Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/NVIDIA/thrust

[ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
https://github.com/NVIDIA/thrust

algorithms cpp cpp11 cpp14 cpp17 cpp20 cuda cxx cxx11 cxx14 cxx17 cxx20 gpu gpu-computing nvidia nvidia-hpc-sdk thrust

Last synced: about 1 month ago
JSON representation

[ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl

Lists

README 

        
:warning: **The Thrust repository has been archived and is now part of the unified [nvidia/cccl repository](https://github.com/nvidia/cccl). See the [announcement here](https://github.com/NVIDIA/cccl/discussions/520) for more information. Please visit the new repository for the latest updates.** :warning:



# 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.



```cuda

#include 

#include 

#include 

#include 

#include 

#include 



int main() {

  // Generate 32M random numbers serially.

  thrust::default_random_engine rng(1337);

  thrust::uniform_int_distribution dist;

  thrust::host_vector h_vec(32 << 20);

  thrust::generate(h_vec.begin(), h_vec.end(), [&] { return dist(rng); });



  // Transfer data to the device.

  thrust::device_vector 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](https://godbolt.org/z/GeWEd8Er9)



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



```cuda

#include 

#include 

#include 

#include 

#include 

#include 



int main() {

  // Generate random data serially.

  thrust::default_random_engine rng(1337);

  thrust::uniform_real_distribution dist(-50.0, 50.0);

  thrust::host_vector 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 d_vec = h_vec;

  double x = thrust::reduce(d_vec.begin(), d_vec.end(), 0, thrust::plus());

}

```



[See it on Godbolt](https://godbolt.org/z/cnsbWWME7)



This example show how to perform such a reduction asynchronously:



```cuda

#include 

#include 

#include 

#include 

#include 

#include 

#include 

#include 



int main() {

  // Generate 32M random numbers serially.

  thrust::default_random_engine rng(123456);

  thrust::uniform_real_distribution dist(-50.0, 50.0);

  thrust::host_vector h_vec(32 << 20);

  thrust::generate(h_vec.begin(), h_vec.end(), [&] { return dist(rng); });



  // Asynchronously transfer to the device.

  thrust::device_vector 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 f0 = thrust::async::reduce(thrust::device.after(e),

                                                           d_vec.begin(), d_vec.end(),

                                                           0.0, thrust::plus());



  // 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());

}

```



[See it on Godbolt](https://godbolt.org/z/be54efaKj)



## 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](thrust/cmake/README.md) for more

information. Thrust can also be added via `add_subdirectory` or tools like

the [CMake Package Manager](https://github.com/cpm-cmake/CPM.cmake).



For non-CMake projects, compile with:

- The Thrust include path (`-I`)

- The libcu++ include path (`-I/dependencies/libcudacxx/`)

- The CUB include path, if using the CUDA device system (`-I/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:



```bash

# 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:



```bash

# 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].



## CI Status
















































[GitHub]: https://github.com/nvidia/thrust



[CMake section]: https://nvidia.github.io/thrust/setup/cmake_options.html

[contributing section]: https://nvidia.github.io/thrust/contributing.html



[CMake build system]: https://cmake.org



[Apache License v2.0 with LLVM Exceptions]: https://llvm.org/LICENSE.txt

[Apache License v2.0]: https://www.apache.org/licenses/LICENSE-2.0.txt

[Boost License v1.0]: https://www.boost.org/LICENSE_1_0.txt