The project makes use of Crystal's simple C bindings to leverage LAPACK power. The first operation implemented for testing was computing the inverse of a matrix, which first computes de LU decomposition of the matrix and then calculates the inverse from there.
A simple benchmark shows that the implementation works quite fast with a 10000 x 10000 float random matrix, taking less than 40 seconds:
require "benchmark" require "./src/crystalla" cols =  of Array(Float64) range = 10000 r = Random.new (1..range).each do |i| row =  of Float64 (1..range).each do |j| row.push r.next_float end cols.push row end m = Crystalla::Matrix.columns cols m.invert!
We decided to contrast this against numpy, a widely-used python package for numeric operations, which also leverages LAPACK for linear algebra.
import numpy SIZE = 10000 m = numpy.random.rand(SIZE, SIZE) numpy.linalg.inv(m)
time on this script yields one order of magnitude higher than Crystal:
real 3m38.331s user 3m36.148s sys 0m1.817s
We expect to continue adding basic functionality to crystalla over the following weeks. Feel free to contribute with your own additions!