Martin Splitt

Posted on Nov 29, 2019 • Updated on Mar 19, 2021

JavaScript in parallel - web workers explained

#javascript #web #webworkers

TL;DR

JavaScript is single-threaded and long-running scripts make the page unresponsive
Web Workers allow running JavaScript in separate threads, communicating with the main thread using messages.
Messages that transfer large amount of data in TypedArrays or ArrayBuffers cause large memory cost due to data being cloned
Using transfers mitigates the memory cost of cloning, but makes the data inaccessible to the sender
All code can be found in this repository
Depending on the type of work that our JavaScript carries out, navigator.hardwareConcurrency might help us spread the work across processors.

An example application

As an example we want to build a web application that constructs a table where each entry denotes if the number that belongs to it is prime or not.

We will use an ArrayBuffer to hold our booleans for us and we will be bold and make it 10 megabyte large.

Now this just serves to have our script do some heavy lifting - it isn't a very useful thing, but I may use techniques described here in future posts dealing with binary data of different sorts (images, audio, video for instance).

Here we will use a very naive algorithm (there are much better ones available):

function isPrime(candidate) {
  for(var n=2; n <= Math.floor(Math.sqrt(candidate)); n++) {
    // if the candidate can be divided by n without remainder it is not prime
    if(candidate % n === 0) return false
  }
  // candidate is not divisible by any potential prime factor so it is prime
  return true
}

Here is the rest of our application:

index.html

<!doctype html>
<html>
<head>
  <style>
    /* make the page scrollable */
    body {
      height: 300%;
      height: 300vh;
    }
  </style>
<body>
  <button>Run test</button>
  <script src="app.js"></script>
</body>
</html>

We make the page scrollable to see the effect of our JavaScript code in a moment.

app.js

document.querySelector('button').addEventListener('click', runTest)

function runTest() {
  var buffer = new ArrayBuffer(1024 * 1024 * 10) // reserves 10 MB
  var view = new Uint8Array(buffer) // view the buffer as bytes
  var numPrimes = 0

  performance.mark('testStart')
  for(var i=0; i<view.length;i++) {
    var primeCandidate = i+2 // 2 is the smalles prime number
    var result = isPrime(primeCandidate)
    if(result) numPrimes++
    view[i] = result
  }
  performance.mark('testEnd')
  performance.measure('runTest', 'testStart', 'testEnd')
  var timeTaken = performance.getEntriesByName('runTest')[0].duration

  alert(`Done. Found ${numPrimes} primes in ${timeTaken} ms`)
  console.log(numPrimes, view)
}

function isPrime(candidate) {
  for(var n=2; n <= Math.floor(Math.sqrt(candidate)); n++) {
    if(candidate % n === 0) return false
  }
  return true
}

We are using the User Timing API to measure time and add our own information into the timeline.

Now I let the test run on my trusty "old" Nexus 7 (2013):

Okay, that's not very impressive, is it?
Making matters worse is that the website stops reacting to anything during these 39 seconds - no scrolling, no clicking, no typing. The page is frozen.

This happens because JavaScript is single-threaded and in a single thread only one thing can happen at the same time. Making matters worse, pretty much anything that is concerned with interactions for our page (so browser code for scrolling, entering text etc.) runs on the same thread.

So is it that we just cannot do any heavy lifting?

Web Workers to the rescue

No. This is just the kind of work we can use Web Workers.

A Web Worker is a JavaScript file from the same origin as our web application that will be running in a separate thread.

Running in a separate thread means:

it will run in parallel
it will not make the page unresponsive by blocking the main thread
it will not have access to the DOM or any variable or function in the main thread
it can use the network and communicate with the main thread using messages

So how do we keep our page responsive while the prime-searching work goes on? Here is the procedure:

We start a worker and send the ArrayBuffer to it
The worker does its job
When the worker is done, it sends the ArrayBuffer and the number of primes it found back to the main thread

Here is the updated code:

app.js

document.querySelector('button').addEventListener('click', runTest)

function runTest() {
  var buffer = new ArrayBuffer(1024 * 1024 * 10) // reserves 10 MB
  var view = new Uint8Array(buffer) // view the buffer as bytes

  performance.mark('testStart')
  var worker = new Worker('prime-worker.js')
  worker.onmessage = function(msg) {
    performance.mark('testEnd')
    performance.measure('runTest', 'testStart', 'testEnd')
    var timeTaken = performance.getEntriesByName('runTest')[0].duration
    view.set(new Uint8Array(buffer), 0)
    alert(`Done. Found ${msg.data.numPrimes} primes in ${timeTaken} ms`)
    console.log(msg.data.numPrimes, view)
  }
  worker.postMessage(buffer)
}

prime-worker.js

self.onmessage = function(msg) {
  var view = new Uint8Array(msg.data),
      numPrimes = 0
  for(var i=0; i<view.length;i++) {
    var primeCandidate = i+2 // 2 is the smalles prime number
    var result = isPrime(primeCandidate)
    if(result) numPrimes++
    view[i] = result
  }
  self.postMessage({
    buffer: view.buffer,
    numPrimes: numPrimes
  })
}

function isPrime(candidate) {
  for(var n=2; n <= Math.floor(Math.sqrt(candidate)); n++) {
    if(candidate % n === 0) return false
  }
  return true
}

And here is what we get when run again on my Nexus 7:

Well, uhm, did all that ceremony give us anything then? After all now it is even slower!

The big win here wasn't making it faster, but try scrolling the page or otherwise interacting... it stays responsive at all times! With the calculation being ferried off to its own thread, we don't get in the way of the main thread taking care of responding to the user.

But before we move on to make things faster, we shall figure out an important detail on how postMessage works.

The cost of cloning

As mentioned earlier the main thread and worker thread are separated so we need to shuttle data between them using messages

But how does that actually move data between them? The answer for the way we have done it before is structured cloning.

This means we are copying our 10 megabyte ArrayBuffer to the worker and then copy the ArrayBuffer from the worker back.

I assumed this would total in 30 MB memory usage: 10 in our original ArrayBuffer, 10 in the copy sent to the worker and another 10 in the copy that is sent back.

Here is the memory usage before starting the test:

And here right after the test:

Wait, that is 50 megabytes more. As it turns out:

we start with 10mb for the ArrayBuffer
the cloning itself* creates another +10mb
the clone is copied to the worker, +10mb
the worker clones its copy again, +10mb
the cloned copy is copied to the main thread, +10mb

*) I am not exactly sure why the clone is not moved to the target thread instead of being copied, but the serialialisation itself seems to be incurring the unexpected memory cost

Transferables save the day

Luckily for us there is a different way of transferring data between the threads in the optional second parameter of postMessage, called the transfer list.

This second parameter can hold a list of Transferable objects that will be excluded from cloning and will be moved or transferred instead.

Transferring an object, however, neutralises it in the source thread, so for instance our ArrayBuffer won't contain any data in the main thread after it's been transferred to the worker and its byteLength will be zero.
This is to avoid the cost of having to implement mechanisms to deal with a bunch of issues that can happen when multiple threads access shared data.

Here is the adjusted code using transfers:

app.js

worker.postMessage(buffer, [buffer])

prime-worker.js

  self.postMessage({
    buffer: view.buffer,
    numPrimes: numPrimes
  }, [view.buffer])

And here are our numbers:

So we got a little faster than the cloning worker, close to the original main-thread-blocking version. How are we doing in terms of memory?

So having started with 40mb and ending up with a little more than 50mb sounds about right.

More workers = more speed?

So up until now we have

unblocked the main thread
removed the memory overhead from cloning

Can we speed it up as well?

We could split the range of numbers (and our buffer) among multiple workers, run them in parallel and merge the results:

app.js

Instead of launching a single worker, we are about to launch four. Each worker will receive a message instructing it with the offset to begin with and how many numbers to check.

When a worker finishes, it reports back with

an ArrayBuffer containing the information about which entries are prime
the amount of primes it found
its original offset
its original length

We then copy the data from the buffer into the target buffer, sum up the total number of primes found.

Once all workers completed, we display the final results.

document.querySelector('button').addEventListener('click', runTest)

function runTest() {
  const TOTAL_NUMBERS = 1024 * 1024 * 10
  const NUM_WORKERS = 4
  var numbersToCheck = TOTAL_NUMBERS, primesFound = 0
  var buffer = new ArrayBuffer(numbersToCheck) // reserves 10 MB
  var view = new Uint8Array(buffer) // view the buffer as bytes

  performance.mark('testStart')
  var offset = 0
  while(numbersToCheck) {
    var blockLen = Math.min(numbersToCheck, TOTAL_NUMBERS / NUM_WORKERS)
    var worker = new Worker('prime-worker.js')
    worker.onmessage = function(msg) {
      view.set(new Uint8Array(msg.data.buffer), msg.data.offset)
      primesFound += msg.data.numPrimes

      if(msg.data.offset + msg.data.length === buffer.byteLength) {
        performance.mark('testEnd')
        performance.measure('runTest', 'testStart', 'testEnd')
        var timeTaken = performance.getEntriesByName('runTest')[0].duration
        alert(`Done. Found ${primesFound} primes in ${timeTaken} ms`)
        console.log(primesFound, view)
      }
    }

    worker.postMessage({
      offset: offset,
      length: blockLen
    })

    numbersToCheck -= blockLen
    offset += blockLen
  }
}

prime-worker.js

The worker creates a Uint8Array view large enough to hold the length bytes as ordered by the main thread.

The prime checks start at the desired offset and finally data is transferred back.

self.onmessage = function(msg) {
  var view = new Uint8Array(msg.data.length),
      numPrimes = 0
  for(var i=0; i<msg.data.length;i++) {
    var primeCandidate = i+msg.data.offset+2 // 2 is the smalles prime number
    var result = isPrime(primeCandidate)
    if(result) numPrimes++
    view[i] = result
  }
  self.postMessage({
    buffer: view.buffer,
    numPrimes: numPrimes,
    offset: msg.data.offset,
    length: msg.data.length
  }, [view.buffer])
}

function isPrime(candidate) {
  for(var n=2; n <= Math.floor(Math.sqrt(candidate)); n++) {
    if(candidate % n === 0) return false
  }
  return true
}

And here are the result:

So this solution took approximately half the time with quite some memory cost (40mb base memory usage + 10mb for the target buffer + 4 x 2.5mb for the buffer in each worker + 2mb overhead per worker.

Here is the timeline of the application using 4 workers:

We can see that the workers run in parallel, but we aren't getting a 4x speed-up as some workers take longer than others. This is the result of the way we divided the number range: As each worker needs to divide each number x by all numbers from 2 to √x, the workers with larger numbers need to do more divisions, i.e. more work. This can surely minimised by dividing the numbers in a way that ends up spreading the operations more equally among them. I will leave this as an exercise to you, the keen reader ;-)

Another question is: Could we just throw more workers at it?

Here is the result for 8 workers:

Well, this got slower! The timeline shows us why this happened:

We see that, aside from minor overlaps, no more than 4 workers are active at the same time.

This will depend on the system and worker characteristics and isn't a hard-and-fast number.

A system can only do so much at the same time and work is usually either I/O-bound (i.e. limited by network or file throughput) or CPU-bound (i.e. limited by running calculations on the CPU).

In our case each worker hogs the CPU for our calculations. As my Nexus 7 has four cores, it can deal with four of our fully CPU-bound workers simultaneously.

Usually you will end up with a mix of CPU- and I/O bound workloads or problems that aren't easy to chunk in smaller workloads, so the number of workers is sometimes a little hard to judge. If you are looking to find out how many logical CPUs are available, you can use navigator.hardwareConcurrency though.