Question

我正在使用numpy.memmap来计算大型covarince矩阵。

问题是为什么第二种情况下的乘法（当我在磁盘上存储A.T时）较慢？

def matrix_append_row(fm,tp,mat):
    #check if number of columns is the same
    rows= fm.shape[0] + mat.shape[0]
    new_fm = np.memmap(fm.filename, mode='r+', dtype= tp, shape=(rows,fm.shape[1]))
    new_fm[fm.shape[0]:rows,:]= mat[:]
    return new_fm
def generate_and_store_data(cols,batch,iter,tp):
    #create memmap file and append generated data in cycle

    #can't create empty array - need to store first entry by copy not by append
    fm= np.memmap('A.npy', dtype=tp, mode='w+', shape=(batch,cols))

    for i in xrange(iter):
        data= np.random.rand(batch,cols)*100  # [0-1]*100
        # print i
        # t0= time.time()
        if i==0:
            fm[:]= data[:]
        else:   
            fm= matrix_append_row(fm,tp,data)
        # print (time.time()-t0)

        # fm.flush()
        # print fm.shape

    return fm


A= generate_and_store_data(1000,5000,10,'float32')
#cov= A.T*A

# A memmaped file
print A.shape
M= A.shape[0]
N= A.shape[1]
#create cov mat
cov= np.memmap('cov.npy', dtype='float32', mode='w+', shape=(N,N))

#A.T don't copy data just view?
t0= time.time()
np.dot(A.T,A,out= cov)
print (time.time()-t0)

print A.T.shape

cov_= np.memmap('cov_.npy', dtype='float32', mode='w+', shape=(N,N))
At= np.memmap('At.npy', dtype='float32', mode='w+', shape=(N,M))

t0= time.time()
At= A.T # only reference
print (time.time()-t0)

t0= time.time()
At[:]= A.T # copy same as At[:]= (A.T)[:] ?
print (time.time()-t0)

t0= time.time()
At[:]= (A.T)[:]
print (time.time()-t0)

t0= time.time()
np.dot(At,A,out= cov_)
print (time.time()-t0)

输出

(50000, 1000)
2.84399986267
(1000, 50000)
0.0
2.17100000381
2.07899999619
2.73399996758

更新

也尝试了逐块矩阵乘法，但它很慢（也许我需要调整block_size）

cov2= np.memmap('cov2.npy', dtype='float32', mode='w+', shape=(N,N))
block_size=100
t0= time.time()
for i_start in range(0, At.shape[0], block_size):
    for j_start in range(0, A.shape[1], block_size):
        for k_start in range(0, At.shape[1], block_size):
            cov2[i_start:i_start+block_size, j_start:j_start + block_size] +=np.dot(At[i_start:i_start + block_size, k_start:k_start + block_size],A[k_start:k_start + block_size, j_start:j_start + block_size])
print (time.time()-t0)

Answer 1

因此，即使内存布局更改是时间的一部分，这也是我获得更小阵列的原因：

arr = numpy.random.rand(500, 100)
arr *= 100

%timeit numpy.dot(arr.T, arr)
100 loops, best of 3: 7.52 ms per loop

%timeit numpy.dot(numpy.asfortranarray(arr.T), arr)
100 loops, best of 3: 7.53 ms per loop

%timeit numpy.dot(arr.T, numpy.asfortranarray(arr))
100 loops, best of 3: 5.26 ms per loop

我不确定为什么您发布的尺寸有如此惊人的不同运行时间，但结果如下：

arr = numpy.random.rand(50000, 1000)
arr *= 100
cov = numpy.zeros((1000, 1000))

%timeit numpy.dot(arr.T, arr, out=cov)
1 loops, best of 3: 12min 45s per loop

%timeit numpy.dot(numpy.asfortranarray(arr.T), arr, out=cov)
1 loops, best of 3: 12min 42s per loop

%timeit numpy.dot(arr.T, numpy.asfortranarray(arr), out=cov)
1 loops, best of 3: 7min 19s per loop

当然，这也很大程度上取决于你编写numpy的BLAS实现，但改变内存布局似乎是值得的。

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1 个答案: