# Triangulating Python Performance Issues with Scalene

arXiv: https://arxiv.org/abs/2212.07597 · HTML: https://mlsanity.com/paper/2212.07597/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2022.

## Built on by (most cited first, 5 of 5)

- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Learning Performance-Improving Code Edits](https://mlsanity.com/paper/2302.07867/) (2023)
- [Search-Based LLMs for Code Optimization](https://mlsanity.com/paper/2408.12159/) (2024)
- [Multi-Programming Language Sandbox for LLMs](https://mlsanity.com/paper/2410.23074/) (2024)
- [KEN: Kernel Extensions using Natural Language](https://mlsanity.com/paper/2312.05531/) (2023)

## Abstract (arXiv)

This paper proposes Scalene, a profiler specialized for Python. Scalene combines a suite of innovations to precisely and simultaneously profile CPU, memory, and GPU usage, all with low overhead. Scalene's CPU and memory profilers help Python programmers direct their optimization efforts by distinguishing between inefficient Python and efficient native execution time and memory usage. Scalene's memory profiler employs a novel sampling algorithm that lets it operate with low overhead yet high precision. It also incorporates a novel algorithm that automatically pinpoints memory leaks, whether within Python or across the Python-native boundary. Scalene tracks a new metric called copy volume, which highlights costly copying operations that can occur when Python silently converts between C and Python data representations, or between CPU and GPU. Since its introduction, Scalene has been widely adopted, with over 500,000 downloads to date. We present experience reports from developers who used Scalene to achieve significant performance improvements and memory savings.