c++ - Allocating (malloc) a double** in cuda __device__ function -


It seems that a lot about moving the 2D arrays from the host to double (or int, or float, etc.) The questions are in the machine. This is not my question.

I have already taken all the data to the GPU and, __ global __ kernel call many __ devices___ function.

In these device corners, I have tried the following:

To assign: 

  __i device__ double ** matrix create (int Rows, int colals, double initial values) {double ** temporary; Floating = (double **) malloc (rows * sizeof (double *)); (Int j = 0; j and lt; rows; j ++) {temp [j] = (double *) malloc (column * size (double));} // Set for initial values ​​(int i = 0 ; i & lt; rows; I ++) {for (int j = 0; j & lt; cols; j ++) {temp [i] [j] = initialValue; }} Return temporary; }   
  
  __ device__ zero matrix desty (double ** temporary, interval lines) {for (int j = 0; j & lt; rows; j ++) {Free (Temporary [j]); } Free (temp); }   
 The  __ device __  for single dimension arrays is great, it can not seem to keep it stable in multi-dimensional case. By the way, variables are used sometime this way:  
  double ** z = matrixcrate (2,2,0); Double * x = z [0];   
 However, attention is always taken to ensure that no calls are made free with active data. The code is actually an optimization of the CPU code, so I know that the strange thing is not happening with the pointer or memory, in fact I am just redefining the recurrent and a  __ device on the serial parts < / Code> throwing Just run the whole serial bit 10000 times and GPU is a good way to do this. ++++++++++++++++++++++++++++++++++++++++++++ Problem solved by +++++++++++++ diameter According to the specifications, the size of the stack is initially set to 8 MB, if your malcons are more than this, then NSETET will not start and the kernel crashes. Use the following under the host code.  
  Float increment = 10; CudaDeviceSetLimit (cudaLimitMallocHeap size, size [0] * increased);   
 worked for me!   
 
 
 GPU side  molok ()  is a sub-allocator of allocation from a limited heap Depending on the number, it is possible that the stack is ending. You can change the size of the backing pile by using  cudaDeviceSetLimit (cudaLimitMallocHeapSize, size_t size) . For more information see:

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