Low-Level Concurrency APIs

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In this article

In this article we’ll talk about some low-level APIs available on both iOS and OS X. Except for dispatch_once, we generally discourage using any of this.

But we wanted to show what’s available under the covers. These low-level APIs provide a huge amount of flexibility, yet with that flexibility comes a lot of complexity and responsibility. The higher-level APIs and patterns that we mention in our article about common background practices let you focus on your task at hand and save you a lot of trouble. And generally, the higher-level APIs will provide better performance unless you can afford the time and effort to tweak and debug code that uses lower-level APIs.

Knowing how things work further down the software stack is good, though. We hope this article will give you a better understanding of the platform, and at the same time, will make you appreciate the higher-level APIs more.

First, we’ll go through most of the bits and pieces that make up Grand Central Dispatch. It’s been around for some years and Apple keeps adding to it and improving it. Apple has open sourced it, which means it’s available for other platforms, too. Finally, we’ll take a look at atomic operations – another set of low-level building blocks.

Probably the best book ever written about concurrent programming is M. Ben-Ari: “Principles of Concurrent Programming”, ISBN 0-13-701078-8. If you’re doing anything with concurrent programming, you need to read this. It’s more than 30 years old, and is still unsurpassed. Its concise writing, excellent examples, and exercises take you through the fundamental building blocks of concurrent programming. It’s out of print, but there are still some copies floating around. There’s a new version called “Principles of Concurrent and Distributed Programming”, ISBN 0-321-31283-X that seems to cover much of the same, but I haven’t read it myself.

Once Upon a Time…

Probably the most widely used and misused feature in GCD is dispatch_once. Correctly used, it will look like this:

+ (UIColor *)boringColor;
    static UIColor *color;
    static dispatch_once_t onceToken;
    dispatch_once(&onceToken, ^{
        color = [UIColor colorWithRed:0.380f green:0.376f blue:0.376f alpha:1.000f];
    return color;

The block will only get run once. And during successive calls, the check is very performant. You can use this to initialize global data such as singletons. Beware that using dispatch_once_t makes testing very difficult – singletons and testing don’t go well together.

Make sure that the onceToken is declared static or has global scope. Anything else causes undefined behavior. In other words: Do not put a dispatch_once_t as a member variable into an object or the like.

Back in ancient times (i.e. a few years ago), people would use pthread_once, which you should never use again, as dispatch_once is easier to use and less error-prone.

Delaying / After

Another common companion is dispatch_after. It lets you do work a little bit later. It’s powerful, but beware: You can easily get into a lot of trouble. Common usage looks like this:

- (void)foo
    double delayInSeconds = 2.0;
    dispatch_time_t popTime = dispatch_time(DISPATCH_TIME_NOW, (int64_t) (delayInSeconds * NSEC_PER_SEC));
    dispatch_after(popTime, dispatch_get_main_queue(), ^(void){
        [self bar];

This looks awesome at first sight. There are a few drawbacks, though. We can’t (directly) cancel the block we’re submitting to dispatch_after. It will run.

Another thing to note is there’s a problematic tendency where people use dispatch_after to work around timing bugs they have in their code. Some code gets run too early and you may not know why, so you put it inside a dispatch_after. Now everything works. But some weeks later it stops working and since you have no clear indication of in which order your code runs, debugging turns into a nightmare. Don’t do this. Most of the time, you’re better of putting your code into the right place. If inside -viewWillAppear is too early, perhaps -viewDidAppear is the right place.

You’ll save yourself a lot of trouble by creating direct calls (analogous to -viewDidAppear) in your own code instead of relying on dispatch_after.

If you need something to run at a specific point in time, dispatch_after may be the right thing, though. Be sure to check out NSTimer, too. That API is a tiny bit more cumbersome, but it allows you to cancel the firing of the timer.


One of the basic building blocks of GCD is queues. Below, we’ll give a few examples on how you can put them to use. When using queues, you’ll do yourself a favor when giving them a good label. While debugging, this label is displayed within Xcode (and lldb), and will help you understand what your app is up to:

- (id)init;
    self = [super init];
    if (self != nil) {
        NSString *label = [NSString stringWithFormat:@"%@.isolation.%p", [self class], self];
        self.isolationQueue = dispatch_queue_create([label UTF8String], NULL);

        label = [NSString stringWithFormat:@"%@.work.%p", [self class], self];
        self.workQueue = dispatch_queue_create([label UTF8String], NULL);
    return self;

Queues can be either concurrent or serial. By default, they’re serial, which means that only a single block runs at any given time. That’s how isolation queues work, which we’ll get to in a bit. Queues can also be concurrent, which allows multiple blocks to run at the same time.

GCD queues use threads internally. GCD manages these threads, and thus when using GCD you don’t need to create threads yourself. But the important takeaway is that GCD presents to you, the user of the API, a very different abstraction level. When you use GCD to do concurrent work, you don’t think in terms of threads, but instead in terms of queues and work items (blocks submitted to queues). While down below, there’re still threads, GCD’s abstraction level lends itself way better to how you’re usually writing code.

The queues and work items also solve a common problem of consecutive fanning out: If we’re using threads directly, and want to do something concurrently, we may split our work up into 100 smaller work items, and then create threads according to the number of available CPU cores, let’s say eight. We send the work items to those eight threads. As we process those items, we might call some function as part of our work. The person who wrote that function also wanted to use concurrency, and hence also creates eight threads when you call the function. Now you have 8 x 8 = 64 threads, even though you only have eight cores – i.e. only 12% of the threads can actually run at any point in time while the other 88% are not doing anything. With GCD you don’t have this problem, and GCD can even adjust the number of threads when the system turns off cores to save power.

GCD creates a so-called thread pool that roughly matches the number of cores. Remember that threads don’t come for free. Each thread ties up memory and kernel resources. There’s one problem though: If you’re submitting a block to GCD, and that code blocks the thread, this thread is no longer available at that point in time to do other work – it’s blocked. In order to keep processing work items (blocks) on the queues, GCD has to create a new thread and add it to the pool.

If your code is blocking many threads, that can become quite problematic. First off, threads consume resources, but moreover, it’s expensive to create them. And it takes some time. And during that time, GCD can’t process work items at full speed. There are quite a few things that can cause a thread to block, but the most common are I/O related, i.e. reading and writing from / to a file or the network. You should not do that on a GCD queue in a blocking manner for this very reason. Take a look at the Input / Output section below for information about how to do I/O in a way that plays nicely with GCD.

Target Queue

You can set a target queue for any queue that you create. This can be very powerful. And it helps debugging.

It is generally considered good style for a class to create its own queue instead of using a global queue. This way you can set the name of that queue, which eases debugging a lot – Xcode lets you see the names of all queues in the Debug Navigator, or if you’re using lldb directly, (lldb) thread list will print out the queue names. Once you’re using a lot of async stuff, this is very valuable help.

Using a private queue also enforces encapsulation. It’s your queue; you get to decide how to use it.

By default, a newly created queue forwards into the default priority global queue. We’ll talk more about priorities in a bit.

You can change which queue your queue forwards into – you can set your queue’s target queue. This way you can chain multiple queues together. Your class Foo has a queue which forwards into the queue of class Bar which forwards into a global queue.

This can be very useful when you use a queue for isolation (which we’ll also talk about). Foo has an isolation queue, and by forwarding into Bar’s isolation queue, it will automatically be thread-safe with respect to the resources that Bar’s isolation queue protects.

Make sure to make your private queue concurrent if you want multiple blocks to run on it. And note that if a queue’s target queue is serial (i.e. non-concurrent), it effectively turns into a serial queue as well.


You change the priority of your own queue by setting its target queue to be one of the global queues. But you should refrain from the temptation to do so.

In most cases, changing the priority is not going to do what you intend. What may seem straightforward is actually a very complex problem. You’ll very easily run into what is known as Priority Inversion. Our article about concurrency APIs and pitfalls has more info on this problem which almost bricked NASA’s Pathfinder rover on Mars.

Furthermore, you need to be particularly careful with the DISPATCH_QUEUE_PRIORITY_BACKGROUND queue. Don’t use it unless you understand what throttled I/O and background status as per setpriority(2) mean. Otherwise the system might end up putting your app to a grinding halt. It is mostly intended for doing I/O in a way such that it doesn’t interfere with other parts of the system doing I/O. But combined with priority inversion, it can easily become a dangerous cocktail.


Isolation queues are one of the most common patterns in GCD queue usage. There are two variations.

Protecting a Resource

The most common scenario in multi-threaded programming is that you have a resource that only one thread is allowed to access at a time.

Our article about concurrency techniques talks a bit more about what resource means in concurrent programming. It’s often a piece of memory or an object that only one thread must access at a time.

Let’s say we need to access a NSMutableDictionary from multiple threads (queues). We would do something like this:

- (void)setCount:(NSUInteger)count forKey:(NSString *)key
    key = [key copy];
    dispatch_async(self.isolationQueue, ^(){
        if (count == 0) {
            [self.counts removeObjectForKey:key];
        } else {
            self.counts[key] = @(count);

- (NSUInteger)countForKey:(NSString *)key;
    __block NSUInteger count;
    dispatch_sync(self.isolationQueue, ^(){
        NSNumber *n = self.counts[key];
        count = [n unsignedIntegerValue];
    return count;

With this, only one thread will access the NSMutableDictionary instance.

Note four things:

  1. Don’t use this code. First read about multiple readers, single writer and also about contention.

  2. We’re using async when storing a value. This is important. We don’t want to and don’t need to block the current thread for the write to complete. When reading, we’re using sync since we need to return the result.

  3. According to the method interface, -setCount:forKey: takes an NSString, which we’re passing onto dispatch_async. The caller is free to pass in an NSMutableString and can modify it after the method returns, but before the block executes. Hence we have to copy the string to guarantee that the method works correctly. If the passed-in string isn’t mutable (i.e. a normal NSString) the call to -copy is basically a no-op.

  4. The isolationQueue needs to have been created with a dispatch_queue_attr_t of DISPATCH_QUEUE_SERIAL (or 0).

One Resource, Multiple Readers, and a Single Writer

We can improve upon the above example. GCD has concurrent queues on which multiple threads can run. We can safely read from the NSMutableDictionary on multiple threads as long as we don’t mutate it at the same time. When we need to change the dictionary, we dispatch the block with a barrier. Such a block runs once all previously scheduled blocks have completed and before any following blocks are run.

We’ll create the queue with:

self.isolationQueue = dispatch_queue_create([label UTF8String], DISPATCH_QUEUE_CONCURRENT);

and then change the setter like this:

- (void)setCount:(NSUInteger)count forKey:(NSString *)key
    key = [key copy];
    dispatch_barrier_async(self.isolationQueue, ^(){
        if (count == 0) {
            [self.counts removeObjectForKey:key];
        } else {
            self.counts[key] = @(count);

When you use concurrent queues, make sure that all barrier calls are async. If you’re using dispatch_barrier_sync you’ll quite likely get yourself (or rather: your code) into a deadlock. Writes need a barrier, and can be async.

A Word on Contention

First off, a word of warning here: The resource we’re protecting in this simple example is an NSMutableDictionary. This serves the purpose of an example very well. But in real code, it is important to put the isolation at the right complexity level.

If you’re accessing the NSMutableDictionary very frequently, you’ll be running into what’s known as lock contention. Lock contention is in no way specific to GCD or queues. Any form of locking mechanism will have the same problem – different locking mechanisms in different ways.

All calls to dispatch_async, dispatch_sync, etc. need to perform some form of locking – making sure that only one thread or specific threads run a given block. GCD can avoid locks to some extent and use scheduling instead, but at the end of the day the problem just shifts. The underlying problem remains: if you have a lot of threads hitting the same lock or queue at the same time, you’ll see performance hits. Performance can degrade severely.

You need to isolate at the right complexity level. When you see performance degrade, it’s a clear sign of a design problem in your code. There are two costs that you need to balance. First is the cost of being inside a critical section for so long that other threads are blocked from entering a critical section, and second is the cost of constantly entering and leaving critical sections. In the world of GCD, the first cost is describing the fact that if a block runs on your isolation queue, it may potentially block other code from running on your isolation queue. The second cost describes the fact that calling dispatch_async and dispatch_sync, while highly optimized, don’t come for free.

Sadly, there can be no general rule of what the right balance is: You need to measure and adjust. Spin up Instruments and see what your app is up to.

If we look at the example code above, our critical code section is only doing very simple things. That may or may not be good, depending on how it’s used.

In your own code, consider if you’re better off by protecting with an isolation queue on a higher level. For example, instead of the class Foo having an isolation queue and it itself protecting access to its NSMutableDictionary, you could have the class Bar that uses Foo have an isolation queue which protects all use of the class Foo. In other words: you would change Foo so that it is no longer thread-safe, (no isolation queue) and then inside Bar, use an isolation queue to make sure that only one thread is using Foo at any point in time.

Going Fully Asynchronous

Let’s sidetrack a bit here. As you’ve seen above, you can dispatch a block, a work unit, both synchronously and asynchronously. A very common problem that we talk about in our article about concurrency APIs and pitfalls is dead locks. It’s quite easy to run into the problem with GCD with synchronous dispatching. The trivial case is:

dispatch_queue_t queueA; // assume we have this
dispatch_sync(queueA, ^(){
    dispatch_sync(queueA, ^(){

Once we hit the second dispatch_sync we’ll deadlock: We can’t dispatch onto queueA, because someone (the current thread) is already on that queue and is never going to leave it. But there are more subtle ways:

dispatch_queue_t queueA; // assume we have this
dispatch_queue_t queueB; // assume we have this

dispatch_sync(queueA, ^(){

void foo(void)
    dispatch_sync(queueB, ^(){

void bar(void)
    dispatch_sync(queueA, ^(){

Each call to dispatch_sync() on its own looks good, but in combination, they’ll deadlock.

This is all inherent to being synchronous. If we use asynchronous dispatching such as

dispatch_queue_t queueA; // assume we have this
dispatch_async(queueA, ^(){
    dispatch_async(queueA, ^(){

things will work just fine. Asynchronous calls will not deadlock. It is therefore very desirable to use asynchronous calls whenever possible. Instead of writing a function or method that returns a value (and hence has to be synchronous), we use a method that calls a result block asynchronously. That way we’re less likely to run into problems with deadlocks.

The downside of asynchronous calls is that they’re difficult to debug. When we stop the code in the debugger, there’s no meaningful backtrace to look at.

Keep both of these in mind. Deadlocks are generally the tougher problem to deal with.

How to Write a Good Async API

If you’re designing an API for others to use (or even for yourself), there are a few good practices to keep in mind.

As we just mentioned, you should prefer asynchronous API. When you create an API, you can get called in ways that are outside your control, and if your code could deadlock, it will.

You should write your functions or methods so that the function calls dispatch_async(). Don’t make the caller call it. The caller should be able to call into your function or method.

If your function or method has a result, deliver it asynchronously through a callback handler. The API should be such that your function or method takes both a result block and a queue to deliver the result on. The caller of your function should not have to dispatch onto the queue themself. The reason for this is quite simple: Almost all the time, the caller needs to be on a certain queue, and this way the code is easier to read. And your function will (should) call dispatch_async() anyhow to run the callback handler, so it might as well do that on the queue that the caller needs it to be on.

If you’re writing a class, it is probably a good option to let the user of the class set a queue that all callbacks will be delivered on. Your code would look like this:

- (void)processImage:(UIImage *)image completionHandler:(void(^)(BOOL success))handler;
    dispatch_async(self.isolationQueue, ^(void){
        // do actual processing here
        dispatch_async(self.resultQueue, ^(void){

If you write your classes this way, it’s easy to make classes work together. If class A uses class B, it will set the callback queue of B to be its own isolation queue.

Iterative Execution

If you’re doing some number crunching and the problem at hand can be dissected in smaller parts of identical nature, dispatch_apply can be very useful.

If you have some code like this

for (size_t y = 0; y < height; ++y) {
    for (size_t x = 0; x < width; ++x) {
        // Do something with x and y here

you may be able to speed it up by simply changing it to

dispatch_apply(height, dispatch_get_global_queue(0, 0), ^(size_t y) {
    for (size_t x = 0; x < width; x += 2) {
        // Do something with x and y here

How well this works depends a lot on exactly what you’re doing inside that loop.

The work done by the block must be non trivial, otherwise the overhead is too big. Unless the code is bound by computational bandwidth, it is critical that the memory that each work unit needs to read from and write to fits nicely into the cache size. This can have dramatic effects on performance. Code bound by critical sections may not perform well at all. Going into detail about these problems is outside the scope of this article. Using dispatch_apply may help performance, yet performance optimization is a complex topic on its own. Wikipedia has an article about Memory-bound function. Memory access speed changes dramatically between L2, L3, and main memory. Seeing a performance drop of 10x is not uncommon when your data access pattern doesn’t fit within the cache size.


Quite often, you’ll find yourself chaining asynchronous blocks together to perform a given task. Some of these may even run in parallel. Now, if you want to run some code once this task is complete, i.e. all blocks have completed, “groups” are the right tool. Here’s a sample:

dispatch_group_t group = dispatch_group_create();

dispatch_queue_t queue = dispatch_get_global_queue(0, 0);
dispatch_group_async(group, queue, ^(){
    // Do something that takes a while
    [self doSomeFoo];
    dispatch_group_async(group, dispatch_get_main_queue(), ^(){
        self.foo = 42;
dispatch_group_async(group, queue, ^(){
    // Do something else that takes a while
    [self doSomeBar];
    dispatch_group_async(group, dispatch_get_main_queue(), ^(){
        self.bar = 1;

// This block will run once everything above is done:
dispatch_group_notify(group, dispatch_get_main_queue(), ^(){
    NSLog(@"foo: %d", self.foo);
    NSLog(@"bar: %d", self.bar);

The important thing to note is that all of this is entirely non-blocking. At no point are we telling the current thread to wait until something else is done. Quite contrary, we’re simply enqueuing multiple blocks. Since this code pattern doesn’t block, it’s not going to cause a deadlock.

Also note how we’re switching between different queues in this small and simple example.

Using dispatch_group_t with Existing API

Once you’ve added groups to your tool belt, you’ll be wondering why most async API doesn’t take a dispatch_group_t as an optional argument. But there’s no reason to despair: It’s easy to add that yourself, although you have to be more careful to make sure your code is balanced.

We can, for example, add it to Core Data’s -performBlock: API like this:

- (void)withGroup:(dispatch_group_t)group performBlock:(dispatch_block_t)block
    if (group == NULL) {
        [self performBlock:block];
    } else {
        [self performBlock:^(){

This allows us to use dispatch_group_notify to run a block when a set of operations on Core Data (possibly combined with other blocks) completes.

We can obviously do the same for NSURLConnection:

+ (void)withGroup:(dispatch_group_t)group 
        sendAsynchronousRequest:(NSURLRequest *)request 
        queue:(NSOperationQueue *)queue 
        completionHandler:(void (^)(NSURLResponse*, NSData*, NSError*))handler
    if (group == NULL) {
        [self sendAsynchronousRequest:request 
    } else {
        [self sendAsynchronousRequest:request 
                    completionHandler:^(NSURLResponse *response, NSData *data, NSError *error){
            handler(response, data, error);

In order for this to work, you need to make sure that

  • dispatch_group_enter() is guaranteed to run before dispatch_group_leave()
  • Calls to dispatch_group_enter() and dispatch_group_leave() are always balanced (even when errors happen)


One of the lesser-known features of GCD is the event sources dispatch_source_t.

Just like most of GCD, this is pretty low-level stuff. When you need it, it can be extremely useful, though. Some of it is extremely esoteric, and we’ll just touch upon a few of the uses. A lot of this isn’t very useful on iOS where you can’t launch processes (hence no point in watching them) and you can’t write outside your app bundle (hence no need to watch files), etc.

GCD sources are implemented in an extremely resource-efficient way.

Watching Processes

If some process is running and you want to know when it exits, GCD has you covered. You can also use it to check when that process forks, i.e. spawns child processes or a signal was delivered to the process (e.g. SIGTERM).

@import AppKit;
// ...
NSArray *array = [NSRunningApplication 
if (array == nil || [array count] == 0) {
pid_t const pid = [[array firstObject] processIdentifier];
self.source = dispatch_source_create(DISPATCH_SOURCE_TYPE_PROC, pid, 
dispatch_source_set_event_handler(self.source, ^(){
    NSLog(@"Mail quit.");
    // If you would like continue watching for the app to quit,
    // you should cancel this source with dispatch_source_cancel and create new one
    // as with next run app will have another process identifier.

This will print Mail quit. when the Mail.app exits.

Note that you must call dispatch_resume() before any events will be delivered to your event handler.

Watching Files

The possibilities seem near endless. You can watch a file directly for changes, and the source’s event handler will get called when a change happens.

You can also use this to watch a directory, i.e. create a watch folder:

NSURL *directoryURL; // assume this is set to a directory
int const fd = open([[directoryURL path] fileSystemRepresentation], O_EVTONLY);
if (fd < 0) {
    char buffer[80];
    strerror_r(errno, buffer, sizeof(buffer));
    NSLog(@"Unable to open \"%@\": %s (%d)", [directoryURL path], buffer, errno);
dispatch_source_t source = dispatch_source_create(DISPATCH_SOURCE_TYPE_VNODE, fd, 
dispatch_source_set_event_handler(source, ^(){
    unsigned long const data = dispatch_source_get_data(source);
    if (data & DISPATCH_VNODE_WRITE) {
        NSLog(@"The directory changed.");
    if (data & DISPATCH_VNODE_DELETE) {
        NSLog(@"The directory has been deleted.");
dispatch_source_set_cancel_handler(source, ^(){
self.source = source;

You should probably always add DISPATCH_VNODE_DELETE to check if the file or directory has been deleted – and then stop monitoring it.


In most cases, NSTimer is your go-to place for timer events. GCD’s version is lower-level. It gives you more control – use that carefully.

It is extremely important to point out that specifying a low leeway value for GCD timers interferes with the OS’s attempt to conserve power. You’ll be burning more battery if you unnecessarily specify a low leeway value.

Here we’re setting up a timer to fire every 5 seconds and allow for a leeway of 1/10 of a second:

dispatch_source_t source = dispatch_source_create(DISPATCH_SOURCE_TYPE_TIMER, 
dispatch_source_set_event_handler(source, ^(){
    NSLog(@"Time flies.");
dispatch_source_set_timer(source, DISPATCH_TIME_NOW, 5ull * NSEC_PER_SEC, 
  100ull * NSEC_PER_MSEC);
self.source = source;


All sources allow you to add a cancel handler. This can be useful to clean up any resources you’ve created for the event source, such as closing file descriptors. GCD guarantees that all calls to the event handler have completed before the cancel handler gets called.

See the use of dispatch_source_set_cancel_handler() in the above example for a watch folder.

Input / Output

Writing code that performs well while doing heavy I/O is extremely tricky. GCD has a few tricks up its sleeve to help. Without going into too much detail, we’ll shortly touch upon what these problems are, and how GCD approaches them.

Traditionally when you were reading data from a network socket, you’d either have to do a blocking read, i.e. parking a thread until data became available, or you’d have to repeatedly poll. Both approaches are wasteful and don’t scale. However, kqueue solved the polling by posting an event once data became available, and GCD uses the same approach, although more elegantly. When writing data to a socket, the identical problems exist, where you have to either perform a blocking write, or wait for the socket to be able to accept data.

The second problem when performing I/O is that data arrives in small chunks; when reading from a network, the chunk size is typically around 1.5k bytes due to the MTU – maximum transmission unit. This can be anything, though. Once this data arrives, you’re often interested in data that spans multiple chunks, and traditionally you would concatenate the data into one larger buffer and then process that. Let’s say (contrived example), you receive these eight chunks

0: HTTP/1.1 200 OK\r\nDate: Mon, 23 May 2005 22:38
1: :34 GMT\r\nServer: Apache/ (Unix) (Red-H
2: at/Linux)\r\nLast-Modified: Wed, 08 Jan 2003 23
3: :11:55 GMT\r\nEtag: "3f80f-1b6-3e1cb03b"\r\nCon
4: tent-Type: text/html; charset=UTF-8\r\nContent-
5: Length: 131\r\nConnection: close\r\n\r\n<html>\r
6: \n<head>\r\n  <title>An Example Page</title>\r\n
7: </head>\r\n<body>\r\n  Hello World, this is a ve

If you were looking for an HTTP header, it’d be much simpler to concatenate all data chunks into a larger buffer and then scan for \r\n\r\n. But in doing so, you’d be copying around data a lot. The other problem with a lot of the old C APIs is that there’s no ownership of buffers so that functions had to copy data into their own buffers – another copy. Copying data may seem trivial, but when you’re doing a lot of I/O you’ll see those copies show up in your profiling tool (Instruments). Even if you only copy each memory region once, you’re incurring twice the memory bandwidth and you’re burning through twice the amount of memory cache.

GCD and Buffers

The more straightforward piece is about data buffers. GCD has a dispatch_data_t type that to some extent is similar to what NSData does for Objective-C. It can do other things, though, and is more generic.

Note that dispatch_data_t can be retained and released, and the dispatch_data_t object owns the buffer it holds onto.

This may seem trivial, but we have to remember that GCD is a plain C API, and can’t use Objective-C. The traditional way was to have a buffer either backed by the stack or a malloc’d memory region – these don’t have ownership.

One relatively unique property of dispatch_data_t is that it can be backed by disjoint memory regions. That solves the concatenation problem we just mentioned. When you concatenate two data objects with

dispatch_data_t a; // Assume this hold some valid data
dispatch_data_t b; // Assume this hold some valid data
dispatch_data_t c = dispatch_data_create_concat(a, b);

the data object c will not copy a and b into a single, larger memory region. Instead it will simply retain both a and b. You can then traverse the memory regions represented by c with dispatch_data_apply:

dispatch_data_apply(c, ^bool(dispatch_data_t region, size_t offset, const void *buffer, size_t size) {
    fprintf(stderr, "region with offset %zu, size %zu\n", offset, size);
    return true;

Similarly you can create a subrange with dispatch_data_create_subrange that won’t do any copying.

Reading and Writing

At its core, Dispatch I/O is about so-called channels. A dispatch I/O channel provides a different way to read and write from a file descriptor. The most basic way to create such a channel is by calling

dispatch_io_t dispatch_io_create(dispatch_io_type_t type, dispatch_fd_t fd, 
  dispatch_queue_t queue, void (^cleanup_handler)(int error));

This returns the created channel which then owns the file descriptor. You must not modify the file descriptor in any way after you’ve created a channel from it.

There are two fundamentally different types of channels: streams and random access. If you open a file on disk, you can use it to create a random access channel (because such a file descriptor is seekable). If you open a socket, you can create a stream channel.

If you want to create a channel for a file, you’re better off using dispatch_io_create_with_path, which takes a path, and lets GCD open the file. This is beneficial, since GCD can postpone opening the file – hence limiting the number of files that are open at the same time.

Analogous to the normal read(2), write(2), and close(2), GCD offers dispatch_io_read, dispatch_io_write, and dispatch_io_close. Reading and writing is done via a callback block that is called whenever data is read or written. This effectively implements non-blocking, fully async I/O.

We can’t go into all details here, but here’s an example for setting up a TCP server:

First we create a listening socket and set up an event source for incoming connections:

_isolation = dispatch_queue_create([[self description] UTF8String], NULL);
_nativeSocket = socket(PF_INET6, SOCK_STREAM, IPPROTO_TCP);
struct sockaddr_in sin = {};
sin.sin_len = sizeof(sin);
sin.sin_family = AF_INET6;
sin.sin_port = htons(port);
sin.sin_addr.s_addr= INADDR_ANY;
int err = bind(result.nativeSocket, (struct sockaddr *) &sin, sizeof(sin));
NSCAssert(0 <= err, @"");

_eventSource = dispatch_source_create(DISPATCH_SOURCE_TYPE_READ, _nativeSocket, 0, _isolation);
dispatch_source_set_event_handler(result.eventSource, ^{

When accepting a connection, we create an I/O channel:

typedef union socketAddress {
    struct sockaddr sa;
    struct sockaddr_in sin;
    struct sockaddr_in6 sin6;
} socketAddressUnion;

socketAddressUnion rsa; // remote socket address
socklen_t len = sizeof(rsa);
int native = accept(nativeSocket, &rsa.sa, &len);
if (native == -1) {
    // Error. Ignore.
    return nil;

_remoteAddress = rsa;
_isolation = dispatch_queue_create([[self description] UTF8String], NULL);
_channel = dispatch_io_create(DISPATCH_IO_STREAM, native, _isolation, ^(int error) {
    NSLog(@"An error occured while listening on socket: %d", error);

//dispatch_io_set_high_water(_channel, 8 * 1024);
dispatch_io_set_low_water(_channel, 1);
dispatch_io_set_interval(_channel, NSEC_PER_MSEC * 10, DISPATCH_IO_STRICT_INTERVAL);

socketAddressUnion lsa; // remote socket address
socklen_t len = sizeof(rsa);
getsockname(native, &lsa.sa, &len);
_localAddress = lsa;

If we want to set SO_KEEPALIVE (if we’re using HTTP-level keep-alive), we need to do so before calling dispatch_io_create.

Having created a dispatch_io channel, we can set up the read handler:

dispatch_io_read(_channel, 0, SIZE_MAX, _isolation, ^(bool done, dispatch_data_t data, int error){
    if (data != NULL) {
        if (_data == NULL) {
            _data = data;
        } else {
            _data = dispatch_data_create_concat(_data, data);
        [self processData];

If all you want to do is to read from or write to a file, GCD provides two convenience wrappers: dispatch_read and dispatch_write. You pass dispatch_read a file path and a block to be called for each data block that’s read. Similarly, dispatch_write takes a file path and a dispatch_data_t object to be written.


In the obscure corners of GCD, you’ll find a neat little tool for optimizing your code:

uint64_t dispatch_benchmark(size_t count, void (^block)(void));

Put this declaration into your code, and you can measure the average number of nanoseconds the given block takes to execute. E.g.

size_t const objectCount = 1000;
uint64_t n = dispatch_benchmark(10000, ^{
    @autoreleasepool {
        id obj = @42;
        NSMutableArray *array = [NSMutableArray array];
        for (size_t i = 0; i < objectCount; ++i) {
            [array addObject:obj];
NSLog(@"-[NSMutableArray addObject:] : %llu ns", n);

On my machine this outputs

-[NSMutableArray addObject:] : 31803 ns

i.e. that adding 1000 objects to an NSMutableArray takes 31803 ns, or about 32 ns per object.

As the man page for dispatch_benchmark points out, measuring performance isn’t as trivial as it may seem. Particularly when comparing concurrent code with non-concurrent, you need to pay attention to computational bandwidth and memory bandwidth of the particular piece of hardware you’re running on. It will change a lot from machine to machine. And the contention problem we mentioned above will affect code if its performance is bound by access to critical sections.

Don’t put this into shipping code. Aside from the fact that it’d be pretty pointless to do so, it’s also private API. It’s intended for debugging and performance analysis work only.

View the man page with

curl "http://opensource.apple.com/source/libdispatch/libdispatch-84.5/man/dispatch_benchmark.3?txt" 
  | /usr/bin/groffer --tty -T utf8

Atomic Operations

The header file libkern/OSAtomic.h has lots of powerful functions for lower-level multi-threaded programming. Although it’s part of the kernel header files, it’s intended to also be used outside kernel and driver programming.

These functions are lower-level, and there are a few extra things you need to be aware of. If you do, though, you might find a thing or two here, that you’d otherwise not be able to do – or not do as easily. It’s mostly interesting if you’re working on high-performance code and / or are implementing lock-free and wait-free algorithms.

These functions are all summarized in the atomic(3) man page – run man 3 atomic to get the complete documentation. You’ll see it talking about memory barriers. Check out the Wikipedia article about memory barriers. If you’re in doubt, you probably need a memory barrier.


There’s a long list of OSAtomicIncrement and OSAtomicDecrement functions that allow you to increment and decrement an integer value in an atomic way – thread safe without having to take a lock (or use queues). These can be useful if you need to increment global counters from multiple threads for statistics. If all you do is increment a global counter, the barrier-free OSAtomicIncrement versions are fine, and when there’s no contention, they’re cheap to call.

Similarly, the OSAtomicOr, OSAtomicAnd, and OSAtomicXor functions can be used to perform logical operations, and OSAtomicTest functions to set or clear bits.

Compare and Swap

The OSAtomicCompareAndSwap can be useful to do lock-free lazy initializations like this:

void * sharedBuffer(void)
    static void * buffer;
    if (buffer == NULL) {
        void * newBuffer = calloc(1, 1024);
        if (!OSAtomicCompareAndSwapPtrBarrier(NULL, newBuffer, &buffer)) {
    return buffer;

If there’s no buffer, we create one and then atomically write it to buffer if buffer is NULL. In the rare case that someone else set buffer at the same time as the current thread, we simply free it. Since the compare-and-swap method is atomic, this is a thread-safe way to lazily initialize values. The check that NULL and setting buffer are done atomically.

Obviously, you can do something similar with dispatch_once().

Atomic Queues

The OSAtomicEnqueue() and OSAtomicDequeue() let you implement LIFO queues (also known as stacks) in a thread-safe, lock-free way. For code that has strict latency requirements, this can be a powerful building block.

There’s also a OSAtomicFifoEnqueue() and OSAtomicFifoDequeue() for FIFO queues, but these only have documentation in the header file – read carefully when using.

Spin Locks

Finally, the OSAtomic.h header defines the functions to work with spin locks: OSSpinLock. Again, Wikipedia has in-depth information on spin locks. Check out the spinlock(3) man page with man 3 spinlock. The short story is that a spin lock is very cheap when there’s no lock contention.

Spin locks can be useful in certain situations as a performance optimization. As always: measure first, then optimize. Don’t do optimistic optimizations.

Here’s an example for OSSpinLock:

@interface MyTableViewCell : UITableViewCell

@property (readonly, nonatomic, copy) NSDictionary *amountAttributes;


@implementation MyTableViewCell
    NSDictionary *_amountAttributes;

- (NSDictionary *)amountAttributes;
    if (_amountAttributes == nil) {
        static __weak NSDictionary *cachedAttributes = nil;
        static OSSpinLock lock = OS_SPINLOCK_INIT;
        _amountAttributes = cachedAttributes;
        if (_amountAttributes == nil) {
            NSMutableDictionary *attributes = [[self subtitleAttributes] mutableCopy];
            attributes[NSFontAttributeName] = [UIFont fontWithName:@"ComicSans" size:36];
            attributes[NSParagraphStyleAttributeName] = [NSParagraphStyle defaultParagraphStyle];
            _amountAttributes = [attributes copy];
            cachedAttributes = _amountAttributes;
    return _amountAttributes;

In the above example, it’s probably not worth the trouble, but it shows the concept. We’re using ARC’s __weak to make sure that the amountAttributes will get dealloc‘ed once all instances of MyTableViewCell go away. Yet we’re able to share a single instance of this dictionary among all instances.

The reason this performs well is that we’re unlikely to hit the inner-most part of the method. This is quite esoteric – don’t use this in your App unless you have a real need.