如何使用multiprocessing来解决embarrassingly parallel problems？
令人尴尬的并行问题通常包括三个基本部分：
我们可以在两个方面并行化程序：
这似乎是并发编程中最基本的模式，但我仍然在尝试解决它时迷失了，所以让我们编写一个规范示例来说明如何使用多处理来完成。
以下是示例问题：给定一个带有整数行的CSV file作为输入，计算它们的总和。将问题分成三部分，这三部分可以并行运行：
下面是传统的，单进程绑定的Python程序，它解决了这三个任务：
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# basicsums.py
"""A program that reads integer values from a CSV file and writes out their
sums to another CSV file.
"""
import csv
import optparse
import sys
def make_cli_parser():
"""Make the command line interface parser."""
usage = "\n\n".join(["python %prog INPUT_CSV OUTPUT_CSV",
__doc__,
"""
ARGUMENTS:
INPUT_CSV: an input CSV file with rows of numbers
OUTPUT_CSV: an output file that will contain the sums\
"""])
cli_parser = optparse.OptionParser(usage)
return cli_parser
def parse_input_csv(csvfile):
"""Parses the input CSV and yields tuples with the index of the row
as the first element, and the integers of the row as the second
element.
The index is zero-index based.
:Parameters:
- `csvfile`: a `csv.reader` instance
"""
for i, row in enumerate(csvfile):
row = [int(entry) for entry in row]
yield i, row
def sum_rows(rows):
"""Yields a tuple with the index of each input list of integers
as the first element, and the sum of the list of integers as the
second element.
The index is zero-index based.
:Parameters:
- `rows`: an iterable of tuples, with the index of the original row
as the first element, and a list of integers as the second element
"""
for i, row in rows:
yield i, sum(row)
def write_results(csvfile, results):
"""Writes a series of results to an outfile, where the first column
is the index of the original row of data, and the second column is
the result of the calculation.
The index is zero-index based.
:Parameters:
- `csvfile`: a `csv.writer` instance to which to write results
- `results`: an iterable of tuples, with the index (zero-based) of
the original row as the first element, and the calculated result
from that row as the second element
"""
for result_row in results:
csvfile.writerow(result_row)
def main(argv):
cli_parser = make_cli_parser()
opts, args = cli_parser.parse_args(argv)
if len(args) != 2:
cli_parser.error("Please provide an input file and output file.")
infile = open(args[0])
in_csvfile = csv.reader(infile)
outfile = open(args[1], 'w')
out_csvfile = csv.writer(outfile)
# gets an iterable of rows that's not yet evaluated
input_rows = parse_input_csv(in_csvfile)
# sends the rows iterable to sum_rows() for results iterable, but
# still not evaluated
result_rows = sum_rows(input_rows)
# finally evaluation takes place as a chain in write_results()
write_results(out_csvfile, result_rows)
infile.close()
outfile.close()
if __name__ == '__main__':
main(sys.argv[1:])
让我们采用这个程序并重写它以使用多处理来并行化上面概述的三个部分。下面是这个新的并行化程序的框架，需要充实以解决注释中的部分：
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# multiproc_sums.py
"""A program that reads integer values from a CSV file and writes out their
sums to another CSV file, using multiple processes if desired.
"""
import csv
import multiprocessing
import optparse
import sys
NUM_PROCS = multiprocessing.cpu_count()
def make_cli_parser():
"""Make the command line interface parser."""
usage = "\n\n".join(["python %prog INPUT_CSV OUTPUT_CSV",
__doc__,
"""
ARGUMENTS:
INPUT_CSV: an input CSV file with rows of numbers
OUTPUT_CSV: an output file that will contain the sums\
"""])
cli_parser = optparse.OptionParser(usage)
cli_parser.add_option('-n', '--numprocs', type='int',
default=NUM_PROCS,
help="Number of processes to launch [DEFAULT: %default]")
return cli_parser
def main(argv):
cli_parser = make_cli_parser()
opts, args = cli_parser.parse_args(argv)
if len(args) != 2:
cli_parser.error("Please provide an input file and output file.")
infile = open(args[0])
in_csvfile = csv.reader(infile)
outfile = open(args[1], 'w')
out_csvfile = csv.writer(outfile)
# Parse the input file and add the parsed data to a queue for
# processing, possibly chunking to decrease communication between
# processes.
# Process the parsed data as soon as any (chunks) appear on the
# queue, using as many processes as allotted by the user
# (opts.numprocs); place results on a queue for output.
#
# Terminate processes when the parser stops putting data in the
# input queue.
# Write the results to disk as soon as they appear on the output
# queue.
# Ensure all child processes have terminated.
# Clean up files.
infile.close()
outfile.close()
if __name__ == '__main__':
main(sys.argv[1:])
这些代码段以及用于测试目的的another piece of code that can generate example CSV files可以是found on github。
我很感激您在此处了解并发大师将如何处理此问题。
以下是我在考虑此问题时遇到的一些问题。解决任何/所有问题的加分点：
答案 0 :(得分：65)
我的解决方案有一个额外的铃声和哨子，以确保输出的顺序与输入的顺序相同。我使用multiprocessing.queue来在进程之间发送数据，发送停止消息，以便每个进程知道退出检查队列。我认为来源中的评论应该清楚说明发生了什么，但如果不让我知道的话。
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# multiproc_sums.py
"""A program that reads integer values from a CSV file and writes out their
sums to another CSV file, using multiple processes if desired.
"""
import csv
import multiprocessing
import optparse
import sys
NUM_PROCS = multiprocessing.cpu_count()
def make_cli_parser():
"""Make the command line interface parser."""
usage = "\n\n".join(["python %prog INPUT_CSV OUTPUT_CSV",
__doc__,
"""
ARGUMENTS:
INPUT_CSV: an input CSV file with rows of numbers
OUTPUT_CSV: an output file that will contain the sums\
"""])
cli_parser = optparse.OptionParser(usage)
cli_parser.add_option('-n', '--numprocs', type='int',
default=NUM_PROCS,
help="Number of processes to launch [DEFAULT: %default]")
return cli_parser
class CSVWorker(object):
def __init__(self, numprocs, infile, outfile):
self.numprocs = numprocs
self.infile = open(infile)
self.outfile = outfile
self.in_csvfile = csv.reader(self.infile)
self.inq = multiprocessing.Queue()
self.outq = multiprocessing.Queue()
self.pin = multiprocessing.Process(target=self.parse_input_csv, args=())
self.pout = multiprocessing.Process(target=self.write_output_csv, args=())
self.ps = [ multiprocessing.Process(target=self.sum_row, args=())
for i in range(self.numprocs)]
self.pin.start()
self.pout.start()
for p in self.ps:
p.start()
self.pin.join()
i = 0
for p in self.ps:
p.join()
print "Done", i
i += 1
self.pout.join()
self.infile.close()
def parse_input_csv(self):
"""Parses the input CSV and yields tuples with the index of the row
as the first element, and the integers of the row as the second
element.
The index is zero-index based.
The data is then sent over inqueue for the workers to do their
thing. At the end the input process sends a 'STOP' message for each
worker.
"""
for i, row in enumerate(self.in_csvfile):
row = [ int(entry) for entry in row ]
self.inq.put( (i, row) )
for i in range(self.numprocs):
self.inq.put("STOP")
def sum_row(self):
"""
Workers. Consume inq and produce answers on outq
"""
tot = 0
for i, row in iter(self.inq.get, "STOP"):
self.outq.put( (i, sum(row)) )
self.outq.put("STOP")
def write_output_csv(self):
"""
Open outgoing csv file then start reading outq for answers
Since I chose to make sure output was synchronized to the input there
is some extra goodies to do that.
Obviously your input has the original row number so this is not
required.
"""
cur = 0
stop = 0
buffer = {}
# For some reason csv.writer works badly across processes so open/close
# and use it all in the same process or else you'll have the last
# several rows missing
outfile = open(self.outfile, "w")
self.out_csvfile = csv.writer(outfile)
#Keep running until we see numprocs STOP messages
for works in range(self.numprocs):
for i, val in iter(self.outq.get, "STOP"):
# verify rows are in order, if not save in buffer
if i != cur:
buffer[i] = val
else:
#if yes are write it out and make sure no waiting rows exist
self.out_csvfile.writerow( [i, val] )
cur += 1
while cur in buffer:
self.out_csvfile.writerow([ cur, buffer[cur] ])
del buffer[cur]
cur += 1
outfile.close()
def main(argv):
cli_parser = make_cli_parser()
opts, args = cli_parser.parse_args(argv)
if len(args) != 2:
cli_parser.error("Please provide an input file and output file.")
c = CSVWorker(opts.numprocs, args[0], args[1])
if __name__ == '__main__':
main(sys.argv[1:])
答案 1 :(得分：5)
我意识到我参加聚会有点晚了，但我最近发现了GNU parallel，并想表明用它来完成这个典型任务是多么容易。
cat input.csv | parallel ./sum.py --pipe > sums
这样的事情适用于sum.py：
#!/usr/bin/python
from sys import argv
if __name__ == '__main__':
row = argv[-1]
values = (int(value) for value in row.split(','))
print row, ':', sum(values)
并行将为sum.py中的每一行input.csv运行（当然并行），然后将结果输出到sums。显然比multiprocessing麻烦好
答案 2 :(得分：5)
来晚会......
joblib在多处理之上有一个层，以帮助制作并行for循环。除了非常简单的语法之外，它还为您提供了诸如延迟调度作业以及更好的错误报告等功能。
作为免责声明，我是joblib的原作者。
答案 3 :(得分：4)
旧学校。
p1.py
import csv
import pickle
import sys
with open( "someFile", "rb" ) as source:
rdr = csv.reader( source )
for line in eumerate( rdr ):
pickle.dump( line, sys.stdout )
p2.py
import pickle
import sys
while True:
try:
i, row = pickle.load( sys.stdin )
except EOFError:
break
pickle.dump( i, sum(row) )
p3.py
import pickle
import sys
while True:
try:
i, row = pickle.load( sys.stdin )
except EOFError:
break
print i, row
这是多处理最终结构。
python p1.py | python p2.py | python p3.py
是的，shell在操作系统级别将这些编织在一起。这对我来说似乎更简单，而且效果非常好。
是的，使用pickle（或cPickle）的开销略高。然而，简化似乎是值得的。
如果您希望文件名成为p1.py的参数，那么这很容易改变。
更重要的是，像下面这样的功能非常方便。
def get_stdin():
while True:
try:
yield pickle.load( sys.stdin )
except EOFError:
return
允许你这样做：
for item in get_stdin():
process item
这非常简单，但它 不允许您运行多个P2.py副本。
你有两个问题：扇出和扇入。 P1.py必须以某种方式扇出多个P2.py。并且P2.py必须以某种方式将他们的结果合并为单个P3.py。
老派的扇出方法是“推”式架构，非常有效。
理论上，从公共队列中拉出多个P2.py是资源的最佳分配。这通常是理想的，但它也是相当多的编程。编程真的有必要吗？或者循环处理是否足够好？
实际上，你会发现让P1.py做一个简单的“循环”处理多个P2.py可能是相当不错的。您已将P1.py配置为通过命名管道处理P2.py的 n 副本。 P2.py将从适当的管道中读取。
如果一个P2.py得到所有“最坏情况”数据并且落后了怎么办？是的，循环赛并不完美。但它只比一个P2.py好，你可以通过简单的随机化解决这个偏见。
从多个P2.py到一个P3.py的扇入仍然有点复杂。在这一点上，老派的做法不再有利。 P3.py需要使用select库读取多个命名管道以交错读取。
答案 4 :(得分：0)
也可能在第1部分中引入一些并行性。对于像CSV这样简单的格式可能不是问题，但如果输入数据的处理明显慢于读取数据，则可以读取更大的块，然后继续读取，直到找到“行分隔符”（ CSV情况下的换行符，但同样取决于读取的格式;如果格式足够复杂则不起作用）。
然后可以将这些块（每个块可能包含多个条目）耕种到一组并行进程中，从队列中读取作业，然后对它们进行解析和拆分，然后将其放置在第2阶段的队列中。