We are pleased to announce the release of version 1.0.0 of Thrust, an open-source template library for data parallel CUDA applications featuring an interface similar to the C++ Standard Template Library (STL). Thrust provides a flexible high-level interface for GPU programming that greatly enhances developer productivity while remaining high performance. Note that Thrust supersedes Komrade, the initial release of the library, and all future development will proceed under this title.
The thrust::host_vector and thrust::device_vector containers simplify memory management and transfers between host and device. Thrust provides efficient algorithms for:
[*]sorting - thrust::sort and thrust::sort_by_key
[*]transformations - thrust::transform
[*]reductions - thrust::reduce and thrust::transform_reduce
[*]scans - thrust::inclusive_scan and thrust::transform_inclusive_scan
[*]And many more!
Please refer to http://code.google.com/p/thrust/wiki/Documentation for a complete listing and join us on the thrust-users discussion group.
As the following code example shows, Thrust programs are concise and readable.
[codebox]
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/generate.h>
#include <thrust/sort.h>
int main(void)
{
// generate random data on the host
thrust::host_vector h_vec(20);
thrust::generate(h_vec.begin(), h_vec.end(), rand);
// transfer to device and sort
thrust::device_vector d_vec = h_vec;
thrust::sort(d_vec.begin(), d_vec.end());
return 0;
}
[/codebox]
Thrust provides high-level primitives for composing interesting computations.
This example computes the norm of a vector.
[codebox]
#include <thrust/transform_reduce.h>
#include <thrust/functional.h>
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
// square computes the square of a number f(x) → x*x
template
struct square
{
__host__ __device__
T operator()(const T& x) const {
return x * x;
}
};
int main(void)
{
// initialize host array
float x[4] = {1.0, 2.0, 3.0, 4.0};
// transfer to device
thrust::device_vector<float> d_x(x, x + 4);
// setup arguments
square<float> unary_op;
thrust::plus<float> binary_op;
float init = 0;
// compute norm
float norm = std::sqrt( thrust::transform_reduce(d_x.begin(), d_x.end(), unary_op, init, binary_op) );
std::cout << norm << std::endl;
return 0;
}
[/codebox]
Download Thrust here, and check out the tutorial to get started.
Thrust is open source under the Apache 2.0 license and available now @ http://thrust.googlecode.com
