• SIMPLE WEBGPU COMPUTE - simplest GPGPU compute library ever

transpiler, shadergraph, ui for that,

[{ binding: 0, visibility: ShaderStage::Compute, buffer: { type: BufferBindingType::Uniform, hasDynamicOffset: 0, minBindingSize: 28 } }, { binding: 1, visibility: ShaderStage::Compute, buffer: { type: BufferBindingType::ReadOnlyStorage, hasDynamicOffset: 0, minBindingSize: 16 } }, { binding: 2, visibility: ShaderStage::Compute, buffer: { type: BufferBindingType::Storage, hasDynamicOffset: 0, minBindingSize: 16 }

}]

mental models made visual

//get test suite working //add one more compute example //one render pass and one compute per draw call

//3 days //boid simulation -> sheep or pokemon -> existing ismulation and add a image texture //image processing -> blur //falling box (if position > 0, dont fall)

//10 days //gravity simulation -> convert from rickkyreusser //water simulation -> convert from madebyevan

//for AI //neural net as shader //hand writing recognition //eye tracking for focus game //render output to screen as colors???

//this is for //book of simulations and image processing //physics of information theory

//erosion scene

//assume only one draw call and one compute call //

//visualizations that change the way you see the world //https://bost.ocks.org/mike/algorithms/ //https://pudding.cool/

simple-webgpu-compute

Please star https://github.com/gpuweb/gpuweb to support progress in Computer Graphics for 7 billion people

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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