cuda - CUBLAS - is matrix-element exponentiation possible? -

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I am using CUBLAS (Cuda Blas libraries) for the matrix operation.

Exponentiation / root mean of the CUBLAS matrix item to gain the square?

I mean, having 2x2 matrix 

  1 4 9 16   
 What do I want to say to a given value above There is a function to lift 2  
  1 16 81 256   
 and calculate the class square of the root like  
  1 2 3 4   
 Is it possible Cubes? I can not find the appropriate function for this goal, but I will first ask here to start my own colonel coding.   
 
 
 So you probably have to implement  do  yourself because the library For this it will not. (Perhaps this is something to implement in terms of BLS level 3 routine - certainly squaring of matrix elements - but it would include expensive and otherwise unnecessary matrix-vector multiplication and I still do not know how to ' D do workaroa operations) The reason is that these functions are not actually linear-algebraic processes; Taking the square root of each matrix element does not really match any of the original linear algebra operations.  
 The good news is that the operation of these elements is very easy to implement in the CUDA. Then, many tuning options can play for good performance, but one can start quite easily.  
 With additional functions of matrix, you will treat NXM matriculation here (N * M) - length vectors; The structure of the matrix does not make any difference to the operation of these elements. So you will pass the first element of the matrix in an indicator and treat it as a list of the N * M number. (I'm assuming that you are using  float  s, as you were talking about  SGEMM  and  SAXPY  .)  
 The actual bit of kernel, CUDA code that applies the operation, is quite easy. For now, each thread will calculate the square (or class root) of an array element. (Whether it is optimal for performance or not, whatever you can check). Then the kernel looks like I'm assuming you're doing something like B_ij = (A_ij) ^ 2; If you want to perform operation operation, such as A_ij = (A_ij) ^ 2, then you can do this:  
  __ global__ zero class elements (float * a, float * b, int n ) {/ Which element does this calculation? * / Int tid = blockDim.x * blockIdx.x + threadIdx.x; / * If valid, array element * / if (TID & lt; n) b [tid] = (a [tid] * a [tid]); } __global__ zero sqrtElements (float * a, float * b, int n) {/ Which element does this calculate? * / Int tid = blockDim.x * blockIdx.x + threadIdx.x; / * If valid, then sqrt array element * / if (tid & lt; n) b [tid] = sqrt (a [tid]); / * Or sqrtf () * /}   
 Note that if you are OK with very little error, then the 'sqrtf ()' function is the maximum 3 error in the last place) It is quite fast.  
 How do you tell these kernels, will depend on the order in which you are doing things. If you've already made some quick calls to these metrics, you would want to use them already on an array in GPU memory.

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