A need to create huge (>10 Gb) text files where every line is a very long number, basically a string as even types like unsigned long long won't be enough. So i will be using random generator and first though was that probably it's a good idea create several threads. From what I see, every thread will be writing one line at a time, which is considered a thread safe operation in C++.
Is it a good idea or am I missing something and it's better just to write line by line from one thread?
A correct answer here will depend fairly heavily on the type of drive to which you're writing the file.
If it's an actual hard drive, then a single thread will probably be able to generate your random numbers and write the data to the disk as fast as the disk can accept it. With reasonable code on a modern CPU, the core running that code will probably spend more time idle than it does doing actual work.
If it's a SATA hard SSD, effectively the same thing will be true, but (especially if you're using an older CPU) the core running the code will probably spend a lot less time idle. A single thread will probably still be able to generate the data and write it to the drive as fast as the drive can accept it.
Then we get to things like NVMe and Optane drives. Here you honestly might stand a decent chance of improving performance by writing from multiple threads. But (at least in my experience) to do that, you just about have to skip past using iostreams, and instead talk directly to the OS. Under Windows, that would mean opening the file with CreateFile (specifying FILE_FLAG_OVERLAPPED when you do). Windows also has built in support for I/O completion ports (which are really sort of thread pools) to minimize overhead and improve performance--but using it is somewhat nontrivial.
On Linux, asynchronous I/O is a bit more of a sore point. There's an official AIO interface for doing asynchronous I/O, and Linux has had an implementation of that for a long time, but it's never really worked very well. More recently, something called io_uring was added to the Linux kernel. I haven't used it a lot, but it looks like it's probably a better design--but it doesn't (as far as I know) support the standard AIO interface, so you pretty much have to use via its own liburing instead. Rather that Windows I/O completion ports, this works well, but using it is somewhat non-trivial.
If there is no explicit synchronization between the threads, you need to make sure that the library functions you use are thread-safe. For example, the C++ random number generators in <random> are not, so it would be best to have one RNG per thread. Additionally, you need to look at bottlenecks. Conversion of a number to text is one bottleneck, and multithreading would help with that. Output is another, and multithreading would not. Profiling would help resolve this.
Ostreams are not thread-safe, so you'll have to use synchronization to protect each thread's access.
Can some one recommend approaches to parallelize in C++, when the data to be acted up on is huge. I have been reading about openMP and Intel's TBB for parallelization in C++, but have not experimented with them yet. Which of these is better for parallel data processing ? Any other libraries/ approaches ?
"large" and "data processing" cover a lot of ground here, and it's hard to give a sensible answer without more information.
If the data processing is "embarrassingly parallel" -- if it involves doing lots and lots of calculations that are completely independant of each other -- then there's a million things that will work and it's just a matter of finding something that matches your code and background.
If it isn't embarrasingly parallel, but nearly so - the computations take a big chunk of data but just distill it into a handfull of numbers - there's fewer, but still lots of options.
If the calculation is more tightly coupled than this - where you need the processors to work on tandem on big chunks of data then you're probably stuck with the standbys - the OpenMP features of your compiler if it will work on a single machine (there's TBB, too, but usually for number crunching OpenMP is faster and easier) or MPI if it needs several machines simultaneously. You mentioned C++; Boost has a very nice MPI layer.
But thinking about which library to use for parallelization is probably thinking about the wrong end of the problem first. In many cases, you don't necessarily need to deal with these layers directly. If the number crunching involves lots of linear algebra (for instance), then PLASMA (for multicore machines - http://icl.cs.utk.edu/plasma/ ) or PetSC, which has support for distributed memory machines, eg, multiple computers ( http://www.mcs.anl.gov/petsc/petsc-as/ ) are good choices, which can completely hide the actual details of the parallel implementation from you. Other sorts of techniques have other libraries, too. It's probably best to think about what sort of analysis you need to do, and look to see if existing toolkits have the amount of parallization you need. Only once you've determined the answer is no should you start to worry about how to roll your own.
Both OpenMP and Intel TBB are for local use as they help in writing multithreaded applications.
If you have truly huge datasets, you may need to split load over several machines -- and then libraries like Open MPI for parallel programming with MPI come into play. Open MPI has a C++ interface, but you now also face a networking component and some administrative issues you do not have with a single computer.
MPI is also useful on a single local machine. It will run a job across multiple cores/CPUs, while this is probably overkill compared to threading it does mean you can move the job to a cluster with no changes. Most MPI implementations also optimize a local job to use shared memory instead of TCP for data connections.
I do some c++ programming related to mapping software and mathematical modeling.
Some programs take anywhere from one to five hours to perform and output a result; however, they only consume 50% of my core duo. I tried the code on another dual processor based machine with the same result.
Is there a way to force a program to use all available processer resources and memory?
Note: I'm using ubuntu and g++
A thread can only run on one core at a time. If you want to use both cores, you need to find a way to do half the work in another thread.
Whether this is possible, and if so how to divide the work between threads, is completely dependent on the specific work you're doing.
To actually create a new thread, see the Boost.Thread docs, or the pthreads docs, or the Win32 API docs.
[Edit: other people have suggested using libraries to handle the threads for you. The reason I didn't mention these is because I have no experience of them, not because I don't think they're a good idea. They probably are, but it all depends on your algorithm and your platform. Threads are almost universal, but beware that multithreaded programming is often difficult: you create a lot of problems for yourself.]
The quickest method would be to read up about openMP and use it to parallelise your program.
Compile with the command g++ -fopenmp provided that your g++ version is >=4
You need to have as many threads running as there are CPU cores available in order to be able to potentially use all the processor time. (You can still be pre-empted by other tasks, though.)
There are many way to do this, and it depends completely on what you're processing. You may be able to use OpenMP or a library like TBB to do it almost transparently, however.
You're right that you'll need to use a threaded approach to use more than one core. Boost has a threading library, but that's not the whole problem: you also need to change your algorithm to work in a threaded environment.
There are some algorithms that simply cannot run in parallel -- for example, SHA-1 makes a number of "passes" over its data, but they cannot be threaded because each pass relies on the output of the run before it.
In order to parallelize your program, you'll need to be sure your algorithm can "divide and conquer" the problem into independent chunks, which it can then process in parallel before combining them into a full result.
Whatever you do, be very careful to verify the correctness of your answer. Save the single-threaded code, so you can compare its output to that of your multi-threaded code; threading is notoriously hard to do, and full of potential errors.
It may be more worth your time to avoid threading entirely, and try profiling your code instead: you may be able to get dramatic speed improvements by optimizing the most frequently-executed code, without getting near the challenges of threading.
To take full use of a multicore processor, you need to make the program multithreaded.
An alternative to multi-threading is to use more than one process. You would still need to divide & conquer your problem into mutiple independent chunks.
By 50%, do you mean just one core?
If the application isn't either multi-process or multi-threaded, there's no way it can use both cores at once.
Add a while(1) { } somewhere in main()?
Or to echo real advice, either launch multiple processes or rewrite the code to use threads. I'd recommend running multiple processes since that is easier, although if you need to speed up a single run it doesn't really help.
To get to 100% for each thread, you will need to:
(in each thread):
Eliminate all secondary storage I/O
(disk read/writes)
Eliminate all display I/O (screen
writes/prints)
Eliminate all locking mechanisms
(mutexs, semaphores)
Eliminate all Primary storage I/O
(operate strictly out of registers
and cache, not DRAM).
Good luck on your rewrite!
I'm currently working on a wireless networking application in C++ and it's coming to a point where I'm going to want to multi-thread pieces of software under one process, rather than have them all in separate processes. Theoretically, I understand multi-threading, but I've yet to dive in practically.
What should every programmer know when writing multi-threaded code in C++?
I would focus on design the thing as much as partitioned as possible so you have the minimal amount of shared things across threads. If you make sure you don't have statics and other resources shared among threads (other than those that you would be sharing if you designed this with processes instead of threads) you would be fine.
Therefore, while yes, you have to have in mind concepts like locks, semaphores, etc, the best way to tackle this is to try to avoid them.
I am no expert at all in this subject. Just some rule of thumb:
Design for simplicity, bugs really are hard to find in concurrent code even in the simplest examples.
C++ offers you a very elegant paradigm to manage resources(mutex, semaphore,...): RAII. I observed that it is much easier to work with boost::thread than to work with POSIX threads.
Build your code as thread-safe. If you don't do so, your program could behave strangely
I am exactly in this situation: I wrote a library with a global lock (many threads, but only one running at a time in the library) and am refactoring it to support concurrency.
I have read books on the subject but what I learned stands in a few points:
think parallel: imagine a crowd passing through the code. What happens when a method is called while already in action ?
think shared: imagine many people trying to read and alter shared resources at the same time.
design: avoid the problems that points 1 and 2 can raise.
never think you can ignore edge cases, they will bite you hard.
Since you cannot proof-test a concurrent design (because thread execution interleaving is not reproducible), you have to ensure that your design is robust by carefully analyzing the code paths and documenting how the code is supposed to be used.
Once you understand how and where you should bottleneck your code, you can read the documentation on the tools used for this job:
Mutex (exclusive access to a resource)
Scoped Locks (good pattern to lock/unlock a Mutex)
Semaphores (passing information between threads)
ReadWrite Mutex (many readers, exclusive access on write)
Signals (how to 'kill' a thread or send it an interrupt signal, how to catch these)
Parallel design patterns: boss/worker, producer/consumer, etc (see schmidt)
platform specific tools: openMP, C blocks, etc
Good luck ! Concurrency is fun, just take your time...
You should read about locks, mutexes, semaphores and condition variables.
One word of advice, if your app has any form of UI make sure you always change it from the UI thread. Most UI toolkits/frameworks will crash (or behave unexpectedly) if you access them from a background thread. Usually they provide some form of dispatching method to execute some function in the UI thread.
Never assume that external APIs are threadsafe. If it is not explicitly stated in their docs, do not call them concurrently from multiple threads. Instead, limit your use of them to a single thread or use a mutex to prevent concurrent calls (this is rather similar to the aforementioned GUI libraries).
Next point is language-related. Remember, C++ has (currently) no well-defined approach to threading. The compiler/optimizer does not know if code might be called concurrently. The volatile keyword is useful to prevent certain optimizations (i.e. caching of memory fields in CPU registers) in multi-threaded contexts, but it is no synchronization mechanism.
I'd recommend boost for synchronization primitives. Don't mess with platform APIs. They make your code difficult to port because they have similar functionality on all major platforms, but slightly different detail behaviour. Boost solves these problems by exposing only common functionality to the user.
Furthermore, if there's even the smallest chance that a data structure could be written to by two threads at the same time, use a synchronization primitive to protect it. Even if you think it will only happen once in a million years.
One thing I've found very useful is to make the application configurable with regard to the actual number of threads it uses for various tasks. For example, if you have multiple threads accessing a database, make the number of those threads be configurable via a command line parameter. This is extremely handy when debugging - you can exclude threading issues by setting the number to 1, or force them by setting it to a high number. It's also very handy when working out what the optimal number of threads is.
Make sure you test your code in a single-cpu system and a multi-cpu system.
Based on the comments:-
Single socket, single core
Single socket, two cores
Single socket, more than two cores
Two sockets, single core each
Two sockets, combination of single, dual and multi core cpus
Mulitple sockets, combination of single, dual and multi core cpus
The limiting factor here is going to be cost. Ideally, concentrate on the types of system your code is going to run on.
In addition to the other things mentioned, you should learn about asynchronous message queues. They can elegantly solve the problems of data sharing and event handling. This approach works well when you have concurrent state machines that need to communicate with each other.
I'm not aware of any message passing frameworks tailored to work only at the thread level. I've only seen home-brewed solutions. Please comment if you know of any existing ones.
EDIT:
One could use the lock-free queues from Intel's TBB, either as-is, or as the basis for a more general message-passing queue.
Since you are a beginner, start simple. First make it work correctly, then worry about optimizations. I've seen people try to optimize by increasing the concurrency of a particular section of code (often using dubious tricks), without ever looking to see if there was any contention in the first place.
Second, you want to be able to work at as high a level as you can. Don't work at the level of locks and mutexs if you can using an existing master-worker queue. Intel's TBB looks promising, being slightly higher level than pure threads.
Third, multi-threaded programming is hard. Reduce the areas of your code where you have to think about it as much as possible. If you can write a class such that objects of that class are only ever operated on in a single thread, and there is no static data, it greatly reduces the things that you have to worry about in the class.
A few of the answers have touched on this, but I wanted to emphasize one point:
If you can, make sure that as much of your data as possible is only accessible from one thread at a time. Message queues are a very useful construct to use for this.
I haven't had to write much heavily-threaded code in C++, but in general, the producer-consumer pattern can be very helpful in utilizing multiple threads efficiently, while avoiding the race conditions associated with concurrent access.
If you can use someone else's already-debugged code to handle thread interaction, you're in good shape. As a beginner, there is a temptation to do things in an ad-hoc fashion - to use a "volatile" variable to synchronize between two pieces of code, for example. Avoid that as much as possible. It's very difficult to write code that's bulletproof in the presence of contending threads, so find some code you can trust, and minimize your use of the low-level primitives as much as you can.
My top tips for threading newbies:
If you possibly can, use a task-based parallelism library, Intel's TBB being the most obvious one. This insulates you from the grungy, tricky details and is more efficient than anything you'll cobble together yourself. The main downside is this model doesn't support all uses of multithreading; it's great for exploiting multicores for compute power, less good if you wanted threads for waiting on blocking I/O.
Know how to abort threads (or in the case of TBB, how to make tasks complete early when you decide you didn't want the results after all). Newbies seem to be drawn to thread kill functions like moths to a flame. Don't do it... Herb Sutter has a great short article on this.
Make sure to explicitly know what objects are shared and how they are shared.
As much as possible make your functions purely functional. That is they have inputs and outputs and no side effects. This makes it much simpler to reason about your code. With a simpler program it isn't such a big deal but as the complexity rises it will become essential. Side effects are what lead to thread-safety issues.
Plays devil's advocate with your code. Look at some code and think how could I break this with some well timed thread interleaving. At some point this case will happen.
First learn thread-safety. Once you get that nailed down then you move onto the hard part: Concurrent performance. This is where moving away from global locks is essential. Figuring out ways to minimize and remove locks while still maintaining the thread-safety is hard.
Keep things dead simple as much as possible. It's better to have a simpler design (maintenance, less bugs) than a more complex solution that might have slightly better CPU utilization.
Avoid sharing state between threads as much as possible, this reduces the number of places that must use synchronization.
Avoid false-sharing at all costs (google this term).
Use a thread pool so you're not frequently creating/destroying threads (that's expensive and slow).
Consider using OpenMP, Intel and Microsoft (possibly others) support this extension to C++.
If you are doing number crunching, consider using Intel IPP, which internally uses optimized SIMD functions (this isn't really multi-threading, but is parallelism of a related sorts).
Have tons of fun.
Stay away from MFC and it's multithreading + messaging library.
In fact if you see MFC and threads coming toward you - run for the hills (*)
(*) Unless of course if MFC is coming FROM the hills - in which case run AWAY from the hills.
The biggest "mindset" difference between single-threaded and multi-threaded programming in my opinion is in testing/verification. In single-threaded programming, people will often bash out some half-thought-out code, run it, and if it seems to work, they'll call it good, and often get away with it using it in a production environment.
In multithreaded programming, on the other hand, the program's behavior is non-deterministic, because the exact combination of timing of which threads are running for which periods of time (relative to each other) will be different every time the program runs. So just running a multithreaded program a few times (or even a few million times) and saying "it didn't crash for me, ship it!" is entirely inadequate.
Instead, when doing a multithreaded program, you always should be trying to prove (at least to your own satisfaction) that not only does the program work, but that there is no way it could possibly not work. This is much harder, because instead of verifying a single code-path, you are effectively trying to verify a near-infinite number of possible code-paths.
The only realistic way to do that without having your brain explode is to keep things as bone-headedly simple as you can possibly make them. If you can avoid using multithreading totally, do that. If you must do multithreading, share as little data between threads as possible, and use proper multithreading primitives (e.g. mutexes, thread-safe message queues, wait conditions) and don't try to get away with half-measures (e.g. trying to synchronize access to a shared piece of data using only boolean flags will never work reliably, so don't try it)
What you want to avoid is the multithreading hell scenario: the multithreaded program that runs happily for weeks on end on your test machine, but crashes randomly, about once a year, at the customer's site. That kind of race-condition bug can be nearly impossible to reproduce, and the only way to avoid it is to design your code extremely carefully to guarantee it can't happen.
Threads are strong juju. Use them sparingly.
You should have an understanding of basic systems programing, in particular:
Synchronous vs Asynchronous I/O (blocking vs. non-blocking)
Synchronization mechanisms, such as lock and mutex constructs
Thread management on your target platform
I found viewing the introductory lectures on OS and systems programming here by John Kubiatowicz at Berkeley useful.
Part of my graduate study area relates to parallelism.
I read this book and found it a good summary of approaches at the design level.
At the basic technical level, you have 2 basic options: threads or message passing. Threaded applications are the easiest to get off the ground, since pthreads, windows threads or boost threads are ready to go. However, it brings with it the complexity of shared memory.
Message-passing usability seems mostly limited at this point to the MPI API. It sets up an environment where you can run jobs and partition your program between processors. It's more for supercomputer/cluster environments where there's no intrinsic shared memory. You can achieve similar results with sockets and so forth.
At another level, you can use language type pragmas: the popular one today is OpenMP. I've not used it, but it appears to build threads in via preprocessing or a link-time library.
The classic problem is synchronization here; all the problems in multiprogramming come from the non-deterministic nature of multiprograms, which can not be avoided.
See the Lamport timing methods for a further discussion of synchronizations and timing.
Multithreading is not something that only Ph.D.`s and gurus can do, but you will have to be pretty decent to do it without making insane bugs.
I'm in the same boat as you, I am just starting multi threading for the first time as part of a project and I've been looking around the net for resources. I found this blog to be very informative. Part 1 is pthreads, but I linked starting on the boost section.
I have written a multithreaded server application and a multithreaded shellsort. They were both written in C and use NT's threading functions "raw" that is without any function library in-between to muddle things. They were two quite different experiences with different conclusions to be drawn. High performance and high reliability were the main priorities although coding practices had a higher priority if one of the first two was judged to be threatened in the long term.
The server application had both a server and a client part and used iocps to manage requests and responses. When using iocps it is important never to use more threads than you have cores. Also I found that requests to the server part needed a higher priority so as not to lose any requests unnecessarily. Once they were "safe" I could use lower priority threads to create the server responses. I judged that the client part could have an even lower priority. I asked the questions "what data can't I lose?" and "what data can I allow to fail because I can always retry?" I also needed to be able to interface to the application's settings through a window and it had to be responsive. The trick was that the UI had normal priority, the incoming requests one less and so on. My reasoning behind this was that since I will use the UI so seldom it can have the highest priority so that when I use it it will respond immediately. Threading here turned out to mean that all separate parts of the program in the normal case would/could be running simultaneously but when the system was under higher load, processing power would be shifted to the vital parts due to the prioritization scheme.
I've always liked shellsort so please spare me from pointers about quicksort this or that or blablabla. Or about how shellsort is ill-suited for multithreading. Having said that, the problem I had had to do with sorting a semi-largelist of units in memory (for my tests I used a reverse-sorted list of one million units of forty bytes each. Using a single-threaded shellsort I could sort them at a rate of roughly one unit every two us (microseconds). My first attempt to multithread was with two threads (though I soon realized that I wanted to be able to specify the number of threads) and it ran at about one unit every 3.5 seconds, that is to say SLOWER. Using a profiler helped a lot and one bottleneck turned out to be the statistics logging (i e compares and swaps) where the threads would bump into each other. Dividing up the data between the threads in an efficient way turned out to be the biggest challenge and there is definitley more I can do there such as dividing the vector containing the indeces to the units in cache-line size adapted chunks and perhaps also comparing all indeces in two cache lines before moving to the next line (at least I think there is something I can do there - the algorithms get pretty complicated). In the end, I achieved a rate of one unit every microsecond with three simultaneous threads (four threads about the same, I only had four cores available).
As to the original question my advice to you would be
If you have the time, learn the threading mechanism at the lowest possible level.
If performance is important learn the related mechanisms that the OS provides. Multi-threading by itself is seldom enough to achieve an application's full potential.
Use profiling to understand the quirks of multiple threads working on the same memory.
Sloppy architectural work will kill any app, regardless of how many cores and systems you have executing it and regardless of the brilliance of your programmers.
Sloppy programming will kill any app, regardless of the brilliance of the architectural foundation.
Understand that using libraries lets you reach the development goal faster but at the price of less understanding and (usually) lower performance .
Before giving any advice on do's and dont's about multi-thread programming in C++, I would like to ask the question Is there any particular reason you want to start writing the application in C++?
There are other programming paradigms where you utilize the multi-cores without getting into multi-threaded programming. One such paradigm is functional programming. Write each piece of your code as functions without any side effects. Then it is easy to run it in multiple thread without worrying about synchronization.
I am using Erlang for my development purpose. It has increased by productivity by at least 50%. Code running may not be as fast as the code written in C++. But I have noticed that for most of the back-end offline data processing, speed is not as important as distribution of work and utilizing the hardware as much as possible. Erlang provides a simple concurrency model where you can execute a single function in multiple-threads without worrying about the synchronization issue. Writing multi-threaded code is easy, but debugging that is time consuming. I have done multi-threaded programming in C++, but I am currently happy with Erlang concurrency model. It is worth looking into.
Make sure you know what volatile means and it's uses(which may not be obvious at first).
Also, when designing multithreaded code, it helps to imagine that an infinite amount of processors is executing every single line of code in your application at once. (er, every single line of code that is possible according to your logic in your code.) And that everything that isn't marked volatile the compiler does a special optimization on it so that only the thread that changed it can read/set it's true value and all the other threads get garbage.