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        • Metrics
          • Objectness Score
          • Coco Metrics
          • IoU
      • MOT
        • SORT
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  • Related Topics
    • Intro
    • Python
      • Global Interpreter Lock (GIL)
      • Mutability
      • AsyncIO
    • SQL
    • Combinatorics
    • Data Engineering Questions
    • Distributed computation
      • About threads & processes
      • REST vs gRPC
  • Algorithms & data structures
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      • Online Stock Span
      • Two Sum
      • Best time to by and sell stock
      • Rank word combination
      • Largest subarray with zero sum
    • Binary
      • Sum of Two Integers
    • Tree
      • Maximum Depth of Binary Tree
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      • Invert/Flip Binary Tree
      • Binary Tree Paths
      • Binary Tree Maximum Path Sum
    • Matrix
      • Set Matrix Zeroes
    • Linked List
      • Reverse Linked List
      • Detect Cycle
      • Merge Two Sorted Lists
      • Merge k Sorted Lists
    • String
      • Longest Substring Without Repeating Characters
      • Longest Repeating Character Replacement
      • Minimum Window Substring
    • Interval
    • Graph
    • Heap
    • Dynamic Programming
      • Fibonacci
      • Grid Traveler
      • Can Sum
      • How Sum
      • Best Sum
      • Can Construct
      • Count Construct
      • All Construct
      • Climbing Stairs
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  • When is this a problem?
  • How to bypass it?

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  2. Python

Global Interpreter Lock (GIL)

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Last updated 3 years ago

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Overview

  • It's a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecodes at once

  • CPython supports multiple threads within a single interpreter, but threads must request access to the GIL in order to access any object or function.

    • Necessary due to CPython's memory management is not thread-safe.

      • Every python object has a reference counter and when it reaches zero, the garbage collector frees the memory space.

      • So, in order to avoid race conditions when updating every counter, a global mutex is a legit solution.

  • It's controversial because it prevents multithreaded CPython programs from taking full advantage of multiprocessor systems in certain situations. So, python in practice becomes single-processed.

When is this a problem?

  • This shouldn't be a stopper when dealing with I/O operations. This means different threads can access the GIL while other threads wait.

  • However, in CPU-intensive applications, this is an issue given you can't take advantage of multiple cores.

How to bypass it?

  • Another option to take advantage of multicore machines is creating processes instead of threads. However, this creates an overhead (by generating a copy of the parent memory state) and making less efficient the communication between processes (and shared variables).

C libraries like pandas, numpy, or numba use the CPython macros to .

Global Interpreter Lock
Thread State and the Global Interpreter Lock
Full video explanation
manually release the GIL
Distributed computation