Memory Management in CPython and Its Impact on Performance

Abhinav Upadhyay (~abhinav-upadhyay)


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Description:

How a programming language manages memory is a key factor impacting the performance of programs. How the runtime allocates memory for objects, what their memory layout is, and how and when they are destroyed are critical pieces that can help us debug performance issues in production. This talk will uncover the internals of memory management in Python and provide insights into its impact on the performance of your code.

Just like its syntax, the internal implementation Python is unique in many ways, and the same goes for how it manages memory. CPython uses specialized strategies for allocating memory and employs a combination of reference counting and garbage collection to clean up object trash. Each of these details has a direct impact on the execution performance of programs. Some of these aspects are within the control of the programmer, and knowing them can help you debug performance problems. In other cases, you will at least understand that the performance issue results from how the language works.

In this talk, we will start at allocation and understand what happens under the hood when creating objects. Then we will proceed towards garbage collection and discuss reference counting and the cyclic garbage collector—covering how these work, their impact on code execution, and potential ways to improve performance.

By the end of this session, you will have a solid understanding of Python's memory management system and how it affects your program’s performance, equipping you with the knowledge to debug and optimize your Python code more effectively.

Outline

  1. Introduction (_2 minutes_)

    • Why is it important to understand internals of memory management
    • What are we going to discuss
  2. Objects and Their Representation in CPython (_6 minutes_)

    • Everything is an object in Python, but what do objects contain internally?
      • Memory layout of objects:, GC header, object header and object body
    • Performance insights: Overhead of object headers and access through reference
  3. Object Allocation in CPython (_5 minutes_)
    • We now know how objects in Python look like. But what does it take to create them?
      • Small object allocation using the custom pymalloc allocator -- quick description of how it works
      • The new mimalloc allocator for NOGIL build -- quick highlight of its key features
      • Performance insights:
        • Potential memory fragmentation issues with the system's malloc when allocating large objects
        • Using object pools
        • Plugging custom allocators
  4. Reference Counting (_6 minutes_)
    • What is reference counting
    • How it works: (with GIL and without GIL)
    • Performance insights
      • Some positives of reference counting (situations where it is actually useful)
      • Overheads of reference counting:
        • dereferencing and modifying reference count on ever access
        • contention between threads even if just reading an object
        • CPU cache thrashing
        • Latency spikes when destroying a deeply nested tree of objects
  5. Cyclic Garbage Collector (_6 minutes_)
    • Why is a garbage collector required?
    • How does it work (just a quick summary without going into the implementation details)
    • Which kind of objects are tracked by the GC
    • When does the GC run and GC pauses
    • Performance insights:
      • Avoid cyclic references if you can
      • Tune thresholds
      • Disable GC around hot paths

Prerequisites:

General understanding of memory management, and how manual and automatic memory management work will be useful

Video URL:

https://www.youtube.com/watch?v=WFbqTJWZVhM

Speaker Info:

Abhinav Upadhyay is a seasoned software engineer with a robust background in computer science. He has contributed to various open-source projects and is a dev member of the NetBSD project. Abhinav has presented at several BSD conferences, including EuroBSDCon and AsiaBSDCon. He authors "Confessions of a Code Addict" on Substack, where he explores topics like compilers, programming languages, and database internals, making complex technical subjects accessible to a broad audience. His insights are highly valued in the tech community.

Section: Core Python
Type: Talk
Target Audience: Advanced
Last Updated: