Hi,
Thanks for your pointers, problem solved.
I have upgraded to CMake 3.9.1 (I don't think this matters though) and I
switched to using the new CUDA CMake support as at your point 3). I am
also using CUDA 9 RC which supports VS2017 (I was testing under CUDA 8 /
CUDA 9 earlier, but just forgot to mention this). Now all builds
successfully.
I am attaching the updated CMakeLists.txt file for the record.
Thanks, Best Regards,
Andrea
On 8/10/2017 4:28 PM, Robert Maynard wrote:
So you are going to have two issues.
1. The FindCUDA module has not been updated to handle VS2017. The
issue is that the VCInstallDir variable now returns a different
relative path to the compiler than it previously did. If you can
determine the new logic a patch fixing this behavior be great.
2. It doesn't look like CUDA 8.0 supports VS2017 (
http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html#system-requirements
).
3. You could also look at using the new CMake 3.9 cuda support (
https://devblogs.nvidia.com/parallelforall/building-cuda-applications-cmake/
).
On Thu, Aug 10, 2017 at 5:49 AM, Andrea Borsic <abor...@gmail.com> wrote:
Hi All,
I am working on this platform:
Windows 10 64bit
Visual Studio 2015 Community Edition
Visual Studio 2017 Community Edition
CUDA 8.0
CMake 3.9
I am in the middle of switching from VS2015 to VS2017, but CUDA projects
fail to properly compile under VS2017 as the compiler/linker fail to find
tools on the path setup by CMake. I believe this is bug/incompatibly of the
CMake FindCUDA module with VS2017.
To reproduce the problem I am attaching a tiny project
main.cu is a minimal CUDA example from the web
CMakeLists.txt is a CMake file that leads to a successful build under VS2015
and unsuccessful under VS2017
Output VS2015 is the output from building the project under VS2015 (all
targets built OK)
Output VS2017 is the output from building the project under VS2015 (1 target
OK one target fails)
I have noticed also that oddly under VS2017 an "x64" and "main.dir"
directories are created outside the build dir, and at the level of the
source directory.
I thought of reporting this to the list, and any help is welcome,
Thank you and Best Regards,
Andrea
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#include <stdio.h>
//
// Nearly minimal CUDA example.
// Compile with:
//
// nvcc -o example example.cu
//
#define N 1000
//
// A function marked __global__
// runs on the GPU but can be called from
// the CPU.
//
// This function multiplies the elements of an array
// of ints by 2.
//
// The entire computation can be thought of as running
// with one thread per array element with blockIdx.x
// identifying the thread.
//
// The comparison i<N is because often it isn't convenient
// to have an exact 1-1 correspondence between threads
// and array elements. Not strictly necessary here.
//
// Note how we're mixing GPU and CPU code in the same source
// file. An alternative way to use CUDA is to keep
// C/C++ code separate from CUDA code and dynamically
// compile and load the CUDA code at runtime, a little
// like how you compile and load OpenGL shaders from
// C/C++ code.
//
__global__
void add(int *a, int *b) {
int i = blockIdx.x;
if (i<N) {
b[i] = 2*a[i];
}
}
int main() {
//
// Create int arrays on the CPU.
// ('h' stands for "host".)
//
int ha[N], hb[N];
//
// Create corresponding int arrays on the GPU.
// ('d' stands for "device".)
//
int *da, *db;
cudaMalloc((void **)&da, N*sizeof(int));
cudaMalloc((void **)&db, N*sizeof(int));
//
// Initialise the input data on the CPU.
//
for (int i = 0; i<N; ++i) {
ha[i] = i;
}
//
// Copy input data to array on GPU.
//
cudaMemcpy(da, ha, N*sizeof(int), cudaMemcpyHostToDevice);
//
// Launch GPU code with N threads, one per
// array element.
//
add<<<N, 1>>>(da, db);
//
// Copy output array from GPU back to CPU.
//
cudaMemcpy(hb, db, N*sizeof(int), cudaMemcpyDeviceToHost);
for (int i = 0; i<N; ++i) {
printf("%d\n", hb[i]);
}
//
// Free up the arrays on the GPU.
//
cudaFree(da);
cudaFree(db);
return 0;
}# minimal example based on
https://devblogs.nvidia.com/parallelforall/building-cuda-applications-cmake/
cmake_minimum_required(VERSION 3.9 FATAL_ERROR) # this is required for the new
CUDA support module
project (TestProject LANGUAGES CXX CUDA)
add_executable(TestExecutable main.cu)
target_compile_features(TestExecutable PUBLIC cxx_std_11)
set_target_properties(TestExecutable PROPERTIES CUDA_SEPARABLE_COMPILATION ON)
SET(CUDA_NVCC_FLAGS "-arch=sm_35" CACHE STRING "nvcc flags" FORCE)
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