The Effect of Unrolling and Inlining for Python Bytecode Optimizations

In this study, we consider bytecode optimizations for Python, a programming language which combines object-oriented concepts with features of scripting lan-guages, such as dynamic dictionaries. Due to its design nature, Python is relatively slow compared to other lan-guages. It operates through compiling the code into powerful bytecode instructions that are executed by an interpreter. Python’s speed is limited due to its in-terpreter design, and thus there is a significant need to optimize the language. In this paper, we discuss one possible approach and limitations in optimizing Python based on bytecode transformations. In the first stage of the proposed optimizer, the bytecode is ex-panded using function inline and loop unrolling. The second stage of transformations simplifies the bytecode by applying a complete set of data-flow optimizations, including constant propagation, algebraic simplifica-tions, dead code elimination, copy propagation, com-mon sub expressions elimination, loop invariant code motion and strength reduction. While these optimiza-tions are known and their implementation mechanism (data flow analysis) is well developed, they have not been successfully implemented in Python due to its dy-namic features which prevent their use. In this work we attempt to understand the dynamic features of Python and how these features affect and limit the implemen-tation of these optimizations. In particular, we consider Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee