![]() As nearly all managedCuda classes, CudaContext implements IDisposable and the wrapped Cuda context is valid until Dispose() is called. In the different constructors you can define several properties, e.g. So for each device you want to use, you need to createĪ CudaContext instance. From Cuda 4.0 on, the Cuda API demands (at least) one context per process per device. The CudaContext class: This is one of the three main classes and represents a Cuda context. I will shortly describe in the following the main classes used to implement a fully functional Cuda application in C#: You must know how to use contexts, set kernel launch grid configurations etc. you don’t need to check API call return values, you only need to catch the CudaException just as any other exception.īut still, as a developer using managedCuda you need to know Cuda. Further managedCuda provides specific exceptions The user doesn’t need to handle the entire C like function arguments, this is all done automatically. As a CudaDeviceVariable instance knows about its wrapped data type, array sizes, dimensionsĪnd eventually a memory alignment pitch, a simple call to CopyToHost(“hostArray”) is enough. It is a generic class allowing type safe and object oriented access to the Cuda driver API. These are represented by the class CudaDeviceVariable. In the original Cuda driver API those are given by standard C pointers. A good example for this wrapping approach is a device variable. This design allows an intuitive and simple access to all API calls by providing correspondent methods per class. For example, instead of a handle CUContext, managedCUDA provides a CudaContext class. In general you can find C# classes for each Cuda handle in the driver API. ManagedCUDA takes a different approach to represent CUDA specifics: managedCuda is object oriented. It is kind of an equivalent to the runtime API (= a comfortable wrapper of the driver API for C/C++) but written entirely in C# for. ManagedCuda provides an intuitive access to the Cuda driver API for any. Void copyInvViewMatrix(float *invViewMatrix, size_t sizeofMatrix)ĬheckCudaErrors(cudaMemcpyToSymbol(c_invViewMatrix, invViewMatrix, sizeofMatrix)) Įxtern "C" void render_kernel(dim3 gridSize, dim3 blockSize, uint *d_output, uint imageW, uint imageH,float density, float brightness, float transferOffset, float transferScale)ĭ_render>(d_output, imageW, imageH, density,īrightness, transferOffset, transferScale) Render_kernel(gridSize, blockSize, d_output, width, height, density, brightness, transferOffset, transferScale) ĬheckCudaErrors(cudaGraphicsUnmapResources(1, &cuda_pbo_resource, 0)) call CUDA kernel, writing results to PBO ![]() printf("CUDA mapped PBO: May access %ld bytes\n", num_bytes) ĬheckCudaErrors(cudaMemset(d_output, 0, width*height*4)) And i don't know how to deal with this part:ĬopyInvViewMatrix(invViewMatrix, sizeof(float4)*3) ĬheckCudaErrors(cudaGraphicsMapResources(1, &cuda_pbo_resource, 0)) ĬheckCudaErrors(cudaGraphicsResourceGetMappedPointer((void **)&d_output, &num_bytes, Hello, i am a noob in ManagedCuda, and i'm trying to translate a sample code from Cuda 6.0 (volumeRender) into C# using ManagedCuda. MessageBox.Show(err.Value) //tell what went wrong K.Run(c.DevicePointer, a.DevicePointer, b.DevicePointer) Ĭatch (Exception) //if done right, only catch linker errors. important: add the device runtime Files\NVIDIA GPU Computing Toolkit\CUDA\v6.0\lib\Win32\cudadevrt.lib", CUJITInputType.Library, null) ĬudaKernel k = ctx.LoadKernelPTX(tempArray, "addKernel") ĬudaDeviceVariable a = new int ĬudaDeviceVariable c = new CudaDeviceVariable(5) Add an info and error buffer to see what the linker wants to tell us:ĬudaJitOptionCollection options = new CudaJitOptionCollection() ĬudaJOErrorLogBuffer err = new CudaJOErrorLogBuffer(1024) ĬudaJOInfoLogBuffer info = new CudaJOInfoLogBuffer(1024) ĬudaLinker linker = new CUJITInputType.PTX, null) If you compile this to a ptx (with option -rdc=true) then you do the following in C# using CudaLinker: _global_ void addKernel(int *c, const int *a, const int *b) _global_ void addKernel2(int *c, const int *a, const int *b) In order to use dynamic parallelism, you need to link the ptx file first with the cuda device runtime library before you load the kernel.įor example a modified vector add kernel from the Cuda samples: This is not a problem caused by managedCuda, it is the driver API of CUDA the we use.
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