The acquisition of HP by Xerox is a bad idea on many fronts. I am very familiar with the business of HP and my opinion is heavily based on this. As far as I’m concerned, Xerox is a poorly managed […]
Posted on November 17, 2018 by admin
Month: November 2018
Posted on November 10, 2018 by admin
CUDA Kernel Error | unspecified launch failure | Error: C:/kernel.cu:170, code: 4, reason: unspecified launch failure | TDR Registry Keys | Tdrlevel | Windows Environment
When I try to run CUDA code that takes a long time to process on the GPU, I would always get an error such as the following: Error: C:/kernel.cu:170, code: 4, reason: unspecified launch failure After spending many sleepless nights […]
Posted on November 8, 2018 by admin
Posted on November 7, 2018 by admin
CUDA Hyper-Q Explained
Hyper-Q explained by the CEO of NVIDIA. He also talks about dynamic parallelism after. Enjoy!
Posted on by admin
CUDA Environment Variables
A discussion of CUDA environment variables that enables you to accomplish more things in your code. See video below. Please donate using the link in the sidebar. List of CUDA environment variables: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars Download code: Cuda_Education_Environment_Variables
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#include "cuda_runtime.h" #include "device_launch_parameters.h" #include <stdio.h> cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size); __global__ void addKernel(int *c, const int *a, const int *b) { int i = threadIdx.x; c[i] = a[i] + b[i]; } int setenv(const char *name, const char *value, int overwrite) { int errcode = 0; if (!overwrite) { size_t envsize = 0; errcode = getenv_s(&envsize, NULL, 0, name); if (errcode || envsize) return errcode; } return _putenv_s(name, value); } int main() { // set up max connection char* iname = "CUDA_VISIBLE_DEVICES"; setenv(iname, "0", 1); char *ivalue = getenv(iname); printf("%s = %s\n", iname, ivalue); const int arraySize = 5; const int a[arraySize] = { 1, 2, 3, 4, 5 }; const int b[arraySize] = { 10, 20, 30, 40, 50 }; int c[arraySize] = { 0 }; // Add vectors in parallel. cudaError_t cudaStatus = addWithCuda(c, a, b, arraySize); if (cudaStatus != cudaSuccess) { fprintf(stderr, "addWithCuda failed!"); return 1; } printf("{1,2,3,4,5} + {10,20,30,40,50} = {%d,%d,%d,%d,%d}\n", c[0], c[1], c[2], c[3], c[4]); // cudaDeviceReset must be called before exiting in order for profiling and // tracing tools such as Nsight and Visual Profiler to show complete traces. cudaStatus = cudaDeviceReset(); if (cudaStatus != cudaSuccess) { fprintf(stderr, "cudaDeviceReset failed!"); return 1; } return 0; } // Helper function for using CUDA to add vectors in parallel. cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size) { int *dev_a = 0; int *dev_b = 0; int *dev_c = 0; cudaError_t cudaStatus; // Choose which GPU to run on, change this on a multi-GPU system. cudaStatus = cudaSetDevice(0); if (cudaStatus != cudaSuccess) { fprintf(stderr, "cudaSetDevice failed! Do you have a CUDA-capable GPU installed?"); goto Error; } // Allocate GPU buffers for three vectors (two input, one output) . cudaStatus = cudaMalloc((void**)&dev_c, size * sizeof(int)); if (cudaStatus != cudaSuccess) { fprintf(stderr, "cudaMalloc failed!"); goto Error; } cudaStatus = cudaMalloc((void**)&dev_a, size * sizeof(int)); if (cudaStatus != cudaSuccess) { fprintf(stderr, "cudaMalloc failed!"); goto Error; } cudaStatus = cudaMalloc((void**)&dev_b, size * sizeof(int)); if (cudaStatus != cudaSuccess) { fprintf(stderr, "cudaMalloc failed!"); goto Error; } // Copy input vectors from host memory to GPU buffers. cudaStatus = cudaMemcpy(dev_a, a, size * sizeof(int), cudaMemcpyHostToDevice); if (cudaStatus != cudaSuccess) { fprintf(stderr, "cudaMemcpy failed!"); goto Error; } cudaStatus = cudaMemcpy(dev_b, b, size * sizeof(int), cudaMemcpyHostToDevice); if (cudaStatus != cudaSuccess) { fprintf(stderr, "cudaMemcpy failed!"); goto Error; } // Launch a kernel on the GPU with one thread for each element. addKernel<<<1, size>>>(dev_c, dev_a, dev_b); // Check for any errors launching the kernel cudaStatus = cudaGetLastError(); if (cudaStatus != cudaSuccess) { fprintf(stderr, "addKernel launch failed: %s\n", cudaGetErrorString(cudaStatus)); goto Error; } // cudaDeviceSynchronize waits for the kernel to finish, and returns // any errors encountered during the launch. cudaStatus = cudaDeviceSynchronize(); if (cudaStatus != cudaSuccess) { fprintf(stderr, "cudaDeviceSynchronize returned error code %d after launching addKernel!\n", cudaStatus); goto Error; } // Copy output vector from GPU buffer to host memory. cudaStatus = cudaMemcpy(c, dev_c, size * sizeof(int), cudaMemcpyDeviceToHost); if (cudaStatus != cudaSuccess) { fprintf(stderr, "cudaMemcpy failed!"); goto Error; } Error: cudaFree(dev_c); cudaFree(dev_a); cudaFree(dev_b); return cudaStatus; } |
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Posted on November 6, 2018 by admin
Posted on November 4, 2018 by admin