Motivation

why vecxt

It is often prefereable to be able to perform sub-parts of a numeric algorithm to be executed as close to the user as possible. That usually comes with a painful pair of tradeoffs;

• sub native performance
• the "two / three platform" two / three sets of code problem

Vecxt challenges these constraints, by providing a single, cross platform, high performance, linear algebra library.

For example if your data acquisition is serverside, but do parts of a calculation in browser or in response to user input, saving the latency of network traffc and improving user experience in scalaJS.

[vecxt] provides easy access to platform native common vector operations via intuitive and common syntax. Guaranteeing platform native performance - wherever you want to do your numerics.

JVM

[vecxt] is cross platform, this example runs on the JVM, see Cross Platform for the same example running in scalaJS.

import vecxt.all.*
import narr.*
import vecxt.BoundsCheck.DoBoundsCheck.yes

def algo(a: NArray[Double], b :NArray[Double], c: Double ) = (a + b)  / c

val a = Array[Double](1, 2, 3)
// a: Array[Double] = Array(1.0, 2.0, 3.0)
val b = Array[Double](4, 5, 6)
// b: Array[Double] = Array(4.0, 5.0, 6.0)
val c = 2.0
// c: Double = 2.0

println(algo(a, b, c).mkString(", "))
// 2.5, 3.5, 4.5

It would be possible to write the algo method in the shared part of your project, and use it on any of the platforms - freeing you from the tyranny of having to choose a platform in advance.

It's also fun to write. And hopefully, fun to use !