[gmx-developers] Compiling with GPU/mkl on MSVC 2019

[Adriaan Riet]

Adriaan Riet
801-602-0172
adriaan.riet at case.edu
Hello,

I have been working on building gromacs with GMX_GPU=ON in MSVC 2019 (and
cuda 10.1)  I've used the Ninja and Visual studio 2019 Win64 generators,
and I'm using the x64 cl.exe as the host compiler, cxx compiler, etc.

With GMX_GPU=OFF, I only needed to make sure there were no spaces in some
of the cmake flags (I changed CUDA_TOOLKIT_ROOT_DIR, MKL_LIBRARIES,
MKL_INCLUDE_DIR), and everything builds and runs fine.

With GMX_GPU=ON, I've seen a couple of issues. First, and I've gotten
around this by changing CUDA_HOST_COMPILER, the location ${VCInstallDir}bin
no longer points to a valid compiler. MS has opted to change the location
and provide separate x86 and x64 compilers and cross compilers. I've
pointed the CUDA_HOST_COMPILER variable to the same compiler as the other
CMAKE_C**_COMPILER variables,
C:\Program Files (x86)\Microsoft Visual
Studio\2019\Community\VC\Tools\MSVC\14.21.27702\bin\Hostx64\x64\cl.exe
I'm not sure that this is the compiler I should be using, as GPU should be
single precision, but I wanted to be consistent.

I'm running into an issue in the CUDA code. Specifically, there are a few
lines in nbnxm_cuda.cu that set up for launching the kernel (lines 834,
879, etc):

const auto kernelArgs          = prepareGpuKernelArguments(kernelFn, config,
                                                               &numColumns,
                                                               &xqPtr,

 &setFillerCoords,
                                                               &d_x,

 &d_atomIndices,
                                                               &d_cxy_na,
                                                               &d_cxy_ind,
                                                               &cellOffset,

 &numAtomsPerCell);
    launchGpuKernel(kernelFn, config, nullptr, "XbufferOps", kernelArgs);

the relevant template generating prepareGpuKernelArguments is (I think,
from cudautils.cuh):

template <typename ... Args>
std::array<void *, sizeof ... (Args)> prepareGpuKernelArguments(void
              (*kernel)(Args...),
                                                                const
KernelLaunchConfig & /*config */,
                                                                const Args
*...          argsPtrs)
{
    std::array<void *, sizeof ... (Args)> kernelArgs;
    prepareGpuKernelArgument(kernel, &kernelArgs, 0, argsPtrs ...);
    return kernelArgs;
}

the compiler throws a fit with this, stating that (I've changed the order
to what I understand to be cause/effect):

1. "Error C2782 'std::array<void*,sizeof...(Args)>
prepareGpuKernelArguments(void (__cdecl *)(Args...),const
KernelLaunchConfig &,const Args *...)': template parameter 'Args' is
ambiguous "
2. " 'prepareGpuKernelArguments': no matching overloaded function found
3. " 'kernelArgs': cannot be used before it is initialized
4. " 'kernelArgs': an object of const-qualified type must be initialized
5. " 'void launchGpuKernel<const float3*__restrict ,rvec(*),const
int*__restrict ,int,int>(void (__cdecl *)(const float3 *__restrict ,rvec
(*),const int *__restrict ,int,int),const KernelLaunchConfig &,CommandEvent
*,const char *,const std::array<void *,5> &)': cannot convert argument 5
from 'int' to 'const std::array<void *,5> &' nbnxm_cuda.cu 886

Seems to me that VS isn't recognizing some feature here. Is this something
that might be easy to fix?

Thanks,
Adriaan Riet
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