Quick start

This section is intended to get you to the point of running a basic HPX program as quickly as possible. To that end we skip many details but instead give you hints and links to more details along the way.

We assume that you are on a Unix system with access to reasonably recent packages. You should have cmake and make available for the build system (pkg-config is also supported, see Using HPX with pkg-config).

Getting HPX

Download a tarball of the latest release from HPX Downloads and unpack it or clone the repository directly using git:

git clone https://github.com/STEllAR-GROUP/hpx.git

It is also recommended that you check out the latest stable tag:

git checkout 1.3.0

HPX dependencies

The minimum dependencies needed to use HPX are Boost and Portable Hardware Locality (HWLOC). If these are not available through your system package manager, see Installing Boost and Installing Hwloc for instructions on how to build them yourself. In addition to Boost and Portable Hardware Locality (HWLOC), it is recommended that you don’t use the system allocator, but instead use either tcmalloc from google-perftools (default) or jemalloc for better performance. If you would like to try HPX without a custom allocator at this point you can configure HPX to use the system allocator in the next step.

A full list of required and optional dependencies, including recommended versions is available at Prerequisites.

Building HPX

Once you have the source code and the dependencies, set up a separate build directory and configure the project. Assuming all your dependencies are in paths known to CMake, the following gets you started:

# In the HPX source directory
mkdir build && cd build
cmake -DCMAKE_INSTALL_PREFIX=/install/path ..
make install

This will build the core HPX libraries and examples, and install them to your chosen location. If you want to install HPX to system folders simply leave out the CMAKE_INSTALL_PREFIX option. This may take a while. To speed up the process launch more jobs by passing the -jN option to make.

Tip

Do not set only -j (i.e. -j without an explicit number of jobs) unless you have a lot of memory available on your machine.

Tip

If you want to change CMake variables for your build it is usually a good idea to start with a clean build directory to avoid configuration problems. It is especially important that you use a clean build directory when changing between Release and Debug modes.

If your dependencies are in custom locations you may need to tell CMake where to find them by passing one or more of the following options to CMake:

-DBOOST_ROOT=/path/to/boost
-DHWLOC_ROOT=/path/to/hwloc
-DTCMALLOC_ROOT=/path/to/tcmalloc
-DJEMALLOC_ROOT=/path/to/jemalloc

If you want to try HPX without using a custom allocator pass -DHPX_WITH_MALLOC=system to CMake.

Important

If you are building HPX for a system with more than 64 processing units you must change the CMake variables HPX_WITH_MORE_THAN_64_THREADS (to On) and HPX_WITH_MAX_CPU_COUNT (to a value at least as big as the number of (virtual) cores on your system).

To build the tests run make tests. To run the tests run either make test or use ctest for more control over which tests to run. You can run single tests for example with ctest --output-on-failure -R tests.unit.parallel.algorithms.for_loop or a whole group of tests with ctest --output-on-failure -R tests.unit.

If you did not run make install earlier do so now or build the hello_world_1 example by running:

make hello_world_1

HPX executables end up in the bin directory in your build directory. You can now run hello_world_1 and should see the following output:

./bin/hello_world_1
Hello World!

You’ve just run an example which prints Hello World! from the HPX runtime. The source for the example is in examples/quickstart/hello_world_1.cpp. The hello_world_distributed example (also available in the examples/quickstart directory) is a distributed hello world program which is described in Remote execution with actions: Hello world. It provides a gentle introduction to the distributed aspects of HPX.

Tip

Most build targets in HPX have two names: a simple name and a hierarchical name corresponding to what type of example or test the target is. If you are developing HPX it is often helpful to run make help to get a list of available targets. For example, make help | grep hello_world outputs the following:

... examples.quickstart.hello_world_2
... hello_world_2
... examples.quickstart.hello_world_1
... hello_world_1
... examples.quickstart.hello_world_distributed
... hello_world_distributed

It is also possible to build e.g. all quickstart examples using make examples.quickstart.

Hello, World!

The following CMakeLists.txt is a minimal example of what you need in order to build an executable using CMake and HPX:

cmake_minimum_required(VERSION 3.3.2)
project(my_hpx_project CXX)
find_package(HPX REQUIRED)
add_hpx_executable(my_hpx_program
    SOURCES main.cpp
    COMPONENT_DEPENDENCIES iostreams)

Note

You will most likely have more than one main.cpp file in your project. See the section on Using HPX with CMake-based projects for more details on how to use add_hpx_executable.

Note

COMPONENT_DEPENDENCIES iostreams is optional for a minimal project but lets us use the HPX equivalent of std::cout, i.e. the HPX The HPX I/O-streams component functionality in our application.

Create a new project directory and a CMakeLists.txt with the contents above. Also create a main.cpp with the contents below.

// Including 'hpx/hpx_main.hpp' instead of the usual 'hpx/hpx_init.hpp' enables
// to use the plain C-main below as the direct main HPX entry point.
#include <hpx/hpx_main.hpp>
#include <hpx/include/iostreams.hpp>

int main()
{
    // Say hello to the world!
    hpx::cout << "Hello World!\n" << hpx::flush;
    return 0;
}

Then, in your project directory run the following:

mkdir build && cd build
cmake -DCMAKE_PREFIX_PATH=/path/to/hpx/installation ..
make all
./my_hpx_program

The program looks almost like a regular C++ hello world with the exception of the two includes and hpx::cout. When you include hpx_main.hpp some things will be done behind the scenes to make sure that main actually gets launched on the HPX runtime. So while it looks almost the same you can now use futures, async, parallel algorithms and more which make use of the HPX runtime with lightweight threads. hpx::cout is a replacement for std::cout to make sure printing never blocks a lightweight thread. You can read more about hpx::cout in The HPX I/O-streams component. If you rebuild and run your program now you should see the familiar Hello World!:

./my_hpx_program
Hello World!

Note

You do not have to let HPX take over your main function like in the example. You can instead keep your normal main function, and define a separate hpx_main function which acts as the entry point to the HPX runtime. In that case you start the HPX runtime explicitly by calling hpx::init:

//  Copyright (c) 2007-2012 Hartmut Kaiser
//
//  Distributed under the Boost Software License, Version 1.0. (See accompanying
//  file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)

///////////////////////////////////////////////////////////////////////////////
// The purpose of this example is to initialize the HPX runtime explicitly and
// execute a HPX-thread printing "Hello World!" once. That's all.

//[hello_world_2_getting_started
#include <hpx/hpx_init.hpp>
#include <hpx/include/iostreams.hpp>

int hpx_main(int, char**)
{
    // Say hello to the world!
    hpx::cout << "Hello World!\n" << hpx::flush;
    return hpx::finalize();
}

int main(int argc, char* argv[])
{
    return hpx::init(argc, argv);
}
//]

You can also use hpx::start and hpx::stop for a non-blocking alternative, or use hpx::resume and hpx::suspend if you need to combine HPX with other runtimes.

See Starting the HPX runtime for more details on how to initialize and run the HPX runtime.

Caution

When including hpx_main.hpp the user-defined main gets renamed and the real main function is defined by HPX. This means that the user-defined main must include a return statement, unlike the real main. If you do not include the return statement you may end up with confusing compile time errors mentioning user_main or even runtime errors.

Writing task-based applications

So far we haven’t done anything that can’t be done using the C++ standard library. In this section we will give a short overview of what you can do with HPX on a single node. The essence is to avoid global synchronization and break up your application into small, composable tasks whose dependencies control the flow of your application. Remember, however, that HPX allows you to write distributed applications similarly to how you would write applications for a single node (see Why HPX? and Writing distributed HPX applications).

If you are already familiar with async and futures from the C++ standard library, the same functionality is available in HPX.

The following terminology is essential when talking about task-based C++ programs:

  • lightweight thread: Essential for good performance with task-based programs. Lightweight refers to smaller stacks and faster context switching compared to OS-threads. Smaller overheads allow the program to be broken up into smaller tasks, which in turns helps the runtime fully utilize all processing units.

  • async: The most basic way of launching tasks asynchronously. Returns a future<T>.

  • future<T>: Represents a value of type T that will be ready in the future. The value can be retrieved with get (blocking) and one can check if the value is ready with is_ready (non-blocking).

  • shared_future<T>: Same as future<T> but can be copied (similar to std::unique_ptr vs std::shared_ptr).

  • continuation: A function that is to be run after a previous task has run (represented by a future). then is a method of future<T> that takes a function to run next. Used to build up dataflow DAGs (directed acyclic graphs). shared_futures help you split up nodes in the DAG and functions like when_all help you join nodes in the DAG.

The following example is a collection of the most commonly used functionality in HPX:

#include <hpx/hpx_main.hpp>
#include <hpx/include/iostreams.hpp>
#include <hpx/include/lcos.hpp>
#include <hpx/include/parallel_generate.hpp>
#include <hpx/include/parallel_sort.hpp>

#include <random>
#include <vector>

void final_task(hpx::future<hpx::util::tuple<hpx::future<double>, hpx::future<void>>>)
{
    hpx::cout << "in final_task" << hpx::endl;
}

// Avoid ABI incompatibilities between C++11/C++17 as std::rand has exception
// specification in libstdc++.
int rand_wrapper()
{
    return std::rand();
}

int main(int, char**)
{
    // A function can be launched asynchronously. The program will not block
    // here until the result is available.
    hpx::future<int> f = hpx::async([]() { return 42; });
    hpx::cout << "Just launched a task!" << hpx::endl;

    // Use get to retrieve the value from the future. This will block this task
    // until the future is ready, but the HPX runtime will schedule other tasks
    // if there are tasks available.
    hpx::cout << "f contains " << f.get() << hpx::endl;

    // Let's launch another task.
    hpx::future<double> g = hpx::async([]() { return 3.14; });

    // Tasks can be chained using the then method. The continuation takes the
    // future as an argument.
    hpx::future<double> result = g.then([](hpx::future<double>&& gg)
        {
            // This function will be called once g is ready. gg is g moved
            // into the continuation.
            return gg.get() * 42.0 * 42.0;
        });

    // You can check if a future is ready with the is_ready method.
    hpx::cout << "Result is ready? " << result.is_ready() << hpx::endl;

    // You can launch other work in the meantime. Let's sort a vector.
    std::vector<int> v(1000000);

    // We fill the vector synchronously and sequentially.
    hpx::parallel::generate(hpx::parallel::execution::seq,
                  std::begin(v), std::end(v), &rand_wrapper);

    // We can launch the sort in parallel and asynchronously.
    hpx::future<void> done_sorting =
        hpx::parallel::sort(
            hpx::parallel::execution::par( // In parallel.
                hpx::parallel::execution::task), // Asynchronously.
            std::begin(v),
            std::end(v));

    // We launch the final task when the vector has been sorted and result is
    // ready using when_all.
    auto all = hpx::when_all(result, done_sorting).then(&final_task);

    // We can wait for all to be ready.
    all.wait();

    // all must be ready at this point because we waited for it to be ready.
    hpx::cout <<
        (all.is_ready() ? "all is ready!" : "all is not ready...") << hpx::endl;

    return hpx::finalize();
}

Try copying the contents to your main.cpp file and look at the output. It can be a good idea to go through the program step by step with a debugger. You can also try changing the types or adding new arguments to functions to make sure you can get the types to match. The type of the then method can be especially tricky to get right (the continuation needs to take the future as an argument).

Note

HPX programs accept command line arguments. The most important one is --hpx:threads=N to set the number of OS-threads used by HPX. HPX uses one thread per core by default. Play around with the example above and see what difference the number of threads makes on the sort function. See Launching and configuring HPX applications for more details on how and what options you can pass to HPX.

Tip

The example above used the construction hpx::when_all(...).then(...). For convenience and performance it is a good idea to replace uses of hpx::when_all(...).then(...) with dataflow. See Dataflow: Interest calculator for more details on dataflow.

Tip

If possible, prefer to use the provided parallel algorithms instead of writing your own implementation. This can save you time and the resulting program is often faster.

Next steps

If you haven’t done so already, reading the Terminology section will help you get familiar with the terms used in HPX.

The Examples section contains small, self-contained walkthroughs of example HPX programs. The Local to remote: 1D stencil example is a thorough, realistic example starting from a single node implementation and going stepwise to a distributed implementation.

The Manual contains detailed information on writing, building and running HPX applications.