The Boost documentation is substantial, but can still be daunting to new users. So this article is the first of a series on using Boost, starting with basic threading. It aims to provide an accessible introduction, with complete working examples. First, this article assumes you know about general threading concepts and the basics of how to use them on your platform. For a refresher, see the Wikipedia article on Threads. Here I focus specifically on how to use Boost threads in a practical setting, starting from the basics.
I also assume you have Boost installed and ready to use see the Boost Getting Started Guide for details. This article looks specifically at the different ways to create threads. There are many other techniques necessary for real multi-threaded systems, such as synchronisation and mutual exclusion, which will be covered in a future article.
A boost::thread object represents a single thread of execution, as you would normally create and manage using your operating system specific interfaces. Because Boost abstracts away all the platform-specific code, you can easily write sophisticated and portable code that runs across all major platforms. A boost::thread object is normally constructed by passing the threading function or method it is to run.
There are actually a number of different ways to do so. I cover the main thread creation approaches below. All the code examples are provided in the repository below, which you can use for any purpose, no strings attached. The usual disclaimer is that no warranties apply! And just as Boost runs on many platforms ie. Clone the source with Mercurialor download a zip here:. There is a separate example program for each section below, and a common Bjam script to build them all Jamroot.
Bjam is the Boost build system, a very powerful but notoriously difficult to learn, and worthy of a whole series of articles. Having said that, you are certainly not obliged to use Bjam. It is still worth knowing how to build applications manually before relying on scripts, so here is an example command line for manual compilation on my system Mac OS X with Boost installed from MacPorts :.
You can use the above as the basis for writing our own Makefile if you prefer, or creating build rules in your IDE of choice. This is simply to avoid cluttering up the examples with code that would take some finite time to execute but would otherwise be irrelevant.
In the main function, we then wait for the worker thread to complete using the join method. This will cause the main thread to sleep until the worker thread completes successfully or otherwise.Forums New posts Search forums.
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Boost Multithread Queue Example
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In particular, we introduce the thread concept. A thread is a software entity and it represents an independent unit of execution in a program. We design an application by creating threads and letting them execute separate parts of the program code with the objective of improving the speedup of an application.
We define the speedup as a number that indicates how many times faster a parallel program is than its serial equivalent. When the speedup equals the number of processors we then say that the speedup is perfectly linear. The improved performance of parallel programs comes at a price and in this case we must ensure that the threads are synchronised so that they do not destroy the integrity of the shared data. To this end, Boost. Thread has a number of synchronisation mechanisms to protect the program from data races, and ensuring that the code is thread-safe.
We also show how to define locks on objects and data so that only one thread can update the data at any given time.These features are experimental and subject to change in future versions. There are not too much tests yet, so it is possible that you can find out some trivial bugs :.
The key problem with protecting shared data with a mutex is that there is no easy way to associate the mutex with the data. It is thus relatively easy to accidentally write code that fails to lock the right mutex - or even locks the wrong mutex - and the compiler will not help you.Ac land gazipur
Moreover, managing the mutex lock also clutters the source code, making it harder to see what is really going on. Both forms of pointer dereference return a proxy object rather than a real reference, to ensure that the lock on the mutex is held across the assignment or method call, but this is transparent to the user. The pointer-like semantics work very well for simple accesses such as assignment and calls to member functions.
However, sometimes you need to perform an operation that requires multiple accesses under protection of the same lock, and that's what the synchronize method provides. This is just the same as when acquiring any two mutexes. Lockable is Lockable. T is DefaultConstructible. T is CopyConstructible. T is DefaultConstructible and Assignable. Assigns the value on a scope protected by the mutex of the rhs. The mutex is not copied. T is MoveConstructible.
T is Assignable. Copies the underlying value on a scope protected by the two mutexes. The locks are acquired avoiding deadlock. A copy of the protected value obtained on a scope protected by the mutex.
Threading with Boost - Part I: Creating Threads
Swaps the data on a scope protected by both mutex. Both mutex are acquired to avoid dead-lock. The mutexes are not swapped. T is Swapable. The locking mechanism capitalizes on the assumption that const methods don't modify their underlying data. The synchronize factory make easier to lock on a scope. With synchronize you get to lock the object in a scoped and to directly access the object inside that scope. A an instance of a class that locks the mutex on construction and unlocks it on destruction and provides implicit conversion to a reference to the protected value.
A an instance of a class that locks the mutex on construction and unlocks it on destruction and provides implicit conversion to a constant reference to the protected value. Queues provide a mechanism for communicating data between components of a system. The existing deque in the standard library is an inherently sequential data structure. Its reference-returning element access operations cannot synchronize access to those elements with other queue operations. So, concurrent pushes and pops on queues require a different interface to the queue structure.
Moreover, concurrency adds a new dimension for performance and semantics. Different queue implementation must trade off uncontended operation cost, contended operation cost, and element order guarantees. Some of these trade-offs will necessarily result in semantics weaker than a serial queue. Concurrent queues are a well know mechanism for communicating data between different threads.
I must be using queue to multithread incorrectly because for this code:. Apparently I misread the link above. Is there a thread-safe queue implementation available that does what I am trying to do? I know this is a common thread organization strategy. As pointed in the comments, STL containers are not thread-safe for read-write operations. I ended up implementing a BlockingQueuewith the suggested fix to pophere:. Creating a Blocking Queue. NET BlockingCollection class. It wraps std::deque to provide concurrent adding and taking of items from multiple threads to a queue.Parallel Processing CPU Bound Tasks with NodeJS
As well as stack and priority containers. Learn more. How to multithread queue processing Ask Question. Asked 6 years, 5 months ago. Active 2 years ago. Viewed 9k times. Anton 5, 1 1 gold badge 17 17 silver badges 48 48 bronze badges. Chris Redford Chris Redford Quoting from the page you linked to: "Container operations that invalidate any iterators modify the container and cannot be executed concurrently with any operations on existing iterators even if those iterators are not invalidated.Unity controller input
They're only threadsafe when you have multiple threads calling const member functions, not if one or more of those threads are modifying the container. That's probably a question of wording: containers' methods are supposedly thread safe, but iterators operating on them are not.
Section 7. There are several other queue examples within the book.
C++ Multithreading in Boost
Active Oldest Votes. Anton Anton 5, 1 1 gold badge 17 17 silver badges 48 48 bronze badges. The other one would be a blockingQueue: stackoverflow. I ended up implementing a BlockingQueuewith the suggested fix to pophere: Creating a Blocking Queue. Sign up or log in Sign up using Google.
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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. I use Boost. Beast WebSockets version 1. I therefore thought that a quick fix would be to guard the call to the async function with a mutex:. But then I get the same problem still. Hmm, does completed in the code's comment really mean that the handler must have been called before I'm allowed to call the async function again!?
Let's try:. But this solution feels like overkill, I could as well use the synchronous API if I have to add a mutex and handle its locking and unlocking manually. Also, I have not seen this convoluted construction before, i. I thought that all of this would be automagically handled by the strand that I'm using, that's the whole purpose of it!
The documentation of strand states:. A strand is defined as a strictly sequential invocation of event handlers i. Use of strands allows execution of code in a multithreaded program without the need for explicit locking e.
So my question is, do I really need both belt and suspenders, or is this maybe a bug in the strand implementation or perhaps in its documentation, because clearly the strand does not allow for the execution of code in my multithreaded program without the need for explicit locking using mutexes!?
However, I think it is more likely that I have missed something fundamental - but what? The following is undefined behavior:. The code above initiates two of the same type of outstanding asynchronous operations simultaneously.
Strands are not intended to solve this problem. Rather, they are intended to solve the problem of completion handlers executing concurrently. This is what is disallowed. Fortunately, solving this problem is trivial. You just need to implement a queue. When you go to send a message, store it in the queue and remove it when the message is done sending.
Every time you send a message, check the queue and see if it has anything in it. If so, then add the new message to the queue but do not send it right away because there is already a message being sent.
Then, every time a message finishes sending, remove it then check the queue if there is another message and send it if so.In the example developed in this article, we shall learn how to add all of the numbers from 0 to 1, 1 US billion exclusive, using multi-threading on a multi-core system to speed up execution and make all the available cores work for us, rather than just one.
Figure 1. Adding all the numbers in a contiguous sequence in a single-threaded application.
You have a long-running, time-consuming calculation or process you wish to execute, but you want to split the execution across multiple threads to increase the execution speed on multi-core processors. The last requirement is a reformulation of the first one: if the threads keep stalling to wait for each other, they are not fully independent, and the speed gain may not be worth it for the added complexity.
Generally, any process that you split into parts must be somewhat re-engineered to work in parallel. The way to do this is highly algorithm-dependent.
Here I have gone for a classic problem of adding a large sequence of numbers, but you can easily re-purpose this to perform any arithmetical operation on any disparate sets of data where partial results can be combined. Figure 2. Adding all the numbers in a contiguous sequence, split across several threads, using aggregated sub-totals. Figure 1 shows the classic approach to adding a sequence of numbers in a single-threaded application.
When the for loop completes, the running total becomes the final total, which is the answer to the calculation. Figure 2 shows how we might split this up across multiple threads. We apportion a block of numbers to add together to each thread. The threads run in parallel and return their own sub-totals.
The main thread then adds these sub-totals to get the grand total. Notice that this actually requires more additions than the single-threaded approach specifically, the sub-totals must be added at the end, so if it is split across 4 threads, there will be 3 more additionsbut these additions are a tiny fraction of the total work and do not translate into any meaningful performance hit.
Figure 3. Adding a new target architecture to your solution in Visual Studio. When people with multi-core processors start dabbling, they often wonder why their new code seems to run so slowly. Be warned! Make sure you do two essential things before speed-testing your algorithms:.
Something like this should do the trick:. Secondly, we can find out how long the single-threaded version takes to execute, which is also very important as we will want to see if our multi-threaded version brings useful performance benefits or not — a poor implementation may be even slower than the single-threaded version!
As we may be working with milliseconds or even microseconds in extreme cases, we are going to need an accurate clock. Hence, the calculation above takes the time a long task started and ended, and works out from those figures how long it took. It looks something like this:. Thread functions cannot return results directly, so instead you must pass a pointer to where you want the result to be stored.
Our initial stab at the function looks like this:.
Creating and Managing Threads
Creating a thread is very simple: you simply create an instance of std::threadpassing in the name of the function to call and an arbitrary length list of arguments to pass to it.This class is used to create a new thread.
The name of the function that the new thread should execute is passed to the constructor of boost::thread. At this point, thread executes concurrently with the main function.Heathers full script pdf
To keep the program from terminating, join is called on the newly created thread. This causes main to wait until thread returns. A particular thread can be accessed using a variable — t in this example — to wait for its termination.
However, the thread will continue to execute even if t goes out of scope and is destroyed. A thread is always bound to a variable of type boost::thread in the beginning, but once created, the thread no longer depends on that variable. There is even a member function called detach that allows a variable of type boost::thread to be decoupled from its corresponding thread.
Anything that can be done inside a function can also be done inside a thread. Ultimately, a thread is no different from a function, except that it is executed concurrently to another function. To slow down the output, every iteration of the loop calls the wait function to stall for one second. By passing an object of type boost::chrono::secondsa period of time is set. Even though Boost.
Doing so will lead to compiler errors. You can pass a user-defined action as a template parameter. The action must be a class with an operator operator that accepts an object of type boost::thread. There is no counterpart in the standard library.
Interruption points are only supported by Boost. Thread and not by the standard library. Calling interrupt on a thread object interrupts the corresponding thread. However, this only happens when the thread reaches an interruption point. Simply calling interrupt does not have an effect if the given thread does not contain an interruption point.Webtoon about idols
Whenever a thread reaches an interruption point it will check whether interrupt has been called. The exception is correctly caught inside the thread even though the catch handler is empty. Because the thread function returns after the handler, the thread terminates as well.
This, in turn, will cause the program to terminate because main was waiting for the thread to terminate. These interruption points make it easy to interrupt threads in a timely manner. In version 1. Calling this function on a dual-core processor returns a value of 2. This function provides a simple method to identify the theoretical maximum number of threads that should be used. Use two threads to calculate the sum of all numbers which are added up in the for -loop:. Generalize the program so that it uses as many threads as can be executed concurrently on a computer.
For example, the program should use four threads if it is run on a computer with a CPU with four cores. Get the book. Your exclusive ad here? Creating and Managing Threads.
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