Concurrency in iOS is a massive topic. So in this article I want to zoom in on a sub-topic concerning queues and the Grand Central Dispatch (GCD) framework.

In particular, I wish to explore the differences between serial and concurrent queues, as well as the differences between synchronous and asynchronous execution.

If you've never used GCD before, this article is a great place to start. If you have some experience with GCD, but are still curious about the topics mentioned above, I think you will still find it useful. And I hope you will pick up one or two new things along the way.

I created a SwiftUI companion app to visually demonstrate the concepts in this article. The app also has a fun short quiz that I encourage you to try before and after reading this article. Download the source code here, or get the public beta here.

I will begin with an introduction to GCD, followed by a detailed explanation on sync, async, serial and concurrent. Afterwards, I will cover some pitfalls when working with concurrency. Finally, I will end with a summary and some general advice.

Introduction

Let’s start with a brief intro to GCD and dispatch queues. Feel free to skip to the Sync vs Async section if you are already familiar with the topic.

Concurrency and Grand Central Dispatch

Concurrency lets you take advantage of the fact that your device has multiple CPU cores. To make use of these cores, you will need to use multiple threads. However, threads are a low-level tool, and managing threads manually in an efficient manner is extremely difficult.

Grand Central Dispatch was created by Apple over 10 years ago as an abstraction to help developers write multi-threaded code without manually creating and managing the threads themselves.

With GCD, Apple took an asynchronous design approach to the problem. Instead of creating threads directly, you use GCD to schedule work tasks, and the system will perform these tasks for you by making the best use of its resources. GCD will handle creating the requisite threads and will schedule your tasks on those threads, shifting the burden of thread management from the developer to the system.

A big advantage of GCD is that you don’t have to worry about hardware resources as you write your concurrent code. GCD manages a thread pool for you, and it will scale from a single-core Apple Watch all the way up to a many-core MacBook Pro.

Dispatch Queues

These are the main building blocks of GCD which let you execute arbitrary blocks of code using a set of parameters that you define. The tasks in dispatch queues are always started in a first-in, first-out (FIFO) fashion. Note that I said started, because the completion time of your tasks depends on several factors, and is not guaranteed to be FIFO (more on that later.)

Broadly speaking, there are three kinds of queues available to you:

  • The Main dispatch queue (serial, pre-defined)
  • Global queues (concurrent, pre-defined)
  • Private queues (can be serial or concurrent, you create them)

Every app comes with a Main queue, which is a serial queue that executes tasks on the main thread. This queue is responsible for drawing your application’s UI and responding to user interactions (touch, scroll, pan, etc.) If you block this queue for too long, your iOS app will appear to freeze, and your macOS app will display the infamous beach ball/spinning wheel.

When performing a long-running task (network call, computationally intensive work, etc), we avoid freezing the UI by performing this work on a background queue. Then we update the UI with the results on the main queue:

URLSession.shared.dataTask(with: url) { data, response, error in
    if let data = data {
        DispatchQueue.main.async { // UI work
            self.label.text = String(data: data, encoding: .utf8)
        }
    }
}
URLSession delivers callbacks on a background queue

As a rule of thumb, all UI work must be executed on the Main queue. You can turn on the Main Thread Checker option in Xcode to receive warnings whenever UI work gets executed on a background thread.

the main thread checker can be found in the scheme editor

In addition to the main queue, every app comes with several pre-defined concurrent queues that have varying levels of Quality of Service (an abstract notion of priority in GCD.)

For example, here’s the code to submit work asynchronously to the user interactive (highest priority) QoS queue:

DispatchQueue.global(qos: .userInteractive).async {
    print("We're on a global concurrent queue!") 
}

Alternatively, you can call the default priority global queue by not specifying a QoS like this:

DispatchQueue.global().async {
    print("Generic global queue")
}
default QoS falls somewhere between user initiated and utility

Additionally, you can create your own private queues using the following syntax:

let serial = DispatchQueue(label: "com.besher.serial-queue")
serial.async {
    print("Private serial queue")
}
private queues are serial by default

When creating private queues, it helps to use a descriptive label (such as reverse DNS notation), as this will aid you while debugging in Xcode’s navigator, lldb, and Instruments:

By default, private queues are serial (I’ll explain what this means shortly, promise!) If you want to create a private concurrent queue, you can do so via the optional attributes parameter:

let concurrent = DispatchQueue(label: "com.besher.serial-queue", attributes: .concurrent)
concurrent.sync {
    print("Private concurrent queue")
}

There is an optional QoS parameter as well. The private queues that you create will ultimately land in one of the global concurrent queues based on their given parameters.

What’s in a task?

I mentioned dispatching tasks to queues. Tasks can refer to any block of code that you submit to a queue using the sync or async functions. They can be submitted in the form of an anonymous closure:

DispatchQueue.global().async {
    print("Anonymous closure")
}

Or inside a dispatch work item that gets performed later:

let item = DispatchWorkItem(qos: .utility) {
    print("Work item to be executed later")
}
notice how we defined a task QoS here

Regardless of whether you dispatch synchronously or asynchronously, and whether you choose a serial or concurrent queue, all of the code inside a single task will execute line by line. Concurrency is only relevant when evaluating multiple tasks.

For example, if you have 3 loops inside the same task, these loops will always execute in order:

DispatchQueue.global().async {
    for i in 0..<10 {
        print(i)
    }

    for _ in 0..<10 {
        print("🔵")
    }

    for _ in 0..<10 {
        print("💔")
    }
}

This code always prints out ten digits from 0 to 9, followed by ten blue circles, followed by ten broken hearts, regardless of how you dispatch that closure.

Individual tasks can also have their own QoS level as well (by default they use their queue’s priority.) This distinction between queue QoS and task QoS leads to some interesting behaviour that we will discuss in the priority inversion section.

By now you might be wondering what serial and concurrent are all about. You might also be wondering about the differences between sync and async when submitting your tasks. This brings us to the crux of this article, so let’s dive in!

Sync vs Async

When you dispatch a task to a queue, you can choose to do so synchronously or asynchronously using the sync and async dispatch functions. Sync and async primarily affect the source of the submitted task, that is the queue where it is being submitted from.

When your code reaches a sync statement, it will block the current queue until that task completes. Once the task returns/completes, control is returned to the caller, and the code that follows the sync task will continue.

Think of sync as synonymous with ‘blocking’.

An async statement, on the other hand, will execute asynchronously with respect to the current queue, and immediately returns control back to the caller without waiting for the contents of the async closure to execute. There is no guarantee as to when exactly the code inside that async closure will execute.

Current queue?

It may not be obvious what the source, or current, queue is, because it’s not always explicitly defined in the code.

For example, if you call your sync statement inside viewDidLoad, your current queue will be the Main dispatch queue. If you call the same function inside a URLSession completion handler, your current queue will be a background queue.

Going back to sync vs async, let’s take this example:

DispatchQueue.global().sync {
    print("Inside")
}
print("Outside")
// Console output:
// Inside
// Outside

The above code will block the current queue, enter the closure and execute its code on the global queue by printing “Inside”, before proceeding to print “Outside”. This order is guaranteed.

Let’s see what happens if we try async instead:

DispatchQueue.global().async {
    print("Inside")
}
print("Outside")
// Potential console output (based on QoS): 
// Outside
// Inside

Our code now submits the closure to the global queue, then immediately proceeds to run the next line. It will likely print “Outside” before “Inside”, but this order isn’t guaranteed. It depends on the QoS of the source and destination queues, as well as other factors that the system controls.

Threads are an implementation detail in GCD — we do not have direct control over them and can only deal with them using queue abstractions. Nevertheless, I think it can be useful to ‘peek under the covers’ at thread behaviour to understand some challenges we might encounter with GCD.

For instance, when you submit a task using sync, GCD optimizes performance by executing that task on the current thread (the caller.)

There is one exception however, which is when you submit a sync task to the main queue —  doing so will always run the task on the main thread and not the caller. This behaviour can have some ramifications that we will explore in the priority inversion section.

From Dispatcher on Github

Which one to use?

When submitting work to a queue, Apple recommends using asynchronous execution over synchronous execution. However, there are situations where sync might be the better choice, such as when dealing with race conditions, or when performing a very small task. I will cover these situations shortly.

One large consequence of performing work asynchronously inside a function is that the function can no longer directly return its values (if they depend on the async work that’s being done). It must instead use a closure/completion handler parameter to deliver the results.

To demonstrate this concept, let’s take a small function that accepts image data, performs some expensive computation to process the image, then returns the result:

func processImage(data: Data) -> UIImage? {
    guard let image = UIImage(data: data) else { return nil }
    // calling an expensive function
    let processedImage = upscaleAndFilter(image: image)
    return processedImage 
}

In this example, the function upscaleAndFilter(image:) might take several seconds, so we want to offload it into a separate queue to avoid freezing the UI. Let’s create a dedicated queue for image processing, and then dispatch the expensive function asynchronously:

let imageProcessingQueue = DispatchQueue(label: "com.besher.image-processing")

func processImageAsync(data: Data) -> UIImage? {
    guard let image = UIImage(data: data) else { return nil }
    
    imageProcessingQueue.async {
        let processedImage = upscaleAndFilter(image: image)
        return processedImage
    }
}
this code doesn't compile!

There are two issues with this code. First, the return statement is inside the async closure, so it is no longer returning a value to the processImageAsync(data:) function, and currently serves no purpose.

But the bigger issue is that our processImageAsync(data:) function is no longer returning any value, because the function reaches the end of its body before it enters the async closure.

To fix this error, we will adjust the function so that it no longer directly returns a value. Instead, it will have a new completion handler parameter that we can call once our asynchronous function has completed its work:

let imageProcessingQueue = DispatchQueue(label: "com.besher.image-processing")

func processImageAsync(data: Data, completion: @escaping (UIImage?) -> Void) {
    guard let image = UIImage(data: data) else {
        completion(nil)
        return
    }

    imageProcessingQueue.async {
        let processedImage =  self.upscaleAndFilter(image: image)
        completion(processedImage)
    }
}

As evident in this example, the change to make the function asynchronous has propagated to its caller, who now has to pass in a closure and handle the results asynchronously as well. By introducing an asynchronous task, you can potentially end up modifying a chain of several functions.

Concurrency and asynchronous execution add complexity to your project as we just observed. This indirection also makes debugging more difficult. That’s why it really pays off to think about concurrency early in your design — it’s not something you want to tack on at the end of your design cycle.

Synchronous execution, by contrast, does not increase complexity. Rather, it allows you to continue using return statements as you did before. A function containing a sync task will not return until the code inside that task has completed. Therefore it does not require a completion handler.

If you are submitting a tiny task (for example, updating a value), consider doing it synchronously. Not only does that help you keep your code simple, it will also perform better — Async is believed to incur an overhead that outweighs the benefit of doing the work asynchronously for tiny tasks that take under 1ms to complete.

If you are submitting a large task, however, like the image processing we performed above, then consider doing it asynchronously to avoid blocking the caller for too long.

Dispatching on the same queue

While it is safe to dispatch a task asynchronously from a queue into itself (for example, you can use .asyncAfter on the current queue), you can not dispatch a task synchronously from a queue into the same queue. Doing so will result in a deadlock that immediately crashes the app!

This issue can manifest itself when performing a chain of synchronous calls that lead back to the original queue. That is, you sync a task onto another queue, and when the task completes, it syncs the results back into the original queue, leading to a deadlock. Use async to avoid such crashes.

Blocking the main queue

Dispatching tasks synchronously from the main queue will block that queue, thereby freezing the UI, until the task is completed. Thus it’s better to avoid dispatching work synchronously from the main queue unless you’re performing really light work.

prefer to use async from the main queue

Serial vs Concurrent

Serial and concurrent affect the destination —  the queue in which your work has been submitted to run. This is in contrast to sync and async, which affected the source.

A serial queue will not execute its work on more than one thread at a time, regardless of how many tasks you dispatch on that queue. Consequently, the tasks are guaranteed to not only start, but also terminate, in first-in, first-out order.

Moreover, when you block a serial queue (using a sync call, semaphore, or some other tool), all work on that queue will halt until the block is over.

From Dispatcher on Github

A concurrent queue can spawn multiple threads, and the system decides how many threads are created. Tasks always start in FIFO order, but the queue does not wait for tasks to finish before starting the next task, therefore tasks on concurrent queues can finish in any order.

When you perform a blocking command on a concurrent queue, it will not block the other threads on this queue. Additionally, when a concurrent queue gets blocked, it runs the risk of thread explosion. I will cover this in more detail later on.

From Dispatcher on Github

The main queue in your app is serial. All the global pre-defined queues are concurrent. Any private dispatch queue you create is serial by default, but can be set to be concurrent using an optional attribute as discussed earlier.

It’s important to note here that the concept of serial vs concurrent is only relevant when discussing a specific queue. All queues are concurrent relative to each other.

That is, if you dispatch work asynchronously from the main queue to a private serial queue, that work will be completed concurrently with respect to the main queue. And if you create two different serial queues, and then perform blocking work on one of them, the other queue is unaffected.

To demonstrate the concurrency of multiple serial queues, let’s take this example:

let serial1 = DispatchQueue(label: "com.besher.serial1")
let serial2 = DispatchQueue(label: "com.besher.serial2")

serial1.async {
    for _ in 0..<5 { print("🔵") }
}

serial2.async {
    for _ in 0..<5 { print("🔴") }
}

Both queues here are serial, but the results are jumbled up because they execute concurrently in relation to each other. The fact that they’re each serial (or concurrent) has no effect on this result. Their QoS level determines who will generally finish first (order not guaranteed).

If we want to ensure that the first loop finishes first before starting the second loop, we can submit the first task synchronously from the caller:

let serial1 = DispatchQueue(label: "com.besher.serial1")
let serial2 = DispatchQueue(label: "com.besher.serial2")

serial1.sync { // <---- we changed this to 'sync'
    for _ in 0..<5 { print("🔵") }
}
// we don't get here until first loop terminates
serial2.async {
    for _ in 0..<5 { print("🔴") }
}

This is not necessarily desirable, because we are now blocking the caller while the first loop is executing.

To avoid blocking the caller, we can submit both tasks asynchronously, but to the same serial queue:

let serial = DispatchQueue(label: "com.besher.serial")

serial.async {
    for _ in 0..<5 { print("🔵") }
}

serial.async {
    for _ in 0..<5 { print("🔴") }
}	

Now our tasks execute concurrently with respect to the caller, while also keeping their order intact.

Note that if we make our single queue concurrent via the optional parameter, we go back to the jumbled results, as expected:

let concurrent = DispatchQueue(label: "com.besher.concurrent", attributes: .concurrent)

concurrent.async {
    for _ in 0..<5 { print("🔵") }
}

concurrent.async {
    for _ in 0..<5 { print("🔴") }
}

Sometimes you might confuse synchronous execution with serial execution (at least I did), but they are very different things. For example, try changing the first dispatch on line 3 from our previous example to a sync call:

let concurrent = DispatchQueue(label: "com.besher.concurrent", attributes: .concurrent)

concurrent.sync {
    for _ in 0..<5 { print("🔵") }
}

concurrent.async {
    for _ in 0..<5 { print("🔴") }
}
this can be misleading

Suddenly, our results are back in perfect order. But this is a concurrent queue, so how could that happen? Did the sync statement somehow turn it into a serial queue?

The answer is no!

This is a bit sneaky. What happened is that we did not reach the async call until the first task had completed its execution. The queue is still very much concurrent, but inside this zoomed-in section of the code. it appears as if it were serial. This is because we are blocking the caller, and not proceeding to the next task, until the first one is finished.

If another queue somewhere else in your app tried submitting work to this same queue while it was still executing the sync statement, that work will run concurrently with whatever we got running here, because it’s still a concurrent queue.

Which one to use?

Serial queues take advantage of CPU optimizations and caching, and help reduce context switching.

Apple recommends starting with one serial queue per subsystem in your app —  for example one for networking, one for file compression, etc. If the need arises, you can later expand to a hierarchy of queues per subsystem using the setTarget method or the optional target parameter when building queues.

If you run into a performance bottleneck, measure your app’s performance then see if a concurrent queue helps. If you do not see a measurable benefit, it’s better to stick to serial queues.

Pitfalls

Priority Inversion and Quality of Service

Priority inversion is when a high priority task is prevented from running by a lower priority task, effectively inverting their relative priorities.

This situation often occurs when a high QoS queue shares a resources with a low QoS queue, and the low QoS queue gets a lock on that resource.

But I wish to cover a different scenario that is more relevant to our discussion —  it’s when you submit tasks to a low QoS serial queue, then submit a high QoS task to that same queue. This scenario also results in priority inversion, because the high QoS task has to wait on the lower QoS tasks to finish.

GCD resolves priority inversion by temporarily raising the QoS of the queue that contains the low priority tasks that are ‘ahead’ of, or blocking, your high priority task.

It’s kind of like having cars stuck in front of an ambulance. Suddenly they’re allowed to cross the red light just so that the ambulance can move (in reality the cars move to the side, but imagine a narrow (serial) street or something, you get the point :-P)

To illustrate the inversion problem, let’s start with this code:


enum Color: String {
    case blue = "🔵"
    case white = "⚪️"
}

func output(color: Color, times: Int) {
    for _ in 1...times {
        print(color.rawValue)
    }
}

let starterQueue = DispatchQueue(label: "com.besher.starter", qos: .userInteractive)
let utilityQueue = DispatchQueue(label: "com.besher.utility", qos: .utility)
let backgroundQueue = DispatchQueue(label: "com.besher.background", qos: .background)
let count = 10

starterQueue.async {

    backgroundQueue.async {
        output(color: .white, times: count)
    }

    backgroundQueue.async {
        output(color: .white, times: count)
    }

    utilityQueue.async {
        output(color: .blue, times: count)
    }

    utilityQueue.async {
        output(color: .blue, times: count)
    }

    // next statement goes here
}

We create a starter queue (where we submit the tasks from), as well as two queues with different QoS. Then we dispatch tasks to each of these two queues, each task printing out an equal number of circles of a specific colour (utility queue is blue, background is white.)

Because these tasks are submitted asynchronously, every time you run the app, you’re going to see slightly different results. However, as you would expect, the queue with the lower QoS (background) almost always finishes last. In fact, the last 10–15 circles are usually all white.

no surprises here

But watch what happens when we submit a sync task to the background queue after the last async statement. You don’t even need to print anything inside the sync statement, just adding this line is enough:

// add this after the last async statement, 
// still inside starterQueue.async
backgroundQueue.sync {}
priority inversion

The results in the console have flipped! Now, the higher priority queue (utility) always finishes last, and the last 10–15 circles are blue.

To understand why that happens, we need to revisit the fact that synchronous work is executed on the caller thread (unless you’re submitting to the main queue.)

In our example above, the caller (starterQueue) has the top QoS (userInteractive.) Therefore, that seemingly innocuous sync task is not only blocking the starter queue, but it’s also running on the starter’s high QoS thread. The task therefore runs with high QoS, but there are two other tasks ahead of it on the same background queue that have background QoS. Priority inversion detected!

As expected, GCD resolves this inversion by raising the QoS of the entire queue to temporarily match the high QoS task. Consequently, all the tasks on the background queue end up running at user interactive QoS, which is higher than the utility QoS. And that’s why the utility tasks finish last!

Side-note: If you remove the starter queue from that example and submit from the main queue instead, you will get similar results, as the main queue also has user interactive QoS.

To avoid priority inversion in this example, we need to avoid blocking the starter queue with the sync statement. Using async would solve that problem.

Although it’s not always ideal, you can minimize priority inversions by sticking to the default QoS when creating private queues or dispatching to the global concurrent queue.

Thread explosion

When you use a concurrent queue, you run the risk of thread explosion if you’re not careful. This can happen when you try to submit tasks to a concurrent queue that is currently blocked (for example with a semaphore, sync, or some other way.) Your tasks will run, but the system will likely end up spinning up new threads to accommodate these new tasks, and threads aren’t cheap.

This is likely why Apple suggests starting with a serial queue per subsystem in your app, as each serial queue can only use one thread. Remember that serial queues are concurrent in relation to other queues, so you still get a performance benefit when you offload your work to a queue, even if it isn’t concurrent.

Race conditions

Swift Arrays, Dictionaries, Structs, and other value types are not thread-safe by default. For example, when you have multiple threads trying to access and modify the same array, you will start running into trouble.

There are different solutions to the readers-writers problem, such as using locks or semaphores. But the relevant solution I wish to discuss here is the use of an isolation queue.

Let’s say we have an array of integers, and we want to submit asynchronous work that references this array. As long as our work only reads the array and does not modify it, we are safe. But as soon as we try to modify the array in one of our asynchronous tasks, we will introduce instability in our app.

It’s a tricky problem because your app can run 10 times without issues, and then it crashes on the 11th time. One very handy tool for this situation is the Thread Sanitizer in Xcode. Enabling this option will help you identify potential race conditions in your app.

the thread sanitizer can be accessed in the scheme editor
this option is only available on the simulator

To demonstrate the problem, let’s take this (admittedly contrived) example:

class ViewController: UIViewController {
    
    let concurrent = DispatchQueue(label: "com.besher.concurrent", attributes: .concurrent)
    var array = [1,2,3,4,5]

    override func viewDidLoad() {
        for _ in 0...1 {
            race()
        }
    }

    func race() {

        concurrent.async {
            for i in self.array { // read access
                print(i)
            }
        }

        concurrent.async {
            for i in 0..<10 {
                self.array.append(i) // write access
            }
        }
    }
}

One of the async tasks is modifying the array by appending values. If you try running this on your simulator, you might not crash. But run it enough times (or increase the loop frequency on line 7), and you will eventually crash. If you enable the thread sanitizer, you will get a warning every time you run the app.

To deal with this race condition, we are going to add an isolation queue that uses the barrier flag. This flag allows any outstanding tasks on the queue to finish, but blocks any further tasks from executing until the barrier task is completed.

Think of the barrier like a janitor cleaning a public restroom (shared resource.) There are multiple (concurrent) stalls inside the restroom that people can use.

Upon arrival, the janitor places a cleaning sign (barrier) blocking any newcomers from entering until the cleaning is done, but the janitor does not start cleaning until all the people inside have finished their business. Once they all leave, the janitor proceeds to clean the public restroom in isolation.

When finally done, the janitor removes the sign (barrier) so that the people who are queued up outside can finally enter.

Here’s what that looks like in code:

class ViewController: UIViewController {
    let concurrent = DispatchQueue(label: "com.besher.concurrent", attributes: .concurrent)
    let isolation = DispatchQueue(label: "com.besher.isolation", attributes: .concurrent)
    private var _array = [1,2,3,4,5]
    
    var threadSafeArray: [Int] {
        get {
            return isolation.sync {
                _array
            }
        }
        set {
            isolation.async(flags: .barrier) {
                self._array = newValue
            }
        }
    }
    
    override func viewDidLoad() {
        for _ in 0...15 {
            race()
        }
    }
    
    func race() {
        concurrent.async {
            for i in self.threadSafeArray {
                print(i)
            }
        }
        
        concurrent.async {
            for i in 0..<10 {
                self.threadSafeArray.append(i)
            }
        }
    }
}

We have added a new isolation queue, and restricted access to the private array using a getter and setter that will place a barrier when modifying the array.

The getter needs to be sync in order to directly return a value. The setter can be async, as we don’t need to block the caller while the write is taking place.

We could have used a serial queue without a barrier to solve the race condition, but then we would lose the advantage of having concurrent read access to the array. Perhaps that makes sense in your case, you get to decide.

Conclusion

Thank you so much for reading this far! I hope you learned something new from this article. I will leave you with a summary and some general advice:

Summary

  • Queues always start their tasks in FIFO order
  • Queues are always concurrent relative to other queues
  • Sync vs Async concerns the source
  • Serial vs Concurrent concerns the destination
  • Sync is synonymous with ‘blocking’
  • Async immediately returns control to caller
  • Serial uses a single thread, and guarantees order of execution
  • Concurrent uses multiple-threads, and risks thread explosion
  • Think about concurrency early in your design cycle
  • Synchronous code is easier to reason about and debug
  • Avoid relying on global concurrent queues if possible
  • Consider starting with a serial queue per subsystem
  • Switch to concurrent queue only if you see a measurable performance benefit

I like the metaphor from the Swift Concurrency Manifesto of having an ‘island of serialization in a sea of concurrency’. This sentiment was also shared in this tweet by Matt Diephouse:

When you apply concurrency with that philosophy in mind, I think it will help you achieve concurrent code that can be reasoned about without getting lost in a mess of callbacks.

If you have any questions or comments, feel free to reach out to me on Twitter

Besher Al Maleh

Cover photo by Pascal Meier on Unsplash

Download the companion app here:

almaleh/Dispatcher
Companion app to my article on concurrency. Contribute to almaleh/Dispatcher development by creating an account on GitHub.
Fireworks — A visual particles editor for Swift
Generate Swift code on the fly for macOS and iOS as you design and iterate on your particle effects
You don’t (always) need [weak self]
In this article, we’ll talk about weak self inside of Swift closures to avoid retain cycles & explore cases where it may or may not be necessary to capture self weakly.

Further reading:

Introduction
Explains how to implement concurrent code paths in an application.
Concurrent Programming: APIs and Challenges · objc.io
objc.io publishes books on advanced techniques and practices for iOS and OS X development
Low-Level Concurrency APIs · objc.io
objc.io publishes books on advanced techniques and practices for iOS and OS X development

http://khanlou.com/2016/04/the-GCD-handbook/

Concurrent vs serial queues in GCD
I’m struggling to fully understand the concurrent and serial queues in GCD. I have some issues and hoping someone can answer me clearly and at the point. I’m reading that serial queues are created...

WWDC Videos:

Modernizing Grand Central Dispatch Usage - WWDC 2017 - Videos - Apple Developer
macOS 10.13 and iOS 11 have reinvented how Grand Central Dispatch and the Darwin kernel collaborate, enabling your applications to run...
Building Responsive and Efficient Apps with GCD - WWDC 2015 - Videos - Apple Developer
watchOS and iOS Multitasking place increased demands on your application’s efficiency and responsiveness. With expert guidance from the...