’s performance landscape by providing a JIT compiler that translates Python code into optimized machine code. Unlike traditional Python interpreters, Numba compiles Python functions on-the-fly, yielding remarkable speed-ups by leveraging the Low-Level Virtual Machine (LLVM) infrastructure. The result is highly efficient native machine code that rivals the performance of compiled languages like C++.

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. In addition, In this talk, attendees will learn about recent progress of accelerated computing in Python with Numba-CUDA, the internal mechanisms of Numbast to help bridge the gap between CUDA C++ and Python.

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.