Thrust: Code at the speed of light

Thrust is a C++ parallel programming library which resembles the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. Interoperability with established technologies (such as CUDA, TBB, and OpenMP) facilitates integration with existing software. Develop high-performance applications rapidly with Thrust!

Thrust is included in the NVIDIA HPC SDK and the CUDA Toolkit.

Refer to the Quick Start Guide page for further information and examples.

Examples

Thrust is best explained through examples. The following source code 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 <algorithm>
#include <cstdlib>

int main(void)
{
  // generate 32M random numbers serially
  thrust::host_vector<int> h_vec(32 << 20);
  std::generate(h_vec.begin(), h_vec.end(), rand);

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

  // sort data on the device (846M keys per second on GeForce GTX 480)
  thrust::sort(d_vec.begin(), d_vec.end());

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

  return 0;
}

This code sample computes the sum of 100 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 <algorithm>
#include <cstdlib>

int main(void)
{
  // generate random data serially
  thrust::host_vector<int> h_vec(100);
  std::generate(h_vec.begin(), h_vec.end(), rand);

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

Releases

Thrust is distributed with the NVIDIA HPC SDK and the CUDA Toolkit in addition to GitHub.

See the changelog for details about specific releases.

Thrust Release	Included In
1.10.0	NVIDIA HPC SDK 20.9
1.9.10-1	NVIDIA HPC SDK 20.7 & CUDA Toolkit 11.1
1.9.10	NVIDIA HPC SDK 20.5
1.9.9	CUDA Toolkit 11.0
1.9.8-1	NVIDIA HPC SDK 20.3
1.9.8	CUDA Toolkit 11.0 Early Access
1.9.7-1	CUDA Toolkit 10.2 for Tegra
1.9.7	CUDA Toolkit 10.2
1.9.6-1	NVIDIA HPC SDK 20.3
1.9.6	CUDA Toolkit 10.1 Update 2
1.9.5	CUDA Toolkit 10.1 Update 1
1.9.4	CUDA Toolkit 10.1
1.9.3	CUDA Toolkit 10.0
1.9.2	CUDA Toolkit 9.2
1.9.1-2	CUDA Toolkit 9.1
1.9.0-5	CUDA Toolkit 9.0
1.8.3	CUDA Toolkit 8.0
1.8.2	CUDA Toolkit 7.5
1.8.1	CUDA Toolkit 7.0
1.8.0
1.7.2	CUDA Toolkit 6.5
1.7.1	CUDA Toolkit 6.0
1.7.0	CUDA Toolkit 5.5
1.6.0
1.5.3	CUDA Toolkit 5.0
1.5.2	CUDA Toolkit 4.2
1.5.1	CUDA Toolkit 4.1
1.5.0
1.4.0	CUDA Toolkit 4.0
1.3.0
1.2.1
1.2.0
1.1.1
1.1.0
1.0.0

Adding Thrust To A CMake Project

Since Thrust is a header library, there is no need to build or install Thrust to use it. The thrust directory contains a complete, ready-to-use Thrust package upon checkout.

We provide CMake configuration files that make it easy to include Thrust from other CMake projects. See the CMake README for details.

Development Process

Thrust uses the CMake build system to build unit tests, examples, and header tests. To build Thrust as a developer, the following recipe should be followed:

# 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. More information on configuring your Thrust build and creating a pull request can be found in CONTRIBUTING.md.