imclab/weblas

GPU Powered BLAS for Browsers :gem:

GPU accelerated Javascript. Numerical computing in your browser with performance comparable to native.

Currently includes hundreds of unit tests, which verify correctness on hundreds of millions of data points.

Operations

Our focus is on numerical operations useful for neural networks and machine learning. So far, we've got 32-bit versions of each of these:

• sscal - Matrix (and Vector) Scale (with addition)
• sgemm - Matrix Multiply
• sdwns - Matrix (and Image) Downsample (for Max Pooling)
• sclmp - Matrix clamp (for ReLU)

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Usage

First, include the weblas.js file (from a release or the dist directory).

<script type="text/javascript" src="weblas.js"></script>

Then use it like this.

<script>


var h1 = 1024, w1 = 1024,
    h2 = 1024, w2 = 1024;

var A = new Float32Array(h1 * w1);
var B = new Float32Array(h2 * w2);

// fill A and B with science

var M = h1,
	N = w2,
	K = h2; // must match w1

var alpha = 1.0;
var beta = 0.0;
var C = new Float32Array(w2)      // specialized for neural net bias calculation

// result will contain matrix multiply of A x B (times alpha)
result = weblas.sgemm(M, N, K, alpha, A, B, beta, C);

</script>

Pipeline Mode

Pipeline mode gives (sometimes very large) increases in performance by leaving data in GPU memory. A demo illustrating performance on a deep neural net can be found here.

Here's a basic example:

// create Tensor containers for interacting directly with GPU memory
var t0 = weblas.pipeline.Tensor([M, K], data0);
// second matrix must be transposed
var t1 = weblas.pipeline.Tensor([N, K], weblas.util.transpose(K, N, data1));
var t2 = weblas.pipeline.Tensor([1, N], data2);
var alpha = 1.0;
var beta = 0.5;

/* NOTE: pipeline.sgemm takes a transpose matrix in the
  second slot (t1 here)
  (this requirement allows for improved performance)
 */
var t3 = weblas.pipeline.sgemm(alpha, t0, t1, beta, t2);

// result is a Float32Array
var result = t3.transfer();

More information can be found on the wiki Pipeline page.

Testing

Unit tests and benchmarks both require browserify and testling.

Install with:

npm install -g browserify
npm install -g testling

Unit Tests

All operations have unit test coverage. Unit tests use data generated outside the browser (to verify correctness). Generating the data requires python and the modules in requirements.txt.

With pip installed run:

pip install -r requirements.txt

Then, to generate the data, run:

npm run data

Then, run the unit tests with:

npm test

OS Setup

If the tests won't run, try this (it restores the default npm browser setting)

OSX

npm config set browser open

Linux

npm config set browser xdg-open

Windows

npm config set browser start

Benchmarks

After installing browserify and testling, run the benchmarks with:

npm run benchmark

results

[email protected]

TAP version 13
ok 1 128x128 . 128x128
# 316 ops/sec  ±4.80%  n = 51 µ = 3ms
ok 2 128x256 . 256x128
# 280 ops/sec  ±6.15%  n = 40 µ = 4ms
ok 3 256x256 . 256x256
# 171 ops/sec  ±14.79%  n = 47 µ = 6ms
ok 4 512x256 . 256x512
# 101 ops/sec  ±6.68%  n = 50 µ = 10ms
ok 5 256x512 . 512x256
# 139 ops/sec  ±3.64%  n = 49 µ = 7ms
ok 6 512x512 . 512x512
# 61.61 ops/sec  ±3.14%  n = 42 µ = 16ms
ok 7 513x513 . 513x513
# 52.92 ops/sec  ±8.82%  n = 49 µ = 19ms
ok 8 1024x512 . 512x1024
# 34.99 ops/sec  ±4.86%  n = 38 µ = 29ms
ok 9 512x1024 . 1024x512
# 52.03 ops/sec  ±2.66%  n = 47 µ = 19ms
ok 10 1024x1024 . 1024x1024
# 23.27 ops/sec  ±12.70%  n = 34 µ = 43ms
ok 11 2048x2048 . 2048x2048
# 4.89 ops/sec  ±1.82%  n = 17 µ = 204ms

1..11
# tests 11
# pass  11

# ok

more information about benchmarks (including test configuration) can be found on the wiki.