並行性の説明:マルチスレッドiOSアプリを構築する方法

iOSでの並行性は大きなトピックです。したがって、この記事では、キューとGrand Central Dispatch(GCD)フレームワークに関するサブトピックにズームインしたいと思います。

特に、シリアルキューと並行キューの違い、および同期実行と非同期実行の違いについて調べたいと思います。

これまでGCDを使用したことがない場合は、この記事から始めるのが最適です。GCDの経験はあるものの、上記のトピックに興味がある場合は、GCDが役立つと思います。そして、私はあなたが途中で1つか2つの新しいものを拾うことを望みます。

この記事の概念を視覚的に示すために、SwiftUIコンパニオンアプリを作成しました。このアプリには、この記事を読む前後に試すことをお勧めする楽しい短いクイズもあります。ここからソースコードをダウンロードするか、ここからパブリックベータを入手してください。

まずGCDの概要から始め、次に同期、非同期、シリアル、並行について詳しく説明します。その後、並行性を扱う際のいくつかの落とし穴について説明します。最後に、要約といくつかの一般的なアドバイスで終わります。

前書き

GCDとディスパッチキューの簡単な紹介から始めましょう。このトピックに既に精通している場合は、[同期と非同期]セクションに進んでください。

並行性とグランドセントラルディスパッチ

同時実行性により、デバイスに複数のCPUコアがあるという事実を利用できます。これらのコアを利用するには、複数のスレッドを使用する必要があります。ただし、スレッドは低レベルのツールであり、効率的な方法で手動でスレッドを管理することは非常に困難です。

Grand Central Dispatchは、開発者がスレッド自体を手動で作成および管理することなくマルチスレッドコードを記述できるようにするための抽象化として、10年以上前にAppleによって作成されました。

GCDを使用して、Appleは非同期設計アプローチを採用しました問題に。スレッドを直接作成する代わりに、GCDを使用して作業タスクをスケジュールすると、システムはそのリソースを最大限に活用してこれらのタスクを実行します。GCDは、必要なスレッドの作成を処理し、それらのスレッドでタスクをスケジュールして、スレッド管理の負担を開発者からシステムに移します。

GCDの大きな利点は、並行コードを作成するときにハードウェアリソースについて心配する必要がないことです。GCDはスレッドプールを管理し、シングルコアのAppleWatchからメニーコアのMacBookProまで拡張できます。

ディスパッチキュー

これらはGCDの主要な構成要素であり、定義したパラメーターのセットを使用してコードの任意のブロックを実行できます。ディスパッチキュー内のタスクは、常に先入れ先出し(FIFO)方式で開始されます。タスクの完了時間はいくつかの要因に依存し、FIFOであることが保証されていないため、開始したと言ったことに注意してください(これについては後で詳しく説明します)。

大まかに言えば、使用できるキューには次の3種類があります。

  • メインディスパッチキュー(シリアル、事前定義済み)
  • グローバルキュー(同時、事前定義)
  • プライベートキュー(シリアルまたはコンカレントにすることができます。作成します)

すべてのアプリには、メインスレッドでタスクを実行するシリアルキューであるメインキューが付属しています。このキューは、アプリケーションのUIを描画し、ユーザーの操作(タッチ、スクロール、パンなど)に応答する役割を果たします。このキューを長時間ブロックすると、iOSアプリがフリーズしたように見え、macOSアプリに悪名高いビーチが表示されます。ボール/スピニングホイール。

長時間実行されるタスク(ネットワーク呼び出し、計算量の多い作業など)を実行する場合、バックグラウンドキューでこの作業を実行することにより、UIのフリーズを回避します。次に、メインキューの結果でUIを更新します。

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) } } }

経験則として、すべてのUI作業はメインキューで実行する必要があります。Xcodeのメインスレッドチェッカーオプションをオンにすると、UI作業がバックグラウンドスレッドで実行されるたびに警告を受け取ることができます。

メインスレッドチェッカーはスキームエディターにあります

メインキューに加えて、すべてのアプリには、さまざまなレベルのサービス品質(GCDの優先度の抽象的な概念)を持ついくつかの事前定義された同時キューが付属しています。

たとえば、ユーザーの対話型(最高優先度)のQoSキューに非同期で作業を送信するコードは次のとおりです。

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

または、次のようにQoSを指定しないことで、デフォルトの優先度のグローバルキューを呼び出すことができます。

DispatchQueue.global().async { print("Generic global queue") }

さらに、次の構文を使用して独自のプライベートキューを作成できます。

let serial = DispatchQueue(label: "com.besher.serial-queue") serial.async { print("Private serial queue") }

プライベートキューを作成するときは、説明ラベル(DNSの逆表記など)を使用すると便利です。これは、Xcodeのナビゲーター、lldb、およびInstrumentsでのデバッグ時に役立ちます。

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") }

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.

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 } }

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.

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.

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.

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("?") } }

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 frontof 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 queueis 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.

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 {}

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 relationto 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.

スレッドサニタイザーはスキームエディターでアクセスできます

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

私は、「並行性の海の中でシリアル化の島」を持っているというSwift ConcurrencyManifestoのメタファーが好きです。この感情は、MattDiephouseによるこのツイートでも共有されました。

並行コードを書く秘訣は、そのほとんどをシリアルにすることです。並行性を小さな外側のレイヤーに制限します。(シリアルコア、並行シェル。)

たとえば、ロックを使用して5つのプロパティを管理する代わりに、それらをラップする新しいタイプを作成し、ロック内で1つのプロパティを使用します。

— Matt Diephouse(@mdiep)2019年12月18日

その哲学を念頭に置いて並行性を適用すると、コールバックの混乱に迷うことなく推論できる並行コードを実現するのに役立つと思います。

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Besher Al Maleh

UnsplashのOnurKによるカバー写真

ここからコンパニオンアプリをダウンロードします。

並行性に関する私の記事へのalmaleh / DispatcherCompanionアプリ。GitHubでアカウントを作成して、almaleh / Dispatcherの開発に貢献します。almaleh GitHub

私の他の記事のいくつかをチェックしてください:

Fireworks —Swift用のビジュアルパーティクルエディタパーティクルエフェクトを設計および反復するときに、macOSおよびiOS用のSwiftコードをオンザフライで生成します。BesherAlMaleh完璧なiOS (常に)必要ありません[弱い自己]この記事では、保持サイクルを回避するためにSwiftクロージャー内の弱い自己について話し、自己を弱くキャプチャする必要がある場合とない場合がある場合を調査します。Besher AlMaleh完璧なiOS

参考文献:

はじめにアプリケーションに並行コードパスを実装する方法について説明します。並行プログラミング:APIと課題・objc.io objc.io iOSとOS Xの開発のための高度な技術と実践に関する書籍を出版フロリアンクーグラー低レベルの同時実行性のAPI・はobjc.io objc.io iOS用の高度な技術と実践に関する書籍を出版し、OSX開発DanielEggert

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

GCDの並行キューとシリアルキューGCDの並行キューとシリアルキューを完全に理解するのに苦労しています。私はいくつかの問題を抱えており、誰かが私に明確にそしてその時点で答えてくれることを望んでいます。私はシリアルキューが作成されていることを読んでいます...ボグダンアレクサンドルスタックオーバーフロー

WWDCビデオ:

Grand CentralDispatchの使用法の最新化-WWDC2017-ビデオ-AppleDeveloper macOS10.13とiOS11は、Grand Central DispatchとDarwinカーネルの連携方法を再発明し、アプリケーションの実行を可能にしました... Apple Developer Building Responsive and Efficient Apps with GCD-WWDC 2015 -ビデオ-AppleDeveloper watchOSおよびiOSマルチタスクでは、アプリケーションの効率と応答性に対する要求が高まっています。専門家の指導を受けて... Apple Developer