我从python中调用了intel的数学内核库。到目前为止，根据linux的顶级命令，它只使用一个cpu而不是全部12个cpu。如何让它使用全部12个CPU？
我尝试将三个环境变量（OMP_NUM_THREADS，MKL_NUM_THREADS，MKL_DOMAIN_NUM_THREADS）设置为12。
我也尝试过mkl_set_num_threads。
numpy使用mkl可以在密集矩阵乘法中使用全部12个cpu。
mkl是2016版。
欢迎任何建议。
感谢。
以下是测试代码：
from ctypes import *
import scipy.sparse as spsp
import numpy as np
import multiprocessing as mp
# Load the share library
mkl = cdll.LoadLibrary("libmkl_rt.so")
def get_csr_handle2(data, indices, indptr, shape):
a_pointer = data.ctypes.data_as(POINTER(c_float))
ja_pointer = indices.ctypes.data_as(POINTER(c_int))
ia_pointer = indptr.ctypes.data_as(POINTER(c_int))
return (a_pointer, ja_pointer, ia_pointer, shape)
def get_csr_handle(A,clear=False):
if clear == True:
A.indptr[:] = 0
A.indices[:] = 0
A.data[:] = 0
return get_csr_handle2(A.data, A.indices, A.indptr, A.shape)
def csr_t_dot_csr(A_handle, C_handle, nz=None):
# Calculate (A.T).dot(A) and put result into C
#
# This uses one-based indexing
#
# Both C.data and A.data must be in np.float32 type.
#
# Number of nonzero elements in C must be greater than
# or equal to the size of C.data
#
# size of C.indptr must be greater than or equal to
# 1 + (num rows of A).
#
# C_data = np.zeros((nz), dtype=np.single)
# C_indices = np.zeros((nz), dtype=np.int32)
# C_indptr = np.zeros((m+1),dtype=np.int32)
(a_pointer, ja_pointer, ia_pointer, A_shape) = A_handle
(c_pointer, jc_pointer, ic_pointer, C_shape) = C_handle
trans_pointer = byref(c_char('T'))
sort_pointer = byref(c_int(0))
(m, n) = A_shape
sort_pointer = byref(c_int(0))
m_pointer = byref(c_int(m)) # Number of rows of matrix A
n_pointer = byref(c_int(n)) # Number of columns of matrix A
k_pointer = byref(c_int(n)) # Number of columns of matrix B
# should be n when trans='T'
# Otherwise, I guess should be m
###
b_pointer = a_pointer
jb_pointer = ja_pointer
ib_pointer = ia_pointer
###
if nz == None:
nz = n*n #*n # m*m # Number of nonzero elements expected
# probably can use lower value for sparse
# matrices.
nzmax_pointer = byref(c_int(nz))
# length of arrays c and jc. (which are data and
# indices of csr_matrix). So this is the number of
# nonzero elements of matrix C
#
# This parameter is used only if request=0.
# The routine stops calculation if the number of
# elements in the result matrix C exceeds the
# specified value of nzmax.
info = c_int(-3)
info_pointer = byref(info)
request_pointer_list = [byref(c_int(0)), byref(c_int(1)), byref(c_int(2))]
return_list = []
for ii in [0]:
request_pointer = request_pointer_list[ii]
ret = mkl.mkl_scsrmultcsr(trans_pointer, request_pointer, sort_pointer,
m_pointer, n_pointer, k_pointer,
a_pointer, ja_pointer, ia_pointer,
b_pointer, jb_pointer, ib_pointer,
c_pointer, jc_pointer, ic_pointer,
nzmax_pointer, info_pointer)
info_val = info.value
return_list += [ (ret,info_val) ]
return return_list
def test():
num_cpu = 12
mkl.mkl_set_num_threads(byref(c_int(num_cpu))) # try to set number of mkl threads
print "mkl get max thread:", mkl.mkl_get_max_threads()
test_csr_t_dot_csr()
def test_csr_t_dot_csr():
AA = np.random.choice([0,1], size=(12,750000), replace=True, p=[0.99,0.01])
A_original = spsp.csr_matrix(AA)
A = A_original.astype(np.float32).tocsc()
A = spsp.csr_matrix( (A.data, A.indices, A.indptr) )
A.indptr += 1 # convert to 1-based indexing
A.indices += 1 # convert to 1-based indexing
A_ptrs = get_csr_handle(A)
C = spsp.csr_matrix( np.ones((12,12)), dtype=np.float32)
C_ptrs = get_csr_handle(C, clear=True)
print "=call mkl function="
while (True):
return_list = csr_t_dot_csr(A_ptrs, C_ptrs)
if __name__ == "__main__":
test()