Parallelize code using CUDA [migrated]
- by user878944
If I have a code which takes struct variable as input and manipulate it's elements, how can I parallelize this using CUDA?
void BackpropagateLayer(NET* Net, LAYER* Upper, LAYER* Lower)
{
INT i,j;
REAL Out, Err;
for (i=1; i<=Lower->Units; i++) {
Out = Lower->Output[i];
Err = 0;
for (j=1; j<=Upper->Units; j++) {
Err += Upper->Weight[j][i] * Upper->Error[j];
}
Lower->Error[i] = Net->Gain * Out * (1-Out) * Err;
}
}
Where NET and LAYER are structs defined as:
typedef struct { /* A LAYER OF A NET: */
INT Units; /* - number of units in this layer */
REAL* Output; /* - output of ith unit */
REAL* Error; /* - error term of ith unit */
REAL** Weight; /* - connection weights to ith unit */
REAL** WeightSave; /* - saved weights for stopped training */
REAL** dWeight; /* - last weight deltas for momentum */
} LAYER;
typedef struct { /* A NET: */
LAYER** Layer; /* - layers of this net */
LAYER* InputLayer; /* - input layer */
LAYER* OutputLayer; /* - output layer */
REAL Alpha; /* - momentum factor */
REAL Eta; /* - learning rate */
REAL Gain; /* - gain of sigmoid function */
REAL Error; /* - total net error */
} NET;
What I could think of is to first convert the 2d Weight into 1d. And then send it to kernel to take the product or just use the CUBLAS library. Any suggestions?
