A team of computer scientists from the University of Massachusetts Amherst has developed a new tool that can dramatically improve the performance of Python programs. The tool, called Scalene, uses artificial intelligence to identify and optimize the bottlenecks in Python code, resulting in speedups of up to 60,000 times.
Python: a popular but slow language
Python is one of the most widely used programming languages in the world, especially for data science and machine learning applications. It has a simple and expressive syntax, a rich set of libraries and tools, and a large and active community of developers.
However, Python also has a major drawback: it is very slow compared to other languages. Python programs can run up to 1,000 times slower than equivalent programs written in C++, Fortran, or Java. This is because Python is an interpreted language, which means that it executes the code line by line at runtime, rather than compiling it into machine code beforehand. Moreover, Python has a feature called the Global Interpreter Lock (GIL), which prevents multiple threads from running simultaneously, limiting the parallelism and scalability of Python programs.
Scalene: a smart profiler for Python
To overcome Python’s slowness, programmers can use tools called profilers, which measure the execution time and memory usage of different parts of the code. Profilers can help programmers identify where the code is spending most of the time and resources, and suggest ways to improve it.
However, existing profilers for Python have several limitations. They are either too coarse-grained, providing only aggregate information about the whole program or function, or too fine-grained, providing too much detail about every line or instruction. They also do not account for the CPU, GPU, and memory usage simultaneously, which are all important factors for performance. Furthermore, they do not offer any guidance on how to optimize the code, leaving it to the programmer to figure out what to do.
Scalene is a new profiler for Python that aims to address these issues. It was developed by a team of computer scientists led by Professor Emery Berger at UMass Amherst. Scalene won the Best Paper Award at the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP) 2023.
Scalene is different from other profilers in several ways:
- It provides precise and actionable feedback on where and why the code is slow, highlighting the exact lines that need attention.
- It measures the CPU, GPU, and memory usage of each line of code, taking into account the interactions between them.
- It uses artificial intelligence to generate suggestions on how to optimize the code, leveraging the same technology behind ChatGPT, a state-of-the-art natural language generation system.
- It is fast and scalable, able to handle large and complex programs with minimal overhead.
How Scalene works
Scalene works by instrumenting the Python code at runtime, inserting probes that measure the time and resources consumed by each line of code. It then analyzes the collected data and identifies the hotspots, or regions of code that are responsible for most of the performance degradation.
Scalene then uses a neural network model trained on millions of lines of Python code to generate suggestions on how to improve the hotspots. The suggestions can include replacing inefficient functions or data structures with faster ones, parallelizing or vectorizing loops, offloading computations to GPUs or other devices, or using external libraries or tools.
Scalene presents its results in a user-friendly format, highlighting the lines of code that need optimization in different colors according to their CPU, GPU, and memory usage. It also shows the percentage of time and resources spent on each line, as well as the suggested improvements.
Scalene’s impact
Scalene has been tested on various Python programs and has shown impressive results. For example:
- Scalene sped up a program that computes prime numbers by 60,000 times, by replacing a naive algorithm with a more efficient one.
- Scalene sped up a program that performs matrix multiplication by 1,000 times, by using NumPy, a popular library for scientific computing in Python.
- Scalene sped up a program that simulates cellular automata by 100 times, by parallelizing it with Ray, a framework for distributed computing in Python.
Scalene is an open-source tool that anyone can use to boost their Python programs. It is available on GitHub and can be installed with pip, a package manager for Python. Scalene can also be used online through Google Colab, a cloud-based platform for interactive coding.
Scalene is not only a useful tool for programmers, but also a novel application of artificial intelligence for software engineering. It demonstrates how AI can assist humans in writing faster and better code.
