Anna Herlihy - Wrestling Python into LLVM Intermediate Representation The LLVM Project provides an intermediate representation (LLVM-IR) that can be compiled on many platforms. LLVM-IR is used by analytical frameworks to achieve language and platform independence. What if we could add Python to the long list of languages that can be translated to LLVM-IR? This talk will go through the steps of wrestling Python into LLVM-IR with a simple, static one-pass compiler. ----- What is LLVM-IR? The LLVM Compiler Infrastructure Project provides a transportable intermediate representation (LLVM-IR) that can be compiled and linked into multiple types of assembly code. What is great about LLVM-IR is that you can take any language and distill it into a form that can be run on many different machines. Once the code gets into IR it doesn’t matter what platform it was originally written on, and it doesn’t matter that Python can be slow. It doesn’t matter if you have weird CPUs - if they’re supported by LLVM it will run. What is Tupleware? TupleWare is an analytical framework built at Brown University that allows users to compile functions into distributed programs that are automatically deployed. TupleWare is unique because it uses LLVM-IR to be language and platform independent. What is PyLLVM? This is the heart of the talk. PyLLVM is a simple, easy to extend, one-pass static compiler that takes in the subset of Python most likely to be used by Tupleware. PyLLVM is based on an existing project called py2llvm that was abandoned around 2011. This talk will go through some basic compiler design and talk about how some LLVM-IR features make our lives easier, and some much harder. It will cover types, scoping, memory management, and other implementation details. To conclude, it will compare PyLLVM to Numba, a Python-to-LLVM compiler from Continuum Analytics and touch on what the future has in store for PyLLVM.