javascript - Webworker Canvas 性能很糟糕

Question

我正在尝试使用网络 worker 来渲染动画曼德尔布罗缩放器的部分帧，因为涉及大量计算，并且由于这可以很容易地分成 block ，这应该是并行处理的理想情况.

但无论我如何尝试，我都没有获得任何性能来换取工作人员使用的额外 cpu。与非工作版本相比，在 Chrome 中我的基准测试有点慢，在 Firefox 中则慢得多。

我的猜测是，将图像数据传输给网络 worker 非常昂贵，我尝试只接收原始数据并使用它来渲染帧，但结果大致相同。我不认为这是向工作人员发送和接收图像数据的理想方式(事实上我只需要接收它，但我无法在工作人员内部创建可直接用于 Canvas 的缓冲区) 。因此，发送任何大量数据都会造成真正的瓶颈。

亲爱的 stackoverflow，请帮我回答这两个问题:我在这里做错了什么，还有哪些可以改进的地方？

可以找到演示 here对于 worker ，以及引用 jsfiddle 上的非 worker 版本.

代码如下:

"use strict";

/*global $*/

$(function() {

    var mandelbrot = new Mandelbrot();

});

var Mandelbrot = function() {

    // set some values
    this.width = 500;
    this.height = 500;

    this.x_center = -1.407566731001088;
    this.y_center = 2.741525895538953e-10;

    this.iterations = 250;
    this.escape = 4,
    this.zoom = 10;
    this.count = 0;
    this.worker_size = 10;
    this.received = 0;
    this.refresh = true;

    //let's go - create canvas, image data and workers
    this.init();
    //start animation loop
    this.animate();

};

Mandelbrot.prototype = {

    init: function() {

        var self = this;

        //create main canvas and append it to div
        var container = $("#content");

        this.canvas = document.createElement("canvas");
        this.canvas.width = this.width;
        this.canvas.height = this.height;

        container.append(this.canvas);

        //create imagedata
        this.context = this.canvas.getContext("2d");
        this.image = this.context.getImageData(0, 0, this.width, this.height);
        this.data = new Int32Array(this.image.data.buffer);

        //create imagedata for webworkers
        this.worker_data = this.context.getImageData(0, 0, this.width, this.height / this.worker_size);

        //create webworkers drop them in array
        this.pool = [];

        for (var i = 0; i < this.worker_size; i++) {

            this.pool[i] = new Worker("js/worker.js");
            this.pool[i].idle = true;
            this.pool[i].id = i;

            //on webworker finished 
            this.pool[i].onmessage = function(e) {

                self.context.putImageData(e.data, 0, self.height / self.worker_size * e.target.id);
                self.received++;

            };

        }
    },

    iterate: function() {

        for (var i = 0; i < this.pool.length; i++) {

            this.pool[i].postMessage({

                image: this.worker_data,
                id: this.pool[i].id,
                worker_size: this.worker_size,
                width: this.width,
                height: this.height,
                x_center: this.x_center,
                y_center: this.y_center,
                iterations: this.iterations,
                escape: this.escape,
                zoom: this.zoom

            });
        }
    },

    animate: function() {

        requestAnimationFrame(this.animate.bind(this));

        //poor man's benchmark over 250 frames
        if (this.count === 0) {
            console.time("timer");
        } 

        if (this.count === 250) {
            console.timeEnd("timer");
        }

        //refresh at init, then refresh when all webworkers are done and reset
        if (this.received === this.worker_size | this.refresh) {

            this.received = 0;
            this.refresh = false;
            this.count++;
            this.zoom *= 0.95;
            this.iterate();

        }
    }
};

和worker.js:

self.onmessage = function(e) {

    "use strict";

    var x_step = e.data.zoom / e.data.width;
    var y_step = e.data.zoom / e.data.height;

    var y_start = e.data.height / e.data.worker_size * e.data.id;
    var y_end = e.data.height / e.data.worker_size;

    var data = new Int32Array(e.data.image.data.buffer);

    for (var y = 0; y < y_end; y++) {

        var iy = e.data.y_center - e.data.zoom / 2 + (y + y_start) * y_step;

        for (var x = 0; x < e.data.width; x++) {

            var rx = e.data.x_center - e.data.zoom / 2 + x * x_step;

            var zx = rx;
            var zy = iy;
            var zx2 = 0;
            var zy2 = 0;

            for (var i = 0; zx2 + zy2 < e.data.escape && i < e.data.iterations; ++i) {

                zx2 = zx * zx;
                zy2 = zy * zy;
                zy = (zx + zx) * zy + iy;
                zx = zx2 - zy2 + rx;
            }

            data[y * e.data.width + x] = (255 << 24) | (i << 16) | (i << 8) | i;

        }
    }

    self.postMessage(e.data.image);

};

Answer 1

问题在于您正在迭代父图片中的每个像素。如果将迭代限制为两个图像中较小的一个，事情会快得多。此外，如果平铺绘图，则每个平铺都可以在单独的 Web Worker 中处理，从而增加图像每个部分的托盘化。我写了这个:http://robertleeplummerjr.github.io/CanvasWorker/这正是你想要的。