Np (numphp) is numerical library with low memory footprint for php language. Suitable for large scale computing. php-8.0+ 64 bit is required!

0.0.1-alpha 2021-06-13 17:33 UTC

This package is auto-updated.

Last update: 2022-11-19 14:55:31 UTC


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Lite, Fast & Memory Efficient Mathematical PHP library for scientific computing

Np(numphp) is a library that provides objects for computing large sets of numbers in PHP.


Install Np into your project with Composer:

$ composer require ghostjat/np

##Sample Code

require __DIR__ . '/../vendor/autoload.php';
use Np\matrix;

$ta = matrix::randn(1000, 1000);    
$tb = matrix::randn(1000, 1000); // to generate random 2d matrix
$ta->dot($tb);                  // do a dot operation on given matrix
$ta->getMemory();              // get memory use
$ta->time();                  // get time
 * 7.7mb
 * Time-Consumed:- 0.18390893936157


This module is in its early stages and should be considered a Work in Progress.The interface is not final and may change in the future.


  • PHP 8+ 64bit with ffi & #libblas, #liblapacke

Make sure you have all the necessary tools installed such as FFI, libblas, liblapacke.


System Conf:- Intel(R) Core(TM) i3-2370M CPU @ 2.40GHz 64bit Memory:- 8GB php:- 8.0.5 64bit

Current Benchmarks of this library


Data Size :- [500x500] Revolutions:- 5 Iterations:- 5

subject mem_peak best mode mean worst stdev
sum 3.606mb 0.014s 0.014s 0.015s 0.015s 0.000s
multiply 8.589mb 0.070s 0.071s 0.071s 0.071s 0.000s
lu 4.648mb 0.064s 0.065s 0.065s 0.068s 0.001s
eign 2.801mb 0.085s 0.086s 0.086s 0.088s 0.001s
cholesky 1.621mb 0.001s 0.001s 0.001s 0.001s 0.000s
svd 3.706mb 0.126s 0.126s 0.127s 0.133s 0.002s
normL2 1.621mb 0.003s 0.003s 0.003s 0.003s 0.000s
Pinverse 4.903mb 0.156s 0.156s 0.158s 0.163s 0.003s
inverse 1.819mb 0.016s 0.016s 0.016s 0.017s 0.000s
normL1 1.621mb 0.001s 0.001s 0.001s 0.001s 0.000s
dotMatrix 3.769mb 0.006s 0.006s 0.006s 0.006s 0.000s
det 4.662mb 0.066s 0.066s 0.067s 0.067s 0.000s
rref 1.529mb 9.227s 9.271s 9.309s 9.427s 0.072s
ref 1.818mb 0.007s 0.008s 0.008s 0.008s 0.000s
clip 8.516mb 0.073s 0.076s 0.075s 0.077s 0.002s
clipUpper 8.516mb 0.055s 0.056s 0.057s 0.059s 0.002s
clipLower 8.516mb 0.055s 0.058s 0.057s 0.059s 0.002s
joinBelow 4.517mb 0.027s 0.027s 0.027s 0.028s 0.000s
transpose 8.504mb 0.057s 0.057s 0.058s 0.059s 0.001s
joinLeft 4.511mb 0.025s 0.025s 0.026s 0.027s 0.001s
poisson 1.590mb 0.029s 0.029s 0.029s 0.030s 0.000s
gaussian 20.203mb 0.056s 0.056s 0.056s 0.056s 0.000s
randn 1.528mb 0.017s 0.017s 0.017s 0.017s 0.000s
uniform 1.528mb 0.021s 0.021s 0.021s 0.022s 0.000s
multiply 4.507mb 0.042s 0.042s 0.043s 0.045s 0.001s

Previous BenchMark

benchmark subject set revs its mem_peak mode rstdev
eignBench eign 0 1 5 2.699mb 0.309s ±4.51%
svdBench svd 0 1 5 3.604mb 0.148s ±3.60%
poissonMatrixBench poisson 0 1 5 11.738mb 0.105s ±7.07%
gaussianMatrixBench gaussian 0 1 5 11.738mb 0.112s ±17.12%
randMatrixBench randn 0 1 5 1.429mb 0.048s ±2.37%
uniformMatrixBench uniform 0 1 5 1.429mb 0.063s ±8.16%
matrixTransposeBench transpose 0 1 5 8.431mb 0.120s ±1.32%
rrefBench rref 0 1 5 1.501mb 28.513s ±1.90%
refBench ref 0 1 5 1.731mb 0.023s ±7.24%
sumMatrixBench sum 0 1 5 2.434mb 0.051s ±3.59%
matrixPseudoInverseBench inverse 0 1 5 4.775mb 0.222s ±13.76%
matrixInverseBench inverse 0 1 5 1.731mb 0.032s ±127.50%
dotMatrixBench dotMatrix 0 1 5 3.656mb 0.013s ±27.94%
matrixL1NormBench normL1 0 1 10 1.525mb 0.001s ±0.80%
matrixL2NormBench normL2 0 1 10 1.525mb 0.003s ±1.63%


The code is licensed MIT and the documentation is licensed CC BY-NC 4.0.


Shubham Chaudhary