reduction_sum.py 文件源码

def hist_comp(arry, hist, result):

    # We have N threads per block
    # And We have one block only

    x = cuda.grid(1)

    R = cuda.shared.array(9, dtype=float64)


    # No of featureVectors
    # array.shape[0] == 9*34
    A = cuda.shared.array(shape=(9,34), dtype=float64)

    # Vecture To Compair
    # hist.shape[0] == BIN_COUNT == 34 ?
    B = cuda.shared.array(34, dtype=float64)
    for i in range(BIN_COUNT):
        B[i] = hist[i]

    A[x] = arry[x]



    cuda.syncthreads()

    # Do Actual Calculations.
    # i.e: kullback_leibler_divergence
    Sum = 0.00
    for i in range(BIN_COUNT):
        a = B[i]
        b = A[x][i]
        Sum += (a * (math.log(a/b) / math.log(2.0)))

    # R Contains the KL-Divergences
    R[x] = Sum
    cuda.syncthreads()

    # Finding the Min Divergence OR
    # Finding the sum of all Divergences
    # by Reducing Method

    rSize = cuda.blockDim.x >> 1
    while rSize > 0:
        if x < rSize:
            R[x] = (R[x]+R[x+rSize])
            # R[x] = min(R[x],R[x+rSize])
        rSize >>= 1
        cuda.syncthreads()

    # This implementation doesn't take care of last two values.
    # So, Using Hack
    # TODO: need to Fix It.
    if x == 0 :
        # R[x] = x if R[x] < R[x+1] else (x+1)
        R[x] = (R[x]+R[x+1])
        # R[x] = min(R[x],R[x+1])
        result[x] = R[x]