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This article will explain in detail how to use GPU.js to improve JavaScript performance. The editor thinks it is very practical, so I share it with you as a reference. I hope you can get something after reading this article.

Using GPU.js to improve JavaScript performance

Have you ever tried to run a complex calculation, only to find that it takes a long time and slows you down?

There are many ways to solve this problem, such as using web worker or background threads. GPU reduces the processing load on CPU and gives CPU more space to deal with other processes. At the same time, web worker still runs on CPU, but on different threads.

In this beginner's guide, we will demonstrate how to use GPU.js to perform complex mathematical calculations and improve the performance of JavaScript applications.

What is GPU.js?

GPU.js is a JavaScript acceleration library built for Web and Node.js for general programming on graphics processing units (GPGPU). It allows you to hand over complex and time-consuming calculations to GPU instead of CPU for faster calculations and operations. There is also an alternative: in the absence of GPU on the system, these features will still run on regular JavaScript engines.

When you perform complex calculations, you essentially shift the burden to the system's GPU rather than CPU, increasing processing speed and time.

High performance computing is one of the main advantages of using GPU.js. If you want to do parallel computing in a browser without knowing WebGL, then GPU.js is a library for you.

Why use GPU.js

There are numerous reasons why you should use GPU to perform complex calculations, and there are too many reasons that cannot be discussed in an article. Here are some of the most noteworthy benefits of using GPU.

GPU can be used to perform massively parallel GPGPU computing. This is the type of calculation that needs to be done asynchronously.

When there is no GPU in the system, it gracefully falls back to JavaScript

GPU currently runs on browsers and Node.js, making it ideal for speeding up websites through a lot of computing

GPU.js is built with JavaScript in mind, so these features use the legal JavaScript syntax

If you think your processor is competent and you don't need GPU.js, take a look at the following results of GPU and CPU running the calculation.

As you can see, GPU is 22.97times faster than CPU.

The way GPU.js works

Given this level of speed, the JavaScript ecosystem seems to have a rocket to ride. GPU can help websites load faster, especially those that have to perform complex calculations on the home page. You no longer need to worry about using background threads and loaders, because GPU runs calculations 22.97 times faster than normal CPU.

The gpu.createKernel method creates a GPU accelerated kernel that is ported from the JavaScript function.

Running kernel functions in parallel with GPU results in faster computing-1-15 times faster, depending on your hardware.

Getting started with GPU.js

To show how to use GPU.js to calculate complex calculations more quickly, let's quickly start an actual demonstration.

Installation

Sudo apt install mesa-common-dev libxi-dev / / using Linux

Npm

Npm install gpu.js-- save / / OR yarn add gpu.js

Import GPU.js into your Node project.

Import {GPU} from ('gpu.js') / / OR const {GPU} = require (' gpu.js') const gpu = new GPU ()

Multiplication demonstration

In the following example, the calculation is done in parallel on the GPU.

First, generate a large amount of data.

Const getArrayValues = () = > {/ / create 2D arrary const values here = [], []] / / insert the value into the first array for (let y = 0; y < 600; ytree +) {values [0] .push ([]) values [1] .push ([]) / insert the value into the second array for (let x = 0; x < 600) X push +) {values\ [0\] [y] .push (Math.random ()) values\ [1\] [y] .push (Math.random ())}} / / returns the filled array return values}

Create a kernel (another word for a function running on GPU).

Const gpu = new GPU (); / / use the `createKernel () `method to multiply the array by const multiplyLargeValues = gpu.createKernel (function (a, b) {let sum = 0; for (let I = 0; I < 600; iSum +) {sum + = a\ [this.thread.y\] [I] * b\ [I\] [this.thread.x];} return sum;}) .setOutput ([600,600])

Invokes the kernel using the matrix as an argument.

Const largeArray = getArrayValues () const out = multiplyLargeValues (largeArray [0], largeArray [1])

Output

Console.log (out\ [y\] [x]) / / record the elements in row x and column y of the array console.log (out\ [10\] [12]) / / record the elements in rows 10 and 12 of the output array

Run the GPU benchmark

You can follow the steps specified on GitHub to run the benchmark.

Npm install @ gpujs/benchmark const benchmark = require ('@ gpujs/benchmark') const benchmarks = benchmark.benchmark (options)

The options object contains various configurations that can be passed to the benchmark.

Go to the official GPU.js website to see the complete computing benchmark, which will help you understand how fast you can get for complex calculations using GPU.js.

This is the end of this article on "how to use GPU.js to improve JavaScript performance". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, please share it for more people to see.

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