Comparison of Copy-and-Patch and Meta-Tracing Compilation techniques in the context of Python

Python's dynamic nature makes performance enhancement challenging. Recently, a JIT compiler using a novel copy-and-patch compilation approach was implemented in the reference Python implementation, CPython. Our goal in this work is to study and understand the performance properties of CPython's new JIT compiler. To facilitate this study, we compare the quality and performance of the code generated by this new JIT compiler with a more mature and traditional meta-tracing based JIT compiler implemented in PyPy (another Python implementation). Our thorough experimental evaluation reveals that, while it achieves the goal of fast compilation speed, CPython's JIT severely lags in code quality/performance compared with PyPy. While this observation is a known and intentional property of the copy-and-patch approach, it results in the new JIT compiler failing to elevate Python code performance beyond that achieved by the default interpreter, despite significant added code complexity. In this thesis, we report and explain our novel experiments, results, and observations.