Batuhan Taşkaya

Python with LLVM has at least one decade of history. This session will be going to cover-up how python implementations tried to use LLVM such as CPython's Unladen Swallow branch (PEP 3146) or attempts from PyPy and why they failed. After that it will show what are the current python projects that use LLVM for speed, such as numba and python libraries for working with LLVM IR. In the end, it will mention about new ideas that would unite the powers of both LLVM and Python.

This talk is about LLVM's influence over Python's ecosystem. It is targeted an audience of language developers who want to integrate LLVM and developers who are curious about why dont dynamic languages can unite their power with LLVM to speed-up. It will start with python's implementations and the approaches they take. The Unladen Swallow branch which basically tries to integrate LLVM to CPython (under google sponsored) is a good example of these approaches. There are attempts from the PyPy side but they are also failed because of the dynamic nature of Python. After this fails, we'll swap back to our current time and show projects that are benefiting from LLVM to speed up python especially on the scientific side such as numba (which offers JITting via LLVM). Besides these projects, there are also a few projects that offer an interface to LLVM. Such as llvmpy and llvmlite. I've been using llvmlite about 1 year in my side projects and toy languages so these projects has the potential to inspire developers to work with LLVM and build languages a-top on it. In the end, it will show what is the future of these two big projects (LLVM & Python) and how we can participate.

Learn what is going on behind the scene about memory. How CPython allocates memory? How it manages Private Heap? Does it uses abstractions for managing the memory? Does python relase memory back to the system? Learn how python GC works, reference counting and generational GC. How generations classified? How does reference counting mechanism work? This talk is going to try to answer all this questions.

• Motivation about that subject. Why do i need to know this stuff? What is the effect of these things on my code quality? Is that lecture enough for being a master about this subject?

• Understanding Objects Everything is an object in python (even code). If we want to understand what is going on behind the scene about memory allocation for objects first we should understand objects. This part has a cpython code reference for PyObject. We are going to learn python objects with seeing how they implemented.

• Memory Management Model Python has a great system about managing the memory (the private heap). Heap managed by +1th layer but the main part of this talk is +2th layer, object allocation. We'll discuss them

• Small Object Threshold Classifies objects into 2 sections. If size of an object bigger than 512 it is big else it is small.

• Abstractions We'll talk about abstractions, Arena > Pool > Block

• Deallocations We'll talk about GC, reference count, counting mechanism, generational gc, mark & sweep, generations.