Programming languages like Python, JavaScript, and Ruby are becoming increasingly popular due to their dynamic capabilities. These languages are often much easier to learn than other, statically type checked, languages such as C++ or Rust. Unfortunately, these dynamic languages come at the cost of losing compile-time optimizations. Python is arguably the most popular language for data scientists and researchers in the artificial intelligence and machine learning communities. As this research becomes increasingly popular, and the problems these researchers face become increasingly computationally expensive, questions are being raised about the performance of languages like Python. Language features found in Python, more specifically dynamic typing and run-time modification of object attributes, preclude common static analysis optimizations that often yield improved performance.
This thesis attempts to quantify the cost of dynamic features in Python. Namely, the run-time modification of objects and scope as well as the dynamic type system. We introduce Cannoli, a Python 3.6.5 compiler that enforces restrictions on the language to enable opportunities for optimization. The Python code is compiled into an intermediate representation, Rust, which is further compiled and optimized by the Rust pipeline. We show that the analyzed features cause a significant reduction in performance and we quantify the cost of these features for language designers to consider.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3149 |
| Date | 01 June 2018 |
| Creators | Catanio, Jonathan Joseph |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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