我有一个包含二进制编码字符串的大列表，我之前曾在单个函数中处理过这些字符串，如下所示：
""" just included this to demonstrate the 'data' structure """
data=np.zeros(250,dtype='float32, (250000,2)float32')
def func numpy_array(data, peaks):
rt_counter=0
for x in peaks:
if rt_counter %(len(peaks)/20) == 0:
update_progress()
peak_counter=0
data_buff=base64.b64decode(x)
buff_size=len(data_buff)/4
unpack_format=">%dL" % buff_size
index=0
for y in struct.unpack(unpack_format,data_buff):
buff1=struct.pack("I",y)
buff2=struct.unpack("f",buff1)[0]
if (index % 2 == 0):
data[rt_counter][1][peak_counter][0]=float(buff2)
else:
data[rt_counter][1][peak_counter][1]=float(buff2)
peak_counter+=1
index+=1
rt_counter+=1
我一直在阅读有关多处理的内容，并认为我想尝试一下，看看是否可以大幅提高性能，我将我的函数重写为 2（帮助程序和“调用程序”），如下所示：
def numpy_array(data, peaks):
processors=mp.cpu_count #Might as well throw this directly in the mp.Pool (just for clarity for now)
pool = mp.Pool(processes=processors)
chunk_size=len(peaks)/processors
for i in range(processors):
counter = i*chunk_size
chunk=peaks[i*chunk_size:(i+1)*chunk_size-1]
pool.map(decode(data,chunk,counter))
def decode(data,chunk,counter):
for x in chunk:
peak_counter=0
data_buff=base64.b64decode(x)
buff_size=len(data_buff)/4
unpack_format=">%dL" % buff_size
index=0
for y in struct.unpack(unpack_format,data_buff):
buff1=struct.pack("I",y)
buff2=struct.unpack("f",buff1)[0]
if (index % 2 == 0):
data[counter][1][peak_counter][0]=float(buff2)
else:
data[counter][1][peak_counter][1]=float(buff2)
peak_counter+=1
index+=1
print data[counter][1][10][0]
counter+=1
该程序运行但仅使用 100-110% 的 CPU（根据 top），一旦完成就会抛出TypeError: map() takes at least 3 arguments (2 given)对我来说，任何对多进程有更多经验的人都可以给我一个提示，告诉我要注意哪些事情（这可能会导致 TypeError）？是什么原因导致我的 cpu 使用率低？
--合并答案后的代码--
def decode((data,chunk,counter)):
print len(chunk), counter
for x in chunk:
peak_counter=0
data_buff=base64.b64decode(x)
buff_size=len(data_buff)/4
unpack_format=">%dL" % buff_size
index=0
for y in struct.unpack(unpack_format,data_buff):
buff1=struct.pack("I",y)
buff2=struct.unpack("f",buff1)[0]
if (index % 2 == 0):
data[counter][1][peak_counter][0]=float(buff2)
else:
data[counter][1][peak_counter][1]=float(buff2)
peak_counter+=1
index+=1
counter+=1
def numpy_array(data, peaks):
"""Fills the NumPy array 'data' with m/z-intensity values acquired
from b64 decoding and unpacking the binary string read from the
mzXML file, which is stored in the list 'peaks'.
The m/z values are assumed to be ordered without validating this
assumption.
Note: This function uses multi-processing
"""
processors=mp.cpu_count()
pool = mp.Pool(processes=processors)
chunk_size=int(len(peaks)/processors)
map_parameters=[]
for i in range(processors):
counter = i*chunk_size
chunk=peaks[i*chunk_size:(i+1)*chunk_size-1]
map_parameters.append((data,chunk,counter))
pool.map(decode,map_parameters)
到目前为止，这个最新版本“有效”，它填充了进程中的数组（其中数组包含值），但是一旦所有进程完成访问数组，只会产生零值，因为每个进程都会获取数组的本地副本。
