nvcc

I am attempting to modify a GPL program written in C. My goal is to replace one method with a CUDA implementation, which means I need to compile with nvcc instead of gcc. I need help building the project - not implementing it (You don't need to know anything about CUDA C to help, I don't think). This is my first time trying to change a C project of moderate complexity that involves a .configure and Makefile. Honestly, this is my first time doing anything in C in a long time, including anything involving gcc or g++, so I'm pretty lost. I'm not super interested in learning configure and

I was testing the new CUDA 8 along with the Pascal Titan X GPU and is expecting speed up for my code but for some reason it ends up being slower. I am on Ubuntu 16.04. Here is the minimum code that can reproduce the result: CUDASample.cuh class CUDASample{ public: void AddOneToVector(std::vector<int> &in); }; CUDASample.cu __global__ static void CUDAKernelAddOneToVector(int *data) { const int x = blockIdx.x * blockDim.x + threadIdx.x; const int y = blockIdx.y * blockDim.y + threadIdx.y; const int mx = gridDim.x * blockDim.x; data[y * mx + x] = data[y * mx + x] + 1.0f; } void CUDASample:

I am still not sure how to properly specify the architectures for code generation when building with nvcc. I am aware that there is machine code as well as PTX code embedded in my binary and that this can be controlled via the controller switches -code and -arch (or a combination of both using -gencode ). Now, according to this apart from the two compiler flags there are also two ways of specifying architectures: sm_XX and compute_XX , where compute_XX refers to a virtual and sm_XX to a real architecture. The flag -arch only takes identifiers for virtual architectures (such as compute_XX )

The problem : Having a .h, I want to define real to be double if compiling for c/c++ or for cuda with computing capability >= 1.3. If compiling for cuda with computing capability < 1.3 then define real to be float. After many hours I came to this (which does not work ) # if defined(__CUDACC__) # warning * making definitions for cuda # if defined(__CUDA_ARCH__) # warning __CUDA_ARCH__ is defined # else # warning __CUDA_ARCH__ is NOT defined # endif # if (__CUDA_ARCH__ >= 130) # define real double # warning using double in cuda # elif (__CUDA_ARCH__ >= 0) # define real float # warning using

I am rather new to Linux (using Ubuntu 14.04 LTS 64bit), coming from Windows, and am attempting to port over an existing CUDA project of mine. When linking via /usr/local/cuda/bin/nvcc -arch=compute_30 -code=sm_30,compute_30 -o Main.o Display.o FileUtil.o Timer.o NeuralNetwork.o -L/usr/lib -L/usr/local/lib -L/usr/lib/x86_64-linux-gnu -L/usr/local/cuda/lib64 -lGLEW -lglfw3 -lGL -lGLU -lcuda -lcudart I encounter the following error: /usr/bin/ld: /usr/local/lib/libglfw3.a(x11_clipboard.c.o): undefined reference to symbol 'XConvertSelection' //usr/lib/x86_64-linux-gnu/libX11.so.6: error adding

I'm trying to integrate CUDA to an existing aplication wich uses boost::spirit. Isolating the problem, I've found out that the following code does not copile with nvcc: main.cu : #include <boost/spirit/include/qi.hpp> int main(){ exit(0); } Compiling with nvcc -o cudaTest main.cu I get a lot of errors that can be seen here . But if I change the filename to main.cpp , and compile again using nvcc , it works. What is happening here and how can I fix it? nvcc sometimes has trouble compiling complex template code such as is found in Boost, even if the code is only used in __host__ functions. When