GPU.js is a JavaScript Acceleration library for GPGPU (General purpose computing on GPUs) in JavaScript for Web and Node. GPU.js automatically transpiles simple JavaScript functions into shader language and compiles them so they run on your GPU. In case a GPU is not available, the functions will still run in regular JavaScript. For some more quick concepts, see Quick Concepts on the wiki.
Creates a GPU accelerated kernel transpiled from a javascript function that computes a single element in the 512 x 512 matrix (2D array). The kernel functions are ran in tandem on the GPU often resulting in very fast computations! You can run a benchmark of this here. Typically, it will run 1-15x faster depending on your hardware. You can experiment around with the kernel playground here Matrix multiplication (perform matrix multiplication on 2 matrices of size 512 x 512) written in GPU.js:
<script src="dist/gpu-browser.min.js"></script>
<script>
// GPU is a constructor and namespace for browser
const gpu = new GPU();
const multiplyMatrix = gpu.createKernel(function(a, b) {
let sum = 0;
for (let i = 0; i < 512; i++) {
sum += a[this.thread.y][i] * b[i][this.thread.x];
}
return sum;
}).setOutput([512, 512]);
const c = multiplyMatrix(a, b);
</script>
const { GPU } = require('gpu.js');
const gpu = new GPU();
const multiplyMatrix = gpu.createKernel(function(a, b) {
let sum = 0;
for (let i = 0; i < 512; i++) {
sum += a[this.thread.y][i] * b[i][this.thread.x];
}
return sum;
}).setOutput([512, 512]);
const c = multiplyMatrix(a, b);
import { GPU } from 'gpu.js';
const gpu = new GPU();
const multiplyMatrix = gpu.createKernel(function(a: number[][], b: number[][]) {
let sum = 0;
for (let i = 0; i < 512; i++) {
sum += a[this.thread.y][i] * b[i][this.thread.x];
}
return sum;
}).setOutput([512, 512]);
const c = multiplyMatrix(a, b) as number[][];
NOTE: documentation is slightly out of date for the upcoming release of v2. We will fix it! In the mean time, if you'd like to assist (PLEASE) let us know.
- Installation
GPU
Settingsgpu.createKernel
Settings- Creating and Running Functions
- Debugging
- Accepting Input
- Graphical Output
- Combining Kernels
- Create Kernel Map
- Adding Custom Functions
- Adding Custom Functions Directly to Kernel
- Types
- Loops
- Pipelining
- Offscreen Canvas
- Cleanup
- Flattened typed array support
- Precompiled and Lighter Weight Kernels
- Supported Math functions
- How to check what is supported
- Typescript Typings
- Dealing With Transpilation
- Full API reference
- Contributors
- Contributing
- How possible in node
- Terms Explained
- License
On Linux, ensure you have the correct header files installed: sudo apt install mesa-common-dev libxi-dev
(adjust for your distribution)
npm install gpu.js --save
yarn add gpu.js
const { GPU } = require('gpu.js');
const gpu = new GPU();
import { GPU } from 'gpu.js';
const gpu = new GPU();
Download the latest version of GPU.js and include the files in your HTML page using the following tags:
<script src="dist/gpu-browser.min.js"></script>
<script>
const gpu = new GPU();
</script>
Settings are an object used to create an instance of GPU
. Example: new GPU(settings)
canvas
:HTMLCanvasElement
. Optional. For sharing canvas. Example: use THREE.js and GPU.js on same canvas.context
:WebGL2RenderingContext
orWebGLRenderingContext
. For sharing rendering context. Example: use THREE.js and GPU.js on same rendering context.mode
: Defaults to 'gpu', other values generally for debugging:- 'dev' New in V2!: VERY IMPORTANT! Use this so you can breakpoint and debug your kernel! This wraps your javascript in loops but DOES NOT transpile your code, so debugging is much easier.
- 'webgl': Use the
WebGLKernel
for transpiling a kernel - 'webgl2': Use the
WebGL2Kernel
for transpiling a kernel - 'headlessgl' New in V2!: Use the
HeadlessGLKernel
for transpiling a kernel - 'cpu': Use the
CPUKernel
for transpiling a kernel
Settings are an object used to create a kernel
or kernelMap
. Example: gpu.createKernel(settings)
output
orkernel.setOutput(output)
:array
orobject
that describes the output of kernel. When usingkernel.setOutput()
you can call it after the kernel has compiled ifkernel.dynamicOutput
istrue
, to resize your output. Example:- as array:
[width]
,[width, height]
, or[width, height, depth]
- as object:
{ x: width, y: height, z: depth }
- as array:
pipeline
orkernel.setPipeline(true)
New in V2!: boolean, default =false
- Causes
kernel()
calls to output aTexture
. To get array's from aTexture
, use:
const result = kernel(); result.toArray();
- Can be passed directly into kernels, and is preferred:
kernel(texture);
- Causes
graphical
orkernel.setGraphical(boolean)
: boolean, default =false
loopMaxIterations
orkernel.setLoopMaxIterations(number)
: number, default = 1000constants
orkernel.setConstants(object)
: object, default = nulldynamicOutput
orkernel.setDynamicOutput(boolean)
: boolean, default = false - turns dynamic output on or offdynamicArguments
orkernel.setDynamicArguments(boolean)
: boolean, default = false - turns dynamic arguments (use different size arrays and textures) on or offoptimizeFloatMemory
orkernel.setOptimizeFloatMemory(boolean)
New in V2!: boolean - causes a float32 texture to use all 4 channels rather than 1, using less memory, but consuming more GPU.precision
orkernel.setPrecision('unsigned' | 'single')
New in V2!: 'single' or 'unsigned' - if 'single' output texture uses float32 for each colour channel rather than 8fixIntegerDivisionAccuracy
orkernel.setFixIntegerDivisionAccuracy(boolean)
: boolean - some cards have accuracy issues dividing by factors of three and some other primes (most apple kit?). Default on for affected cards, disable if accuracy not required.functions
orkernel.setFunctions(object)
: array, array of functions to be used inside kernel. If undefined, inherits fromGPU
instance.nativeFunctions
orkernel.setNativeFunctions(object)
: object, defined as:{ name: string, source: string, settings: object }
. This is generally set via using GPU.addNativeFunction()- VERY IMPORTANT! - Use this to add special native functions to your environment when you need specific functionality is needed.
injectedNative
orkernel.setInjectedNative(string)
New in V2!: string, defined as:{ functionName: functionSource }
. This is for injecting native code before translated kernel functions.subKernels
orkernel.setSubKernels(array)
: array, generally inherited fromGPU
instance.immutable
orkernel.setImmutable(boolean)
: boolean, default =false
strictIntegers
orkernel.setStrictIntegers(boolean)
: boolean, default =false
- allows undefined argumentTypes and function return values to use strict integer declarations.useLegacyEncoder
orkernel.setUseLegacyEncoder(boolean)
: boolean, defaultfalse
- more info here.warnVarUsage
orkernel.setWarnVarUsage(boolean)
: turn off var usage warnings, they can be irritating, and in transpiled environments, there is nothing we can do about it.tactic
orkernel.setTactic('speed' | 'balanced' | 'performance')
New in V2!: Set the kernel's tactic for compilation. Allows for compilation to better fit how GPU.js is being used (internally useslowp
for 'speed',mediump
for 'balanced', andhighp
for 'precision'). Default is 'balanced'.
Depending on your output type, specify the intended size of your output. You cannot have an accelerated function that does not specify any output size.
Output size | How to specify output size | How to reference in kernel |
---|---|---|
1D | [length] |
value[this.thread.x] |
2D | [width, height] |
value[this.thread.y][this.thread.x] |
3D | [width, height, depth] |
value[this.thread.z][this.thread.y][this.thread.x] |
const settings = {
output: [100]
};
or
// You can also use x, y, and z
const settings = {
output: { x: 100 }
};
Create the function you want to run on the GPU. The first input parameter to createKernel
is a kernel function which will compute a single number in the output. The thread identifiers, this.thread.x
, this.thread.y
or this.thread.z
will allow you to specify the appropriate behavior of the kernel function at specific positions of the output.
const kernel = gpu.createKernel(function() {
return this.thread.x;
}, settings);
The created function is a regular JavaScript function, and you can use it like one.
kernel();
// Result: Float32Array[0, 1, 2, 3, ... 99]
Note: Instead of creating an object, you can use the chainable shortcut methods as a neater way of specifying settings.
const kernel = gpu.createKernel(function() {
return this.thread.x;
}).setOutput([100]);
kernel();
// Result: Float32Array[0, 1, 2, 3, ... 99]
GPU.js makes variable declaration inside kernel functions easy. Variable types supported are: Numbers Array(2) Array(3) Array(4)
Numbers example:
const kernel = gpu.createKernel(function() {
const i = 1;
const j = 0.89;
return i + j;
}).setOutput([100]);
Array(2) examples: Using declaration
const kernel = gpu.createKernel(function() {
const array2 = [0.08, 2];
return array2;
}).setOutput([100]);
Directly returned
const kernel = gpu.createKernel(function() {
return [0.08, 2];
}).setOutput([100]);
Array(3) example: Using declaration
const kernel = gpu.createKernel(function() {
const array2 = [0.08, 2, 0.1];
return array2;
}).setOutput([100]);
Directly returned
const kernel = gpu.createKernel(function() {
return [0.08, 2, 0.1];
}).setOutput([100]);
Array(4) example: Using declaration
const kernel = gpu.createKernel(function() {
const array2 = [0.08, 2, 0.1, 3];
return array2;
}).setOutput([100]);
Directly returned
const kernel = gpu.createKernel(function() {
return [0.08, 2, 0.1, 3];
}).setOutput([100]);
Debugging can be done in a variety of ways, and there are different levels of debugging.
- Debugging kernels with breakpoints can be done with
new GPU({ mode: 'dev' })
- This puts
GPU.js
into development mode. Here you can insert breakpoints, and be somewhat liberal in how your kernel is developed. - This mode does not actually "compile" (parse, and eval) a kernel, it simply iterates on your code.
- You can break a lot of rules here, because your kernel's function still has context of the state it came from.
- PLEASE NOTE: Mapped kernels are not supported in this mode. They simply cannot work because of context.
- Example:
const gpu = new GPU({ mode: 'dev' }); const kernel = gpu.createKernel(function(arg1, time) { // put a breakpoint on the next line, and watch it get hit const v = arg1[this.thread.y][this.thread.x * time]; return v; }, { output: [100, 100] });
- This puts
- Debugging actual kernels on CPU with
debugger
:- This will cause "breakpoint" like behaviour, but in an actual CPU kernel. You'll peer into the compiled kernel here, for a CPU.
- Example:
const gpu = new GPU({ mode: 'cpu' }); const kernel = gpu.createKernel(function(arg1, time) { debugger; // <--NOTICE THIS, IMPORTANT! const v = arg1[this.thread.y][this.thread.x * time]; return v; }, { output: [100, 100] });
- Debugging an actual GPU kernel:
- There are no breakpoints available on the GPU, period. By providing the same level of abstraction and logic, the above methods should give you enough insight to debug, but sometimes we just need to see what is on the GPU.
- Be VERY specific and deliberate, and use the kernel to your advantage, rather than just getting frustrated or giving up.
- Example:
In this example, we return early the value of x, to see exactly what it is. The rest of the logic is ignored, but now you can see the value that is calculated from
const gpu = new GPU({ mode: 'cpu' }); const kernel = gpu.createKernel(function(arg1, time) { const x = this.thread.x * time; return x; // <--NOTICE THIS, IMPORTANT! const v = arg1[this.thread.y][x]; return v; }, { output: [100, 100] });
x
, and debug it. This is an overly simplified problem. - Sometimes you need to solve graphical problems, that can be done similarly.
- Example:
Here we are making the canvas red or green depending on the value of
const gpu = new GPU({ mode: 'cpu' }); const kernel = gpu.createKernel(function(arg1, time) { const x = this.thread.x * time; if (x < 4 || x > 2) { // RED this.color(1, 0, 0); // <--NOTICE THIS, IMPORTANT! return; } if (x > 6 && x < 12) { // GREEN this.color(0, 1, 0); // <--NOTICE THIS, IMPORTANT! return; } const v = arg1[this.thread.y][x]; return v; }, { output: [100, 100], graphical: true });
x
.
- Numbers
- 1d,2d, or 3d Array of numbers
- Arrays of
Array
,Float32Array
,Int16Array
,Int8Array
,Uint16Array
,uInt8Array
- Arrays of
- Pre-flattened 2d or 3d Arrays using 'Input', for faster upload of arrays
- Example:
const { input } = require('gpu.js'); const value = input(flattenedArray, [width, height, depth]);
- HTML Image
- Array of HTML Images
- Video Element New in V2! To define an argument, simply add it to the kernel function like regular JavaScript.
const kernel = gpu.createKernel(function(x) {
return x;
}).setOutput([100]);
kernel(42);
// Result: Float32Array[42, 42, 42, 42, ... 42]
Similarly, with array inputs:
const kernel = gpu.createKernel(function(x) {
return x[this.thread.x % 3];
}).setOutput([100]);
kernel([1, 2, 3]);
// Result: Float32Array[1, 2, 3, 1, ... 1 ]
An HTML Image:
const kernel = gpu.createKernel(function(image) {
const pixel = image[this.thread.y][this.thread.x];
this.color(pixel[0], pixel[1], pixel[2], pixel[3]);
})
.setGraphical(true)
.setOutput([100, 100]);
const image = new document.createElement('img');
image.src = 'my/image/source.png';
image.onload = () => {
kernel(image);
// Result: colorful image
};
An Array of HTML Images:
const kernel = gpu.createKernel(function(image) {
const pixel = image[this.thread.z][this.thread.y][this.thread.x];
this.color(pixel[0], pixel[1], pixel[2], pixel[3]);
})
.setGraphical(true)
.setOutput([100, 100]);
const image1 = new document.createElement('img');
image1.src = 'my/image/source1.png';
image1.onload = onload;
const image2 = new document.createElement('img');
image2.src = 'my/image/source2.png';
image2.onload = onload;
const image3 = new document.createElement('img');
image3.src = 'my/image/source3.png';
image3.onload = onload;
const totalImages = 3;
let loadedImages = 0;
function onload() {
loadedImages++;
if (loadedImages === totalImages) {
kernel([image1, image2, image3]);
// Result: colorful image composed of many images
}
};
An HTML Video: New in V2!
const kernel = gpu.createKernel(function(videoFrame) {
const pixel = videoFrame[this.thread.y][this.thread.x];
this.color(pixel[0], pixel[1], pixel[2], pixel[3]);
})
.setGraphical(true)
.setOutput([100, 100]);
const video = new document.createElement('video');
video.src = 'my/video/source.webm';
kernel(image); //note, try and use requestAnimationFrame, and the video should be ready or playing
// Result: video frame
Sometimes, you want to produce a canvas
image instead of doing numeric computations. To achieve this, set the graphical
flag to true
and the output dimensions to [width, height]
. The thread identifiers will now refer to the x
and y
coordinate of the pixel you are producing. Inside your kernel function, use this.color(r,g,b)
or this.color(r,g,b,a)
to specify the color of the pixel.
For performance reasons, the return value of your function will no longer be anything useful. Instead, to display the image, retrieve the canvas
DOM node and insert it into your page.
const render = gpu.createKernel(function() {
this.color(0, 0, 0, 1);
})
.setOutput([20, 20])
.setGraphical(true);
render();
const canvas = render.canvas;
document.getElementsByTagName('body')[0].appendChild(canvas);
Note: To animate the rendering, use requestAnimationFrame
instead of setTimeout
for optimal performance. For more information, see this.
To make it easier to get pixels from a context, use kernel.getPixels()
, which returns a flat array similar to what you get from WebGL's readPixels
method.
A note on why: webgl's readPixels
returns an array ordered differently from javascript's getImageData
.
This makes them behave similarly.
While the values may be somewhat different, because of graphical precision available in the kernel, and alpha, this allows us to easily get pixel data in unified way.
Example:
const render = gpu.createKernel(function() {
this.color(0, 0, 0, 1);
})
.setOutput([20, 20])
.setGraphical(true);
render();
const pixels = render.getPixels();
// [r,g,b,a, r,g,b,a...
Currently, if you need alpha do something like enabling premultipliedAlpha
with your own gl context:
const canvas = DOM.canvas(500, 500);
const gl = canvas.getContext('webgl2', { premultipliedAlpha: false });
const gpu = new GPU({
canvas,
context: gl
});
const krender = gpu.createKernel(function(x) {
this.color(this.thread.x / 500, this.thread.y / 500, x[0], x[1]);
})
.setOutput([500, 500])
.setGraphical(true);
Sometimes you want to do multiple math operations on the gpu without the round trip penalty of data transfer from cpu to gpu to cpu to gpu, etc. To aid this there is the combineKernels
method.
Note: Kernels can have different output sizes.
const add = gpu.createKernel(function(a, b) {
return a[this.thread.x] + b[this.thread.x];
}).setOutput([20]);
const multiply = gpu.createKernel(function(a, b) {
return a[this.thread.x] * b[this.thread.x];
}).setOutput([20]);
const superKernel = gpu.combineKernels(add, multiply, function(a, b, c) {
return multiply(add(a, b), c);
});
superKernel(a, b, c);
This gives you the flexibility of using multiple transformations but without the performance penalty, resulting in a much much MUCH faster operation.
Sometimes you want to do multiple math operations in one kernel, and save the output of each of those operations. An example is Machine Learning where the previous output is required for back propagation. To aid this there is the createKernelMap
method.
const megaKernel = gpu.createKernelMap({
addResult: function add(a, b) {
return a + b;
},
multiplyResult: function multiply(a, b) {
return a * b;
},
}, function(a, b, c) {
return multiply(add(a[this.thread.x], b[this.thread.x]), c[this.thread.x]);
}, { output: [10] });
megaKernel(a, b, c);
// Result: { addResult: Float32Array, multiplyResult: Float32Array, result: Float32Array }
const megaKernel = gpu.createKernelMap([
function add(a, b) {
return a + b;
},
function multiply(a, b) {
return a * b;
}
], function(a, b, c) {
return multiply(add(a[this.thread.x], b[this.thread.x]), c[this.thread.x]);
}, { output: [10] });
megaKernel(a, b, c);
// Result: { 0: Float32Array, 1: Float32Array, result: Float32Array }
This gives you the flexibility of using parts of a single transformation without the performance penalty, resulting in much much MUCH faster operation.
use gpu.addFunction(function() {}, settings)
for adding custom functions. Example:
gpu.addFunction(function mySuperFunction(a, b) {
return a - b;
});
function anotherFunction(value) {
return value + 1;
}
gpu.addFunction(anotherFunction);
const kernel = gpu.createKernel(function(a, b) {
return anotherFunction(mySuperFunction(a[this.thread.x], b[this.thread.x]));
}).setOutput([20]);
To manually strongly type a function you may use settings. By setting this value, it makes the build step of the kernel less resource intensive. Settings take an optional hash values:
returnType
: optional, defaults to inference fromFunctionBuilder
, the value you'd like to return from the function.argumentTypes
: optional, defaults to inference fromFunctionBuilder
for each param, a hash of param names with values of the return types.
Example:
gpu.addFunction(function mySuperFunction(a, b) {
return [a - b[1], b[0] - a];
}, { argumentTypes: { a: 'Number', b: 'Array(2)'}, returnType: 'Array(2)' });
function mySuperFunction(a, b) {
return a - b;
}
const kernel = gpu.createKernel(function(a, b) {
return mySuperFunction(a[this.thread.x], b[this.thread.x]);
})
.setOutput([20])
.setFunctions([mySuperFunction]);
GPU.js does type inference when types are not defined, so even if you code weak type, you are typing strongly typed. This is needed because c++, which glsl is a subset of, is, of course, strongly typed. Types that can be used with GPU.js are as follows:
- 'Array'
- 'Array(2)' New in V2!
- 'Array(3)' New in V2!
- 'Array(4)' New in V2!
- 'Array1D(2)' New in V2!
- 'Array1D(3)' New in V2!
- 'Array1D(4)' New in V2!
- 'Array2D(2)' New in V2!
- 'Array2D(3)' New in V2!
- 'Array2D(4)' New in V2!
- 'Array3D(2)' New in V2!
- 'Array3D(3)' New in V2!
- 'Array3D(4)' New in V2!
- 'HTMLImage'
- 'HTMLImageArray'
- 'HTMLVideo' New in V2!
- 'Number'
- 'Float'
- 'Integer'
- 'Boolean' New in V2!
NOTE: These refer the the return type of the kernel function, the actual result will always be a collection in the size of the defined output
- 'Array(2)'
- 'Array(3)'
- 'Array(4)'
- 'Number'
- 'Float'
- 'Integer'
Types generally used in the Texture
class, for #pipelining or for advanced usage.
- 'ArrayTexture(1)' New in V2!
- 'ArrayTexture(2)' New in V2!
- 'ArrayTexture(3)' New in V2!
- 'ArrayTexture(4)' New in V2!
- 'NumberTexture'
- 'MemoryOptimizedNumberTexture' New in V2!
- Any loops defined inside the kernel must have a maximum iteration count defined by the loopMaxIterations setting.
- Other than defining the iterations by a constant or fixed value as shown Dynamic sized via constants, you can also simply pass the number of iterations as a variable to the kernel
const matMult = gpu.createKernel(function(a, b) {
var sum = 0;
for (var i = 0; i < this.constants.size; i++) {
sum += a[this.thread.y][i] * b[i][this.thread.x];
}
return sum;
}, {
constants: { size: 512 },
output: [512, 512],
});
const matMult = gpu.createKernel(function(a, b) {
var sum = 0;
for (var i = 0; i < 512; i++) {
sum += a[this.thread.y][i] * b[i][this.thread.x];
}
return sum;
}).setOutput([512, 512]);
Pipeline is a feature where values are sent directly from kernel to kernel via a texture.
This results in extremely fast computing. This is achieved with the kernel setting pipeline: boolean
or by calling kernel.setPipeline(true)
const kernel1 = gpu.createKernel(function(v) {
return v[this.thread.x];
})
.setPipeline(true)
.setOutput([100]);
const kernel2 = gpu.createKernel(function(v) {
return v[this.thread.x];
})
.setOutput([100]);
const result1 = kernel1(array);
// Result: Texture
console.log(result1.toArray());
// Result: Float32Array[0, 1, 2, 3, ... 99]
const result2 = kernel2(result1);
// Result: Float32Array[0, 1, 2, 3, ... 99]
GPU.js supports offscreen canvas where available. Here is an example of how to use it with two files, gpu-worker.js
, and index.js
:
file: gpu-worker.js
importScripts('path/to/gpu.js');
onmessage = function() {
// define gpu instance
const gpu = new GPU();
// input values
const a = [1,2,3];
const b = [3,2,1];
// setup kernel
const kernel = gpu.createKernel(function(a, b) {
return a[this.thread.x] - b[this.thread.x];
})
.setOutput([3]);
// output some results!
postMessage(kernel(a, b));
};
file: index.js
var worker = new Worker('gpu-worker.js');
worker.onmessage = function(e) {
var result = e.data;
console.log(result);
};
- for instances of
GPU
use thedestroy
method. Example:gpu.destroy()
- for instances of
Kernel
use thedestroy
method. Example:kernel.destroy()
To use the useful x
, y
, z
thread
lookup api inside of GPU.js, and yet use flattened arrays, there is the Input
type.
This is generally much faster for when sending values to the gpu, especially with larger data sets. Usage example:
const { GPU, input, Input } = require('gpu.js');
const gpu = new GPU();
const kernel = gpu.createKernel(function(a, b) {
return a[this.thread.y][this.thread.x] + b[this.thread.y][this.thread.x];
}).setOutput([3,3]);
kernel(
input(
new Float32Array([1,2,3,4,5,6,7,8,9]),
[3, 3]
),
input(
new Float32Array([1,2,3,4,5,6,7,8,9]),
[3, 3]
)
);
Note: input(value, size)
is a simple pointer for new Input(value, size)
GPU.js packs a lot of functionality into a single file, such as a complete javascript parse, which may not be needed in some cases.
To aid in keeping your kernels lightweight, the kernel.toJSON()
method was added.
This allows you to reuse a previously built kernel, without the need to re-parse the javascript.
Here is an example:
const gpu = new GPU();
const kernel = gpu.createKernel(function() {
return [1,2,3,4];
}, { output: [1] });
console.log(kernel()); // [Float32Array([1,2,3,4])];
const json = kernel.toJSON();
const newKernelFromJson = gpu.createKernel(json);
console.log(newKernelFromJSON()); // [Float32Array([1,2,3,4])];
NOTE: There is lighter weight, pre-built, version of GPU.js to assist with serializing from to and from json in the dist folder of the project, which include:
GPU.js supports seeing exactly how it is interacting with the graphics processor by means of the kernel.toString(...)
method.
This method, when called, creates a kernel that executes exactly the instruction set given to the GPU as a function that sets up a kernel.
Here is an example:
const gpu = new GPU();
const kernel = gpu.createKernel(function(a) {
let sum = 0;
for (let i = 0; i < 6; i++) {
sum += a[this.thread.x][i];
}
return sum;
}, { output: [6] });
kernel(input(a, [6, 6]));
const kernelString = kernel.toString(input(a, [6, 6]));
const newKernel = new Function('return ' + kernelString)()(context);
newKernel(input(a, [6, 6]));
Since the code running in the kernel is actually compiled to GLSL code, not all functions from the JavaScript Math module are supported.
This is a list of the supported ones:
Math.abs()
Math.acos()
Math.asin()
Math.atan()
Math.atan2()
Math.ceil()
Math.cos()
Math.exp()
Math.floor()
Math.log()
Math.log2()
Math.max()
Math.min()
Math.pow()
Math.random()
- A note on random. We use a plugin to generate random. Random seeded and generated, both from the GPU, is not as good as random from the CPU as there are more things that the CPU can seed random from. However, we seed random on the GPU, from a random value in the CPU. We then seed the subsequent randoms from the previous random value. So we seed from CPU, and generate from GPU. Which is still not as good as CPU, but closer. While this isn't perfect, it should suffice in most scenarios.
Math.round()
Math.sign()
Math.sin()
Math.sqrt()
Math.tan()
To assist with mostly unit tests, but perhaps in scenarios outside of GPU.js, there are the following logical checks to determine what support level the system executing a GPU.js kernel may have:
GPU.disableValidation()
- turn off all kernel validationGPU.enableValidation()
- turn on all kernel validationGPU.isGPUSupported
:boolean
- checks if GPU is in-fact supportedGPU.isKernelMapSupported
:boolean
- checks if kernel maps are supportedGPU.isOffscreenCanvasSupported
:boolean
- checks if offscreen canvas is supportedGOU.isWebGLSupported
:boolean
- checks if WebGL v1 is supportedGOU.isWebGL2Supported
:boolean
- checks if WebGL v2 is supportedGPU.isHeadlessGLSupported
:boolean
- checks if headlessgl is supportedGPU.isCanvasSupported
:boolean
- checks if canvas is supportedGPU.isGPUHTMLImageArraySupported
:boolean
- checks if the platform supports HTMLImageArray'sGPU.isSinglePrecisionSupported
:boolean
- checks if the system supports single precision float 32 values
Typescript is supported! Typings can be found here!
Transpilation doesn't do the best job of keeping code beautiful. To aid in this endeavor GPU.js can handle some scenarios to still aid you harnessing the GPU in less than ideal circumstances. Here is a list of a few things that GPU.js does to fix transpilation:
- When a transpiler such as Babel changes
myCall()
to(0, _myCall.myCall)
, it is gracefully handled. - Using
var
will have a lot of warnings by default, this can be irritating because sometimes there is nothing we can do about this in transpiled environment. To aid in the irritation, there is an option to alleviate the irritation. Whenconst
andlet
are converted tovar
, and you'r prefer not to see it, use the following:const kernel = gpu.createKernel(myKernelFunction) .setWarnVarUsage(false); // or const kernel = gpu.createKernel(myKernelFunction, { output: [1], warnVarUsage: false });
You can find a complete API reference here.
GPU.js uses HeadlessGL in node for GPU acceleration. GPU.js is written in such a way, you can introduce your own backend. Have a suggestion? We'd love to hear it!
- Kernel - A function that is tightly coupled to program that runs on the Graphic Processor
- Texture - A graphical artifact that is packed with data, in the case of GPU.js, bit shifted parts of a 32 bit floating point decimal
Contributors are welcome! Create a merge request to the develop
branch and we
will gladly review it. If you wish to get write access to the repository,
please email us and we will review your application and grant you access to
the develop
branch.
We promise never to pass off your code as ours.
If you have an issue, either a bug or a feature you think would benefit your project let us know and we will do our best.
Create issues here and follow the template.
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