simple-webgpu-compute

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Designed specifically for Biotech, Robotics, and Simulation Games

catalog of useful shader functions and patterns similar to stackGL but for webgpu

module for using webgpu for data visualization and more make compute shaders as simple as possible minimalist runtime for using webgpu shaders in nb/static website

requirements: textures compute shaders(image processing)

 import {init} from "https://cdn.skypack.dev/simple-data-texture/"


let options = {}

let draw = init(options)

let uniformData = {}

draw(uniformData)

 import {init, Texture} from "https://cdn.skypack.dev/simple-data-texture/"


let catTexture = Texture('http://giphy.com/cat_pic.png');
let dogTexture = Texture('http://giphy.com/dog.png');

let draw = init({
  data: {time: 123, texture:catTexture}
})

draw({texture: dogTexture})

cat.subImage({
  width: 200, height: 200,
  data: dogTexture.read({x:5,y:5,w:200,h: 200})
}, 1,1)

//todo make api easier //todo make textures stream and pipe through compute

this library currently just draws a quad and allows custom shaders with minimal boilerplate custom uniforms and textures

• TopLevel Functions init -> returns draw Texture

Default Options

Width x Height

1. passed in explicitly
2. check canvas
3. if neither 1,2 default to 900x500

• References http://vis.stanford.edu/files/2013-imMens-EuroVis.pdf (what is the right name for data-cubes/megatextures/virtual-streaming-textures/etc??) https://observablehq.com/@observablehq/downloading-and-embedding-notebooks

techniques for faster load time (start with lossy to load instantly, fill in with accurate data as user gets closer adjusting level of detail ) png/jpeg https://developers.google.com/protocol-buffers column-based data-stores like arrow/cassandra mega-textures from id-engine-5 (virtual-streaming textures ) immens / nanocubes https://nanocubes.net/ https://www1.nyc.gov/site/doitt/initiatives/3d-building.page https://explorer.morphocode.com/map

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