HPX

Language: CPP

Concurrency/Parallelism

HPX was developed as part of the ParalleX execution model, a research initiative to overcome the limitations of traditional parallel programming approaches. It is designed as a modern C++ runtime that unifies local and distributed computing under a single asynchronous API. HPX has gained adoption in scientific computing, high-performance computing (HPC), and research environments due to its ability to scale efficiently on thousands of nodes.

HPX (High Performance ParalleX) is a C++ runtime system for parallel and distributed applications. It provides a standards-conforming API for asynchronous task-based programming, scalability across many cores, and distributed computing with an emphasis on performance portability.

Installation

linux: sudo apt install libhpx-dev

mac: brew install hpx

windows: vcpkg install hpx

Usage

HPX offers C++ standard-like parallel algorithms (`for_each`, `reduce`, etc.), futures, channels, distributed objects, and executors. It integrates deeply with modern C++ (C++11 and beyond) and emphasizes asynchronous execution using futures and continuations.

Hello World with HPX

#include <hpx/hpx_main.hpp>
#include <hpx/include/iostreams.hpp>

int main() {
    hpx::cout << "Hello, HPX!" << hpx::endl;
    return 0;
}

A minimal HPX program that runs with the HPX runtime system initialized.

Parallel for loop

#include <hpx/hpx_main.hpp>
#include <hpx/include/parallel_for_each.hpp>
#include <vector>
#include <iostream>

int main() {
    std::vector<int> data = {1, 2, 3, 4, 5};
    hpx::for_each(hpx::execution::par, data.begin(), data.end(), [](int& n) {
        n *= 2;
    });
    for (int n : data) std::cout << n << " ";
    return 0;
}

Doubles all elements in a vector in parallel using HPX's parallel algorithm.

Asynchronous tasks with futures

#include <hpx/hpx_main.hpp>
#include <hpx/include/lcos.hpp>
#include <iostream>

int main() {
    auto f = hpx::async([]() { return 42; });
    std::cout << "The answer is " << f.get() << std::endl;
}

Uses `hpx::async` to launch an asynchronous task returning a future.

Continuations with then()

auto f = hpx::async([] { return 10; });
auto g = f.then([](hpx::future<int> f) {
    return f.get() * 2;
});
std::cout << g.get() << std::endl;

Chains tasks together with continuations using `then()`.

Distributed computing

#include <hpx/include/components.hpp>
// Example: distributed object can be registered and called across nodes.

HPX allows distributing work across multiple compute nodes transparently.

Channels (message passing)

#include <hpx/include/lcos.hpp>

hpx::lcos::channel<int> ch;
ch.set(5);
int val = ch.get();

Implements message passing between tasks with channels.

Error Handling

Deadlocks when using futures: Avoid blocking `get()` calls inside parallel tasks; use continuations instead.

Excessive task overhead: Batch small tasks into larger chunks to reduce scheduling overhead.

Distributed runtime initialization failure: Ensure proper MPI or networking support is enabled in HPX build configuration.

Best Practices

Use HPX’s standard-conforming algorithms (`hpx::for_each`, `hpx::reduce`) for seamless parallelism.

Prefer asynchronous APIs with futures to maximize overlap of computation and communication.

Leverage continuations (`then`) to chain dependent tasks efficiently.

Use distributed objects when scaling across multiple nodes in a cluster.

Integrate HPX with existing C++17/20 parallel STL for portability.

Official Docs Github