/contrib/famzah

Enthusiasm never stops


49 Comments

C++ vs. Python vs. PHP vs. Java vs. Others performance benchmark (2016 Q3)

The benchmarks here do not try to be complete, as they are showing the performance of the languages in one aspect, and mainly: loops, dynamic arrays with numbers, basic math operations.

This is an improved redo of the tests done in previous years. You are strongly encouraged to read the additional information about the tests in the article.

Here are the benchmark results:

Language CPU time Slower than Language
version
Source
code
User System Total C++ previous
C++ (optimized with -O2) 0.899 0.053 0.951 g++ 6.1.1 link
Rust 0.898 0.129 1.026 7% 7% 1.12.0 link
Java 8 (non-std lib) 1.090 0.006 1.096 15% 6% 1.8.0_102 link
Python 2.7 + PyPy 1.376 0.120 1.496 57% 36% PyPy 5.4.1 link
C# .NET Core Linux 1.583 0.112 1.695 78% 13% 1.0.0-preview2 link
Javascript (nodejs) 1.371 0.466 1.837 93% 8% 4.3.1 link
Go 2.622 0.083 2.705 184% 47% 1.7.1 link
C++ (not optimized) 2.921 0.054 2.975 212% 9% g++ 6.1.1 link
PHP 7.0 6.447 0.178 6.624 596% 122% 7.0.11 link
Java 8 (see notes) 12.064 0.080 12.144 1176% 83% 1.8.0_102 link
Ruby 12.742 0.230 12.972 1263% 6% 2.3.1 link
Python 3.5 17.950 0.126 18.077 1800% 39% 3.5.2 link
Perl 25.054 0.014 25.068 2535% 38% 5.24.1 link
Python 2.7 25.219 0.114 25.333 2562% 1% 2.7.12 link

The big difference this time is that we use a slightly modified benchmark method. Programs are no longer limited to just 10 loops. Instead they run for 90 wall-clock seconds, and then we divide and normalize their performance as if they were running for only 10 loops. This way we can compare with the previous results. The benefit of doing the tests like this is that the startup and shutdown times of the interpreters should make almost no difference now. It turned out that the new method doesn’t significantly change the outcome compared to the previous benchmark runs, which is good as the old way of benchmarks seems also correct.

For the curious readers, the raw results also show the maximum used memory (RSS).

Brief analysis of the results:

  • Rust, which we benchmark for the first time, is very fast. πŸ™‚
  • C# .NET Core on Linux, which we also benchmark for the first time, performs very well by being as fast as NodeJS and only 78% slower than C++. Memory usage peak was at 230 MB which is the same as Python 3.5 and PHP 7.0, and two times less than Java 8 and NodeJS.
  • NodeJS version 4.3.x got much slower than the previous major version 4.2.x. This is the only surprise. It turned out to be a minor glitch in the parser which was easy to fix. NodeJS 4.3.x is performing the same as 4.2.x.
  • Python and Perl seem a bit slower than before but this is probably due to the fact that C++ performed even better because of the new benchmark method.
  • Java 8 didn’t perform much faster as we expected. Maybe it gets slower as more and more loops are done, which also allocated more RAM.
  • Also review the analysis in the old 2016 tests for more information.

The tests were run on a Debian Linux 64-bit machine.

You can download the source codes, raw results, and the benchmark batch script at:
https://github.com/famzah/langs-performance

Update @ 2016-10-15: Added the Rust implementation. The minor versions of some languages were updated as well.
Update @ 2016-10-19: A redo which includes the NodeJS fix.
Update @ 2016-11-04: Added the C# .NET Core implementation.


39 Comments

C++ vs. Python vs. Perl vs. PHP performance benchmark (2016)

There are newer benchmarks: C++ vs. Python vs. PHP vs. Java vs. Others performance benchmark (2016 Q3)

The benchmarks here do not try to be complete, as they are showing the performance of the languages in one aspect, and mainly: loops, dynamic arrays with numbers, basic math operations.

This is a redo of the tests done in previous years. You are strongly encouraged to read the additional information about the tests in the article.

Here are the benchmark results:

Language CPU time Slower than Language
version
Source
code
User System Total C++ previous
C++ (optimized with -O2) 0.952 0.172 1.124 g++ 5.3.1 link
Java 8 (non-std lib) 1.332 0.096 1.428 27% 27% 1.8.0_72 link
Python 2.7 + PyPy 1.560 0.160 1.720 53% 20% PyPy 4.0.1 link
Javascript (nodejs) 1.524 0.516 2.040 81% 19% 4.2.6 link
C++ (not optimized) 2.988 0.168 3.156 181% 55% g++ 5.3.1 link
PHP 7.0 6.524 0.184 6.708 497% 113% 7.0.2 link
Java 8 14.616 0.908 15.524 1281% 131% 1.8.0_72 link
Python 3.5 18.656 0.348 19.004 1591% 22% 3.5.1 link
Python 2.7 20.776 0.336 21.112 1778% 11% 2.7.11 link
Perl 25.044 0.236 25.280 2149% 20% 5.22.1 link
PHP 5.6 66.444 2.340 68.784 6020% 172% 5.6.17 link

The clear winner among the script languages is… PHP 7. πŸ™‚

Yes, that’s not a mistake. Apparently the PHP team did a great job! The rumor that PHP 7 is really fast confirmed for this particular benchmark test. You can also review the PHP 7 infographic by the Zend Performance Team.

Brief analysis of the results:

  • NodeJS got almost 2x faster.
  • Java 8 seems almost 2x slower.
  • Python has no significant change in the performance. Every new release is a little bit faster but overall Python is steadily 15x slower than C++.
  • Perl has the same trend as Python and is steadily 22x slower than C++.
  • PHP 5.x is the slowest with results between 47x to 60x behind C++.
  • PHP 7 made the big surprise. It is about 10x faster than PHP 5.x, and about 3x faster than Python which is the next fastest script language.

The tests were run on a Debian Linux 64-bit machine.

You can download the source codes, an Excel results sheet, and the benchmark batch script at:
https://github.com/famzah/langs-performance

 


6 Comments

C++ vs. Python vs. Perl vs. PHP performance benchmark (part #2)

This time we will focus on the startup time. The process start time is important if your processes are not persistent. If you are using FastCGI, mod_perl, mod_php, or mod_python, then these statistics are not so important to you. However, if you are spawning many processes which do something small and live for a very short time, then you should consider the CPU resources which get wasted while the script interpreter is being initialized.

The benchmarked scripts do only one thing – say “Hello, world” on the standard output. They do not include any additional modules in their source code – this may, or may not be your use-case. Though, very often the scripting languages have pretty many built-in functions, and for simple tasks you never need to include other modules.

Here are the benchmark results:

Language CPU time Slower than
User System Total C++ previous
C++ (with or w/o optimization) 2.568 3.536 6.051
Perl 12.561 6.096 18.723 209% 209%
PHP (w/o php.ini) 20.473 13.877 34.918 477% 86%
Python 27.014 11.881 39.318 550% 13%
Python + Psyco 32.986 14.845 48.132 695% 22%

The clear winner among the script languages this time is… Perl. πŸ™‚

All scripts were invoked 3000 times using the following Bash loop:

time ( i=3000 ; while [ “$i” -gt 0 ]; do $CMD >/dev/null ; i=$(($i-1)); done )

All tests were done on a Kubuntu Lucid box. The versions of the used software packages follow:

  • g++ (GNU project C and C++ compiler) 4.4.3
  • Python 2.6.5
  • Python Psyco 1.6 (1ubuntu2)
  • Perl 5.10.1
  • PHP 5.3.2 (1ubuntu4.2 with Suhosin-Patch), Zend Engine 2.3.0

The C++ implementation follows, click “show source” below to see the full source:

#include <iostream>
using namespace std;

int main() {
	cout << "Hello, world!\n";
	return 0;
}

The Perl implementation follows, click “show source” below to see the full source:

use strict;
use warnings;

print "Hello, world!\n";

The PHP implementation follows, click “show source” below to see the full source:

<?php
echo "Hello, world!\n";

The Python implementation follows, click “show source” below to see the full source:

#import psyco
#psyco.full()

print 'Hello, world!'

Update (Jan/14/2012): Copied the used test environment info here.


66 Comments

C++ vs. Python vs. Perl vs. PHP performance benchmark

Update: There areΒ newer benchmark results.


This all began as a colleague of mine stated that Python was so damn slow for maths. Which really astonished me and made me check it out, as my father told me once that he was very satisfied with Python, as it was very maths oriented.

The benchmarks here do not try to be complete, as they are showing the performance of the languages in one aspect, and mainly: loops, dynamic arrays with numbers, basic math operations.

Out of curiosity, Python was also benchmarked with and without the Psyco Python extension (now obsoleted by PyPy), which people say could greatly speed up the execution of any Python code without any modifications.

Here are the benchmark results:

Language CPU time Slower than Language
version
Source
code
User System Total C++ previous
C++ (optimized with -O2) 1,520 0,188 1,708 g++ 4.5.2 link
Java (non-std lib) 2,446 0,150 2,596 52% 52% 1.6.0_26 link
C++ (not optimized) 3,208 0,184 3,392 99% 31% g++ 4.5.2 link
Javascript (SpiderMonkey) see comment (SpiderMonkey seems as fast as C++ on Windows)
Javascript (nodejs) 4,068 0,544 4,612 170% 36% 0.8.8 link
Java 8,521 0,192 8,713 410% 150% 1.6.0_26 link
Python + Psyco 13,305 0,152 13,457 688% 54% 2.6.6 link
Ruby see comment (Ruby seems 35% faster than standard Python)
Python 27,886 0,168 28,054 1543% 108% 2.7.1 link
Perl 41,671 0,100 41,771 2346% 49% 5.10.1 link
PHP 5.4 roga’s blog results (PHP 5.4 seems 33% faster than PHP 5.3)
PHP 5.3 94,622 0,364 94,986 5461% 127% 5.3.5 link

The clear winner among the script languages is… Python. πŸ™‚

NodeJS JavaScript is pretty fast too, but internally it works more like a compiled language. See the comments below.

Please read the discussion about Java which I had with Isaac Gouy. He accused me that I am not comparing what I say am comparing. And also that I did not want to show how slow and how fast the Java example program can be. You deserve the whole story, so please read it if you are interested in Java.

Both PHP and Python are taking advantage of their built-in range() function, because they have one. This speeds up PHP by 5%, and Python by 20%.

The times include the interpretation/parsing phase for each language, but it’s so small that its significance is negligible. The math function is called 10 times, in order to have more reliable results. All scripts are using the very same algorithm to calculate the prime numbers in a given range. The correctness of the implementation is not so important, as we just want to check how fast the languages perform. The original Python algorithm was taken from http://www.daniweb.com/code/snippet216871.html.

The tests were run on an Ubuntu Linux machine.

You can download the source codes, an Excel results sheet, and the benchmark batch script at:
http://www.famzah.net/download/langs-performance/


Update (Jul/24/2010): Added the C++ optimized values.
Update (Aug/02/2010): Added a link to the benchmarks, part #2.
Update (Mar/31/2011): Using range() in PHP improves performance with 5%.
Update (Jan/14/2012): Re-organized the results summary table and the page. Added Java.
Update (Apr/02/2012): Added a link to PHP 5.4 vs. PHP 5.3 benchmarks.
Update (May/29/2012): Added the results for Java using a non-standard library.
Update (Jun/25/2012): Made the discussion about Java public, as well as added a note that range() is used for PHP and Python.
Update (Aug/31/2012): Updated benchmarks for the latest node.js.
Update (Oct/24/2012): Added the results for SpiderMonkey JavaScript.
Update (Jan/11/2013): Added the results for Ruby vs. Python and Nodejs.