thrust

thrust::device_vector values thrust::device_vector keys; After initialization, keys contains some elements equal to -1. I wanted to delete the elements in keys and in the same position of values. But I do not know how to deal with it parallel? There are probably many ways to do this. One possible way: use the stencil version of thrust::remove_if ( documentation ), with the keys as your stencil, removing the elements in values where the corresponding key is -1. You will need to create a functor for the predicate test. use thrust::remove ( documentation ) on the keys to remove the values that

I'm rather new to CUDA/Thrust and have a problem with a code snippet. To make it easier I have trimmed it down to the bare minimum. The code is the following: struct functor{ functor(float (*g)(const float&)) : _g{g} {} __host__ __device__ float operator()(const float& x) const { return _g(x); } private: float (*_g)(const float&); }; __host__ __device__ float g(const float& x){return 3*x;} int main(void){ thrust::device_vector<float> X(4,1); thrust::transform(X.begin(), X.end(), X.begin(), functor(&g)); } The idea is that I can pass any function to the functor, so I can apply that function to

Looking at kernel launches within the code of CUDA Thrust, it seems they always use the default stream. Can I make Thrust use a stream of my choice? Am I missing something in the API? JackOLantern I want to update the answer provided by talonmies following the release of Thrust 1.8 which introduces the possibility of indicating the CUDA execution stream as thrust::cuda::par.on(stream) see also Thrust Release 1.8.0 . In the following, I'm recasting the example in False dependency issue for the Fermi architecture in terms of CUDA Thrust APIs. #include <iostream> #include "cuda_runtime.h"

With a just a cursory understanding of these libraries, they look to be very similar. I know that VexCL and Boost.Compute use OpenCl as a backend (although the v1.0 release VexCL also supports CUDA as a backend) and Thrust uses CUDA. Aside from the different backends, what's the difference between these. Specifically, what problem space do they address and why would I want to use one over the other. Also, on the Thrust FAQ it is stated that The primary barrier to OpenCL support is the lack of an OpenCL compiler and runtime with support for C++ templates If this is the case, how is it possible

I am trying to sort an array of class objects based on its type by passing a comparison function as the parameter to the thrust sort. The class defination : class TetraCutInfo { public: int tetraid; unsigned int ncutEdges; unsigned int ncutNodes; unsigned int type_cut; __host__ __device__ TetraCutInfo(); }; Sort: thrust::sort(cutInfoptr,cutInfoptr+n,cmp()); cutInfoptr is a pointer of type TetraCutInfo having the address of the device memory allocated using cudaMalloc. Comparison function struct cmp { __host__ __device__ bool operator()(const TetraCutInfo x, TetraCutInfo y) { return (x.type_cut