I am looking for the most concise amount of code possible that can be coded both for a CPU (using g++) and a GPU (using nvcc) for which the GPU consistently outperforms the CPU. Any type of algorithm
I have the following CUDA code: enum METHOD_E { METH_0 = 0, METH_1 }; template <enum METHOD_E METH> 开发者_如何学JAVAinline __device__ int test_func<METH>()
I am currently writing a CUDA application and want to use the boost::program_options library to get the required parameters and user input.
For a test I have written a code of matrix multiplication in C(cuda) and compiled it using nvcc to create shared library using following command.
Whats the best way to have a static assert for the NVCC compiler inside a struct which is used for compile time settings:
When I compile my CUDA code with NVCC and I have already defined a preprocessing variable in the code, e.g. #define DEBUG_OUTPUT 0, is there a way to overwrite such a variable on the fly when compilin
Hey there, when I compile with nvcc -arch=sm_13 I get: ptxas info: Used 29 registers, 28+16 bytes smem, 7200 bytes cmem[0], 8 bytes cmem[1]
I\'m having problems linking a project using nvcc. They occur with symbols defined inside the project. I have some function symbols defined in the cuda_bvh_constru.o file. This is the nm output for th
I\'ve been trying to allocate a variable that can be accessed by each kernel function. My attempt is the code attached below, but it won\'t compile cause the dArray can\'t be viewed accessed by the ke
We have been developing our code in linux, but would like to compile a windows executable. The old non-gpu version compiles just fine with mingw in windows, so I was hoping I\'d be able to do the same
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