我正在尝试实现一个计算百分比平均值的内核。
示例-取3D数组(在下面的代码中)[[2,4],[3,6],[4,8]]
并计算(4+6+8)/((4+6+8)+(2+3+4))
这里是一个colab笔记本,可以快速运行以下代码:https://colab.research.google.com/drive/1k_XfOVOYWOTnNQFA9Vo_H93D9l-xWO8K?usp=sharing
# -*- coding: utf-8 -*-
import numpy as np
import pycuda.autoinit
import pycuda.driver as cuda
import pycuda.gpuarray as gpuarray
import pycuda.driver as cuda
import pycuda.autoinit
from pycuda.compiler import SourceModule
# set dimentions
ROWS = 3
COLS = 2
h_perms = np.array([[
[ 1,1],
[ 1,1],
[ 1,1]
],[
[ 2,7],
[ 3,11],
[ 4,13]
],[
[ 2,4],
[ 3,6],
[ 4,8]
],[
[ 2,7],
[ 3,11],
[ 4,13]
],[
[ 2,4],
[ 3,6],
[ 4,8]
],[
[ 1,1],
[ 1,1],
[ 1,1]
]
], dtype=np.float32).flatten()
# send to device
d_perms = gpuarray.to_gpu(h_perms)
kernel = SourceModule("""
__global__
void calc(float *permutations, int *permutationShape, float *results)
{
__shared__ float c;
__shared__ float b;
int bIdx = blockIdx.y * gridDim.x + blockIdx.x;
int tIdx = threadIdx.y * blockDim.x + threadIdx.x;
int rowCount = permutationShape[0];
int colCount = permutationShape[1];
int i = (bIdx * rowCount * colCount) + (tIdx * colCount);
c += permutations[i];
b += permutations[i+1];
__syncthreads();
results[bIdx] = b / (b + c);
}
""")
calc = kernel.get_function('calc')
# prepare results array
d_results = gpuarray.zeros((6,1), np.float32)
d_results = gpuarray.to_gpu(d_results)
h_perms_shape = np.array([ROWS,COLS], np.int32);
d_perms_shape = gpuarray.to_gpu(h_perms_shape);
start = cuda.Event()
end = cuda.Event()
start.record()
calc(d_perms, d_perms_shape, d_results, block=(ROWS,1,1), grid=(ROWS*COLS,1,1))
end.record()
secs = start.time_till(end)*1e-3
print(secs)
print(d_results)
我希望得到这个-
array([[0.5 ],
[0.775],
[0.6666667],
[0.775],
[0.6666667],
[0.5 ]], dtype=float32)
但是我明白了-
array([[0.5 ],
[0.7777778],
[0.6666667],
[0.7777778],
[0.6666667],
[0.5 ]], dtype=float32)
我试图理解为什么(7+11+13)/((7+11+13)+(2+3+4))
的特定计算会导致任何非0.775
的结果>
我正在尝试实现一个计算百分比平均值的内核。示例-取3D数组(在下面的代码中)片段[[2,4],[3,6],[4,8]]并计算(4 + 6 + 8)/(((4 + 6 + 8) +(2 + 3 + 4))这是一个...
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