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template: titleslide

C++ Threads – Further topics

Overview

  • std::async and futures
  • Parallel STL
  • C++ threads or OpenMP?

async and futures

  • With std::thread there is no direct way to get a return value from the function/lambda that a thread executes

  • Could alter function to use an output reference argument, but this is problematic

  • Can get round this with std::async and futures

  • Also allows exception handling to work properly if a thread throws an exception

  • async returns a future object, and calling the get() function on the future blocks until the value is available

async and future

int add(int x, int y) {
	 return x+y;
}

int main() {
  int a = 1;
  int b = 27;
  std::future<int> fut = std::async(add, a, b);
  cout << "sum  = " << fut.get() << std::endl;
}

async and future

int add(int x, int y) {
	 return x+y;
}

int main() {
  int a = 1;
  int b = 27;
  auto fut = std::async(add, a, b);
  cout << "sum  = " << fut.get() << std::endl;
}

Problems!

  • By default, the runtime can decide to not create a new thread to run the callable, and simply execute it when fut.get() is called.

  • Can cause deadlock if you are not careful!

  • Can force asynchronous execution via an optional policy argument:

auto fut = std::async(std::launch::async, add, a, b);
  • Implementations can choose to use a thread pool to execute asyncs, but most don’t can end up with way too many threads

Parallel STL

  • Version of the STL that incorporates parallel versions of many STL algorithms

  • Part of C++17 standard, so not yet available in most standard library implementations, but coming soon.

  • Most STL algorithms take a addition execution policy argument:

    • Sequential
    • Parallel
    • Parallel and vectorized
    • (vectorized but not parallel added in C++20)

Example

std::vector<int> v = genLargeVector(); 

// standard sequential sort 
std::sort(v.begin(), v.end()); 

// explicitly sequential 
sort std::sort(std::execution::seq, v.begin(), v.end()); 

// permitting parallel execution 
std::sort(std::execution::par, v.begin(), v.end()); 

// permitting vectorization as well 
std::sort(std::execution::par_unseq, v.begin(), v.end());

Easy to use, but...

  • For algorithms that take a function object argument (e.g. for_each), that operates on each element it is up to the programmer to ensure that parallel/vectorized execution of this is correct.

  • Interface gives almost no control to the programmer

    • how many threads to use?
    • which threads handle which elements?
    • whether to use a static of dynamic assignment of work to threads?

  • Might be difficult to have any control over data affinity/locality

  • Implementations can support additional execution policies

  • Scope for adding more to the standard in the future

C++ threads or OpenMP?

  • Since OpenMP supports C++ as a base language, what are the pros and cons of OpenMP vs C++ threads?

  • Adding OpenMP support for new C++ features takes time (several years in practice)

  • OpenMP 4.5 only supports C++98

  • OpenMP 5.0 supports most of C++11/14

    • some exceptions – including C++ threads and atomics
    • implementations will take some time to catch up with this

  • If you want to use the latest and greatest features in C++, then OpenMP might not work.

C++ threads or OpenMP?

OpenMP has a lot of useful features that are not available in the C++ standard:

  • Thread barriers

    • will likely be added in C++20

  • Thread pools and tasks

    • may be added in C++23 (depends on "executors")

  • Parallel for loops

    • some functionality with std::for_each in Parallel STL

  • SIMD directives

    • probably will work OK with C++ threads

But I can build my own!

  • C++ threads has all the required building blocks to allow you to implement many of these features for yourself

    • or borrow someone else’s implementation

  • Can make use of C++ functionality to make them look syntactically neat

  • May not be correct

    • low level threaded programming is hard!

  • May not be well documented

    • unusual to find documentation that is as good as language standards

  • Unlikely to be both portable and efficient maximum efficiency typically requires architecture-specific coding

What about GPUs?

  • OpenMP has support for offloading code to GPUs

    • still a bit immature, not many good implementations yet

  • C++ may support this in the future sometime??

  • There are a number of C++ frameworks for doing this

    • Kokkos
    • Raja
    • SYCL

  • Various degrees of support and robustness

More differences

  • OpenMP has some restrictions which annoy C++ programmers

    • e.g. can’t use data members of objects in private or reduction clauses

  • Support for reductions is in both, but handled differently

    • OpenMP allows you to define your own reduction operators
    • C++ has std::reduce in the Parallel STL

  • OpenMP does not allow arbitrary forking/joining of threads

    • You can argue whether this is a good thing or not in an HPC context!

  • C++ has no standard way of binding threads to sockets/cores

    • need to call OS-specific functions

C++ threads or OpenMP?

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