def mdist_kernel(Q, S, out):
"""mean-adjusted Euclidean Distance naive kernel: nothing cached"""
warpDim = cuda.blockDim.x // 32
warpIdx = cuda.threadIdx.x // 32
laneIdx = cuda.threadIdx.x % 32
lower = cuda.blockIdx.x * warpDim + warpIdx
stride = cuda.gridDim.x * warpDim
for position in range(lower, S.shape[0] - Q.shape[0] + 1, stride):
accum = float64(0)
for index in range(laneIdx, Q.shape[0], 32):
accum += S[position + index]
for delta in [16, 8, 4, 2, 1]:
accum += cuda.shfl_xor_sync(0xFFFFFFFF, accum, delta)
mean = accum / Q.shape[0]
accum = float64(0)
for index in range(laneIdx, Q.shape[0], 32):
value = Q[index] - S[position + index] + mean
accum += value * value
for delta in [16, 8, 4, 2, 1]:
value = cuda.shfl_down_sync(0xFFFFFFFF, accum, delta)
accum += value
if laneIdx == 0:
out[position] = accum
def zdist_kernel(Q, S, out, epsilon):
"""z-normalized Euclidean Distance naive kernel: nothing cached"""
warpDim = cuda.blockDim.x // 32
warpIdx = cuda.threadIdx.x // 32
laneIdx = cuda.threadIdx.x % 32
lower = cuda.blockIdx.x * warpDim + warpIdx
stride = cuda.gridDim.x * warpDim
for position in range(lower, S.shape[0] - Q.shape[0] + 1, stride):
accum1 = float64(0)
accum2 = float64(0)
for index in range(laneIdx, Q.shape[0], 32):
value = S[position + index]
accum1 += value
accum2 += value * value
for delta in [16, 8, 4, 2, 1]:
accum1 += cuda.shfl_xor_sync(0xFFFFFFFF, accum1, delta)
accum2 += cuda.shfl_xor_sync(0xFFFFFFFF, accum2, delta)
mean = accum1 / Q.shape[0]
sigma = accum2 / Q.shape[0] - mean * mean
sigma = sqrt(sigma) if sigma > 0.0 else epsilon
accum = float64(0)
for index in range(laneIdx, Q.shape[0], 32):
value = Q[index] - (S[position + index] - mean) / sigma
accum += value * value
for delta in [16, 8, 4, 2, 1]:
accum += cuda.shfl_down_sync(0xFFFFFFFF, accum, delta)
if laneIdx == 0:
out[position] = accum
def use_shfl_sync_xor(ary, xor):
i = cuda.grid(1)
val = cuda.shfl_xor_sync(0xffffffff, i, xor)
ary[i] = val