我正在使用Python和MPI4PY中的一些代码，这会抛出一个奇怪的错误。当我尝试运行下面的代码时，我会抛出以下内容：
ERROR; return code from pthread_create() is 11
Error detail: Resource temporarily unavailable
sh: fork: retry: Resource temporarily unavailable
/home/sfortney/anaconda/lib/python2.7/site-packages/numexpr/cpuinfo.py:40: UserWarning: [Errno 11] Resource temporarily unavailable
warnings.warn(str(e), UserWarning, stacklevel=stacklevel)
我从一个更简单，有效的MPI4PY脚本中扩展了这段代码，我在下面发布了这个脚本。从我对这个错误的研究看来，我创建的线程太多了。这对我来说似乎很奇怪，因为我没有调用任何线程，只是多个处理器（我的基本理解是线程是一个内核现象，如果我只是调用多个内核并做一件事就不会触及它如果不是这样，请抱歉。）
我无法理解为什么底部的代码能够完美运行，但紧接着使用相同结构的代码却没有。为什么下面的代码会遇到线程约束？在代码中它甚至调用多个线程？
我已经发布了以下整个代码，以便重现错误。如果它是相关的，我在32核LinuxBox上运行它。
#to run this call "mpiexec -n 10 python par_implement_wavefront.py" in terminal
from __future__ import division
import pandas as pd
import numpy as np
import itertools
import os
from itertools import chain, combinations
from operator import add
from collections import Counter
home="/home/sfortney"
np.set_printoptions(precision=2, suppress=True)
#choose dimensionality and granularity
dim=2
gran=5
from mpi4py import MPI
from mpi4py.MPI import ANY_SOURCE
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
command_buffer = np.zeros(3) # first entry is boolean, second is tuple with objective function inputs, third is array index
result_buffer=np.zeros(3) # first position is node,
if rank==0:
#defining all of our functions we will need on the root node first
#makes ax1 into the axes of an n dim array
def axis_fitter(arr, dim, gran, start=1, stop=101):
ax1=np.linspace(start,stop, num=gran)
for i in range(dim):
indexlist=[0]*dim
indexlist[i]= slice(None)
arr[indexlist]=ax1
return arr
#this is used to make the inital queues
#fix me to work with nan's!
def queue_init(arr):
queue=[]
queueposs=[]
queuedone=np.argwhere(arr >0).tolist()
return queue,queueposs,queuedone
#this is used in the queue updating function
def queue_sorter(queue):
queue.sort(key=lambda x: np.linalg.norm(np.array(x)))
# using the L1 norm
# queue.sort(key=lambda x: sum(x))
return queue
#this finds all the indicies to the "back" of our box
def back_index(dim):
standardbasis=[]
for i in range(dim):
vec=[0]*dim
vec[i]=vec[i]+1
standardbasis.append(vec)
powerset=[]
for z in chain.from_iterable(combinations(standardbasis,r) for r in range(len(standardbasis)+1)):
powerset.append(z)
powersetnew=[]
for i in range(len(powerset)):
powersetnew.append([sum(x) for x in zip(*list(powerset[i]))])
powersetnew.remove([])
powersetnew=[[i*(-1) for i in x] for x in powersetnew]
return powersetnew
#this takes a completed index and updates our queue of possible values
#as well as our done queue
def queue_update(queue,queueposs,queuedone, arr,dim,comp_idx=[0,0]):
queuedone.append(comp_idx)
if comp_idx==[0,0]:
init_index=[1]*dim
queue.append(init_index)
for i in range(dim):
poss_index=[1]*dim
poss_index[i]=2
queueposs.append(poss_index)
return queue,queueposs,queuedone
else:
queuedone.append(comp_idx)
try:
queueposs.remove(comp_idx)
except:
pass
for i in range(dim):
new_idx=comp_idx[:]
new_idx[i]=new_idx[i]+1
back_list=back_index(dim)
back_list2=[]
for x in back_list:
back_list2.append(list(np.add(np.asarray(new_idx),np.asarray(x))))
if set(tuple(x) for x in back_list2).issubset(set(tuple(x) for x in queuedone)):
queueposs.append(new_idx)
queueposs=list(set(tuple(x) for x in queueposs)-set(tuple(x) for x in queuedone))
queueposs=[list(x) for x in queueposs]
queueposs=queue_sorter(queueposs)
try:
for x in range(len(queueposs)):
queueappender=(queueposs).pop(x)
queue.append(queueappender)
except:
print "queueposs empty"
queue=queue_sorter(queue)
return queue,queueposs,queuedone
#this function makes it so we dont have to pass the whole array through MPI but only the pertinent information
def objectivefuncprimer(arr, queue_elem, dim):
inputs=back_index(dim)
inputs2=[]
for x in inputs:
inputs2.append(list(np.add(np.asarray(queue_elem),np.asarray(x))))
inputs3=[]
for x in range(len(inputs2)):
inputs3.append(arr[tuple(inputs2[x])])
return inputs3
#this function takes a value and an index and assigns the array that value at the index
def arrupdater(val,idx):
arr[tuple(idx)]=val
return arr, idx
#########Initializing
all_finished=False
#make our empty array
sizer=tuple([gran]*dim)
arr=np.zeros(shape=sizer)
nodes_avail=range(1, size) # 0 is not a worker
#assumes axes all start at same place
ax1=np.linspace(20,30, num=gran)
arr=axis_fitter(arr, dim, gran)
#fitting axes and initializing queues
arr=axis_fitter(arr, dim, gran, start=20, stop=30)
queue,queueposs,queuedone =queue_init(arr)
#running first updater
queue,queueposs,queuedone=queue_update(queue,queueposs,queuedone,arr,dim)
def sender(queue):
send_num=min(len(queue),len(nodes_avail))
for k in range(send_num):
node=nodes_avail.pop()
queue_elem=queue.pop(k)
command_buffer[0]=int(all_finished)
command_buffer[1]=queue_elem
command_buffer[2]=objectivefuncprimer(arr,queue_elem,dim)
comm.Send(command_buffer, dest=node)
while all_finished==False:
sender(queue)
comm.Recv(result_buffer,source=MPI.ANY_SOURCE)
arr,comp_idx=arrupdater(result_buffer[1],result_buffer[2])
queue,queueposs,queuedone=queue_update(queue,queueposs,queuedone,arr,dim,comp_idx)
nodes_avail.append(result_buffer[0])
if len(queuedone)==gran**2:
for n in range(1, size):
comm.Send(np.array([True,0,0]), dest=n)
all_finished=True
print arr
if rank>0:
all_finished_worker=False
#this test function will only work in 2d
def objectivefunc2d_2(inputs):
#this will be important for more complicated functions later
#backnum=(2**dim)-1
val=sum(inputs)
return val
while all_finished_worker==False:
comm.Recv(command_buffer, source=0)
all_finished_worker=bool(command_buffer[0])
if all_finished_worker==False:
result=objectivefunc2d_2(command_buffer[2])
# print str(result) +" from "+str(rank)
result_buffer=np.array([rank,result,command_buffer[1]])
comm.Send(result_buffer, dest=0)
这段代码有效，并且是我上面使用的基本结构，但是在一个更简单的例子中。
from __future__ import division
import numpy as np
import os
from itertools import chain, combinations
from mpi4py import MPI
from mpi4py.MPI import ANY_SOURCE
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
command_buffer = np.zeros(2) # first entry is boolean, rest is data
result_buffer=np.zeros(2) # first position is node, rest is data
if rank==0:
all_finished=False
nodes_avail=range(1, size) # 0 is not a worker
arr=[]
q=range(20)
def primer(q):
return int(all_finished),q
def sender(q):
send_num=min(len(q),len(nodes_avail))
for k in range(send_num):
node=nodes_avail.pop()
queue_init=q.pop()
command_buffer[0]=primer(queue_init)[0]
command_buffer[1]=primer(queue_init)[1]
comm.Send(command_buffer, dest=node)
while all_finished==False:
sender(q)
# update q
comm.Recv(result_buffer,source=MPI.ANY_SOURCE)
arr.append(result_buffer[1])
nodes_avail.append(result_buffer[0])
if len(arr)==20:
for n in range(1, size):
comm.Send(np.array([True,0]), dest=n)
all_finished=True
print arr
if rank>0:
all_finished_worker=False
while all_finished_worker==False:
comm.Recv(command_buffer, source=0)
all_finished_worker=bool(command_buffer[0])
if all_finished_worker==False:
result=command_buffer[1]*2
# print str(result) +" from "+str(rank)
result_buffer=np.array([rank,result])
comm.Send(result_buffer, dest=0)