markdown-body entry-content container-lg" itemprop="text">

⚠️ The Thrust repository has been archived and is now part of the unified nvidia/cccl repository. See the announcement here for more information. Please visit the new repository for the latest updates. ⚠️

Thrust: The C++ Parallel Algorithms Library

Examples	Godbolt	Documentation

Thrust is the C++ parallel algorithms library which inspired the introduction of parallel algorithms to the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. It builds on top of established parallel programming frameworks (such as CUDA, TBB, and OpenMP). It also provides a number of general-purpose facilities similar to those found in the C++ Standard Library.

The NVIDIA C++ Standard Library is an open source project; it is available on GitHub and included in the NVIDIA HPC SDK and CUDA Toolkit. If you have one of those SDKs installed, no additional installation or compiler flags are needed to use libcu++.

Examples

Thrust is best learned through examples.

The following example generates random numbers serially and then transfers them to a parallel device where they are sorted.

#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/generate.h>
#include <thrust/sort.h>
#include <thrust/copy.h>
#include <thrust/random.h>

int main() {
  // Generate 32M random numbers serially.
  thrust::default_random_engine rng(1337);
  thrust::uniform_int_distribution<int> dist;
  thrust::host_vector<int> h_vec(32 << 20);
  thrust::generate(h_vec.begin(), h_vec.end(), [&] { return dist(rng); });

  // Transfer data to the device.
  thrust::device_vector<int> d_vec = h_vec;

  // Sort data on the device.
  thrust::sort(d_vec.begin(), d_vec.end());

  // Transfer data back to host.
  thrust::copy(d_vec.begin(), d_vec.end(), h_vec.begin());
}

See it on Godbolt

This example demonstrates computing the sum of some random numbers in parallel:

#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/generate.h>
#include <thrust/reduce.h>
#include <thrust/functional.h>
#include <thrust/random.h>

int main() {
  // Generate random data serially.
  thrust::default_random_engine rng(1337);
  thrust::uniform_real_distribution<double> dist(-50.0, 50.0);
  thrust::host_vector<double> h_vec(32 << 20);
  thrust::generate(h_vec.begin(), h_vec.end(), [&] { return dist(rng); });

  // Transfer to device and compute the sum.
  thrust::device_vector<double> d_vec = h_vec;
  double x = thrust::reduce(d_vec.begin(), d_vec.end(), 0, thrust::plus<int>());
}

See it on Godbolt

This example show how to perform such a reduction asynchronously:

#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/generate.h>
#include <thrust/async/copy.h>
#include <thrust/async/reduce.h>
#include <thrust/functional.h>
#include <thrust/random.h>
#include <numeric>

int main() {
  // Generate 32M random numbers serially.
  thrust::default_random_engine rng(123456);
  thrust::uniform_real_distribution<double> dist(-50.0, 50.0);
  thrust::host_vector<double> h_vec(32 << 20);
  thrust::generate(h_vec.begin(), h_vec.end(), [&] { return dist(rng); });

  // Asynchronously transfer to the device.
  thrust::device_vector<double> d_vec(h_vec.size());
  thrust::device_event e = thrust::async::copy(h_vec.begin(), h_vec.end(),
                                               d_vec.begin());

  // After the transfer completes, asynchronously compute the sum on the device.
  thrust::device_future<double> f0 = thrust::async::reduce(thrust::device.after(e),
                                                           d_vec.begin(), d_vec.end(),
                                                           0.0, thrust::plus<double>());

  // While the sum is being computed on the device, compute the sum serially on
  // the host.
  double f1 = std::accumulate(h_vec.begin(), h_vec.end(), 0.0, thrust::plus<double>());
}

See it on Godbolt

Getting The Thrust Source Code

Thrust is a header-only library; there is no need to build or install the project unless you want to run the Thrust unit tests.

The CUDA Toolkit provides a recent release of the Thrust source code in include/thrust. This will be suitable for most users.

Users that wish to contribute to Thrust or try out newer features should recursively clone the Thrust Github repository:

git clone --recursive https://github.com/NVIDIA/thrust.git

Using Thrust From Your Project

For CMake-based projects, we provide a CMake package for use with find_package. See the CMake README for more information. Thrust can also be added via add_subdirectory or tools like the CMake Package Manager.

For non-CMake projects, compile with:

The Thrust include path (-I<thrust repo root>)
The libcu++ include path (-I<thrust repo root>/dependencies/libcudacxx/)
The CUB include path, if using the CUDA device system (-I<thrust repo root>/dependencies/cub/)
By default, the CPP host system and CUDA device system are used. These can be changed using compiler definitions:
- -DTHRUST_HOST_SYSTEM=THRUST_HOST_SYSTEM_XXX, where XXX is CPP (serial, default), OMP (OpenMP), or TBB (Intel TBB)
- -DTHRUST_DEVICE_SYSTEM=THRUST_DEVICE_SYSTEM_XXX, where XXX is CPP, OMP, TBB, or CUDA (default).

Developing Thrust

Thrust uses the CMake build system to build unit tests, examples, and header tests. To build Thrust as a developer, it is recommended that you use our containerized development system:

# Clone Thrust and CUB repos recursively:
git clone --recursive https://github.com/NVIDIA/thrust.git
cd thrust

# Build and run tests and examples:
ci/local/build.bash

That does the equivalent of the following, but in a clean containerized environment which has all dependencies installed:

# Clone Thrust and CUB repos recursively:
git clone --recursive https://github.com/NVIDIA/thrust.git
cd thrust

# Create build directory:
mkdir build
cd build

# Configure -- use one of the following:
cmake ..   # Command line interface.
ccmake ..  # ncurses GUI (Linux only).
cmake-gui  # Graphical UI, set source/build directories in the app.

# Build:
cmake --build . -j ${NUM_JOBS} # Invokes make (or ninja, etc).

# Run tests and examples:
ctest

By default, a serial CPP host system, CUDA accelerated device system, and C++14 standard are used. This can be changed in CMake and via flags to ci/local/build.bash

More information on configuring your Thrust build and creating a pull request can be found in the contributing section.

Licensing

Thrust is an open source project developed on GitHub. Thrust is distributed under the Apache License v2.0 with LLVM Exceptions; some parts are distributed under the Apache License v2.0 and the Boost License v1.0.

Thrust: The C++ Parallel Algorithms Library

Examples

Getting The Thrust Source Code

Using Thrust From Your Project

Developing Thrust

Licensing

CI Status

License

NVIDIA/thrust

ErrorLooks like something went wrong!

About

Topics

Resources

License

Code of conduct

Uh oh!

Stars

Watchers

Forks

Releases 49

Packages 0

Uh oh!

Contributors 81

Languages

Error
Looks like something went wrong!

Packages