Easy Parallel Computing with Javascript
Parallel.js is a library for to make parallel computing in Javascript simple. It works in Node.js and in the Web Browser.
Parallel takes advantage of Web Workers for the web, and child processes for Node.
npm install paralleljsor
<script src='https://unpkg.com/paralleljs@1.0/lib/parallel.js'></script>
This is the constructor. Use it to new up any parallel jobs. The constructor takes an array of data you want to
operate on. This data will be held in memory until you finish your job, and can be accessed via the .data attribute
of your job.
The object returned by the Parallel constructor is meant to be chained, so you can produce a chain of
operations on the provided data.
Arguments
data: This is the data you wish to operate on. Will often be an array, but the only restrictions are that your values are serializable as JSON.options(optional): Some options for your jobevalPath(optional): This is the path to the file eval.js. This is required when running in node, and required for some browsers (IE 10) in order to work around cross-domain restrictions for web workers. Defaults to the same location as parallel.js in node environments, andnullin the browser.maxWorkers(optional): The maximum number of permitted worker threads. This will default to 4, or the number of cpus on your computer if you're running nodesynchronous(optional): If webworkers are not available, whether or not to fall back to synchronous processing usingsetTimeout. Defaults totrue.
Example
const p = new Parallel([1, 2, 3, 4, 5]);
console.log(p.data); // prints [1, 2, 3, 4, 5]This function will spawn a new process on a worker thread. Pass it the function you want to call. Your function will receive one argument, which is the current data. The value returned from your spawned function will update the current data.
Arguments
fn: A function to execute on a worker thread. Receives the wrapped data as an argument. The value returned will be assigned to the wrapped data.opts: An optional object to pass to spawn.- 'opts.timeout': milliseconds to way for function to return value. If the worker does not finish in this time, it will be killed.
Example
const p = new Parallel('forwards');
// Spawn a remote job (we'll see more on how to use then later)
p.spawn(data => {
data = data.reverse();
console.log(data); // logs sdrawrof
return data;
})
.then(data => {
console.log(data) // logs sdrawrof
});Map will apply the supplied function to every element in the wrapped data. Parallel will spawn one worker for each array element in the data, or the supplied maxWorkers argument. The values returned will be stored for further processing.
Arguments
fn: A function to apply. Receives the wrapped data as an argument. The value returned will be assigned to the wrapped data.
Example
const p = new Parallel([0, 1, 2, 3, 4, 5, 6]);
const log = function () { console.log(arguments); };
// One gotcha: anonymous functions cannot be serialzed
// If you want to do recursion, make sure the function
// is named appropriately
function fib(n) {
return n < 2 ? 1 : fib(n - 1) + fib(n - 2);
};
p.map(fib).then(log)
// Logs the first 7 Fibonnaci numbers, woot!Reduce applies an operation to every member of the wrapped data, and returns a scalar value produced by the operation.
Use it for combining the results of a map operation, by summing numbers for example. This takes a reducing function,
which gets an argument, data, an array of the stored value, and the current element.
Arguments
fn: A function to apply. Receives the stored value and current element as argument. The value returned will be stored as the current value for the next iteration. Finally, the current value will be assigned to current data.
Example
const p = new Parallel([0, 1, 2, 3, 4, 5, 6, 7, 8]);
function add(d) { return d[0] + d[1]; }
function factorial(n) { return n < 2 ? 1 : n * factorial(n - 1); }
function log() { console.log(arguments); }
p.require(factorial)
// Approximate e^10
p.map(n => Math.pow(10, n)).reduce(add).then(log);The functions given to then are called after the last requested operation has finished.
success receives the resulting data object, while fail will receive an error object.
Arguments
success: A function that gets called upon successful completion. Receives the wrapped data as an argument.failure(optional): A function that gets called if the job fails. The function is passed an error object.
Example
const p = new Parallel([1, 2, 3]);
function dbl(n) { return n * 2; }
p
.map(dbl)
.map(dbl)
.map(dbl)
.then(data => {
console.log(data); // logs [8, 16, 24]
});
// Approximate e^10
p
.map(n => Math.pow(10, n) / factorial(n))
.reduce(add)
.then(log);If you have state that you want to share between your main thread and the worker threads, this is how. Require
takes either a string or a function. A string should point to a file name. Note that in order to
use require with a file name as an argument, you have to provide the evalPath property in the options
object.
Example
let p = new Parallel([1, 2, 3], { evalPath: 'https://raw.github.com/parallel-js/parallel.js/master/lib/eval.js' });
const cubeRoot = n => Math.pow(n, 1 / 3);
// Require a function
p.require(cubeRoot);
// Require a file
p.require('blargh.js');
p.map(d => blargh(20 * cubeRoot(d)));You can pass data to threads that will be global to that worker. This data will be global in each called function.
The data will be available under the global.env namespace. The namespace can be configured by passing the
envNamespace option to the Parallel constructor. The data you wish to pass should be provided as the env option
to the parallel constructor.
Important: Globals can not be mutated between threads.
Example
let p = new Parallel([1, 2, 3], {
env: {
a: 10
}
});
// returns 10, 20, 30
p.map(d => d * global.env.a);
// Configure the namespace
p = new Parallel([1, 2, 3], {
env: {
a: 10
},
envNamespace: 'parallel'
});
p.map(d => d * global.parallel.a);Parallel.js is made up of four contributors: