Python 3.13 is due to be released in October, yet the first release candidate was published earlier in August. Last year, I did a quick CPU bound benchmark of version 3.12 using Mandelbrot set calculation.
With all files in place, I downloaded version 3.13RC from the official web site and tested 3 versions of Python on my M1 Mac Book Pro:
user@users-MacBook-Pro py_chat_ui % python --version Python 3.11.9 user@users-MacBook-Pro py_chat_ui % python3 --version Python 3.12.5 user@users-MacBook-Pro py_chat_ui % python3.13 --version Python 3.13.0rc1
Here're the execution time (in seconds) for the implementation relying on NumPy (versions 3.11, 3.12 and 3.13RC1 correspondingly):
user@users-MacBook-Pro mandelbrot % python mandelbrot.py 1 Execution Time: 6.305059909820557 78513419 2 Execution Time: 6.282307863235474 78513419 3 Execution Time: 6.473501920700073 78513419 user@users-MacBook-Pro mandelbrot % python3 mandelbrot.py 1 Execution Time: 5.418003082275391 78513419 2 Execution Time: 5.412122011184692 78513419 3 Execution Time: 5.434246778488159 78513419 user@users-MacBook-Pro mandelbrot % python3.13 mandelbrot.py 1 Execution Time: 7.197513818740845 78513419 2 Execution Time: 7.212265968322754 78513419 3 Execution Time: 7.200297832489014 78513419
And the results for the pure Python implementation:
user@users-MacBook-Pro mandelbrot % python mandelbrot_pure.py 1 Execution Time: 41.18416976928711 78513425 2 Execution Time: 41.16466403007507 78513425 3 Execution Time: 41.148504972457886 78513425 4 Execution Time: 41.55486297607422 78513425 user@users-MacBook-Pro mandelbrot % python3 mandelbrot_pure.py 1 Execution Time: 49.806406021118164 78513425 2 Execution Time: 49.485753774642944 78513425 3 Execution Time: 49.52305006980896 78513425 4 Execution Time: 49.57118225097656 78513425 user@users-MacBook-Pro mandelbrot % python3.13 mandelbrot_pure.py 1 Execution Time: 41.07340693473816 78513425 2 Execution Time: 41.08624267578125 78513425 3 Execution Time: 41.09266400337219 78513425 4 Execution Time: 41.1431610584259 78513425
Frankly speaking, I am confused with the inconsistency in the results - newer doesn't mean better :)
P.S. >
For the reference, running Mandelbrot calculation with JiT compiled Dart program and AoT compiled C version:
user@users-MacBook-Pro mandelbrot % dart mandelbrot.dart 1 Execution Time: 0.476 78513425 2 Execution Time: 0.484 78513425 3 Execution Time: 0.475 78513425 user@users-MacBook-Pro mandelbrot % gcc -o mandelbrot mandelbrot.c -Ofast user@users-MacBook-Pro mandelbrot % ./mandelbrot 1 Execution Time: 0.256706 79394433 2 Execution Time: 0.234396 79394433 3 Execution Time: 0.234862 79394433
P.P.S. > Here's the GitHub repo with the same benchmark implemented in different languages.
P.P.P.S. > Here's last year's post comparing Python, Numba and Mojo.
The above is the detailed content of Python C a Quick CPU Benchmark. For more information, please follow other related articles on the PHP Chinese website!

Pythonisbothcompiledandinterpreted.WhenyourunaPythonscript,itisfirstcompiledintobytecode,whichisthenexecutedbythePythonVirtualMachine(PVM).Thishybridapproachallowsforplatform-independentcodebutcanbeslowerthannativemachinecodeexecution.

Python is not strictly line-by-line execution, but is optimized and conditional execution based on the interpreter mechanism. The interpreter converts the code to bytecode, executed by the PVM, and may precompile constant expressions or optimize loops. Understanding these mechanisms helps optimize code and improve efficiency.

There are many methods to connect two lists in Python: 1. Use operators, which are simple but inefficient in large lists; 2. Use extend method, which is efficient but will modify the original list; 3. Use the = operator, which is both efficient and readable; 4. Use itertools.chain function, which is memory efficient but requires additional import; 5. Use list parsing, which is elegant but may be too complex. The selection method should be based on the code context and requirements.

There are many ways to merge Python lists: 1. Use operators, which are simple but not memory efficient for large lists; 2. Use extend method, which is efficient but will modify the original list; 3. Use itertools.chain, which is suitable for large data sets; 4. Use * operator, merge small to medium-sized lists in one line of code; 5. Use numpy.concatenate, which is suitable for large data sets and scenarios with high performance requirements; 6. Use append method, which is suitable for small lists but is inefficient. When selecting a method, you need to consider the list size and application scenarios.

Compiledlanguagesofferspeedandsecurity,whileinterpretedlanguagesprovideeaseofuseandportability.1)CompiledlanguageslikeC arefasterandsecurebuthavelongerdevelopmentcyclesandplatformdependency.2)InterpretedlanguageslikePythonareeasiertouseandmoreportab

In Python, a for loop is used to traverse iterable objects, and a while loop is used to perform operations repeatedly when the condition is satisfied. 1) For loop example: traverse the list and print the elements. 2) While loop example: guess the number game until you guess it right. Mastering cycle principles and optimization techniques can improve code efficiency and reliability.

To concatenate a list into a string, using the join() method in Python is the best choice. 1) Use the join() method to concatenate the list elements into a string, such as ''.join(my_list). 2) For a list containing numbers, convert map(str, numbers) into a string before concatenating. 3) You can use generator expressions for complex formatting, such as ','.join(f'({fruit})'forfruitinfruits). 4) When processing mixed data types, use map(str, mixed_list) to ensure that all elements can be converted into strings. 5) For large lists, use ''.join(large_li

Pythonusesahybridapproach,combiningcompilationtobytecodeandinterpretation.1)Codeiscompiledtoplatform-independentbytecode.2)BytecodeisinterpretedbythePythonVirtualMachine,enhancingefficiencyandportability.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Zend Studio 13.0.1
Powerful PHP integrated development environment

DVWA
Damn Vulnerable Web App (DVWA) is a PHP/MySQL web application that is very vulnerable. Its main goals are to be an aid for security professionals to test their skills and tools in a legal environment, to help web developers better understand the process of securing web applications, and to help teachers/students teach/learn in a classroom environment Web application security. The goal of DVWA is to practice some of the most common web vulnerabilities through a simple and straightforward interface, with varying degrees of difficulty. Please note that this software

VSCode Windows 64-bit Download
A free and powerful IDE editor launched by Microsoft

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.

PhpStorm Mac version
The latest (2018.2.1) professional PHP integrated development tool
