如何使用multiprocessing解决embarrassingly parallel problems ?
令人尴尬的并行问题通常由三个基本部分组成:
- 读取输入数据(来自文件、数据库、tcp 连接等)。
- 对输入数据运行计算，其中每个计算独立于任何其他计算。
- 写入计算结果(到文件、数据库、tcp 连接等)。
我们可以在两个维度上并行化程序:
- 第 2 部分可以在多个内核上运行，因为每个计算都是独立的；处理顺序无关紧要。
- 每个部分都可以独立运行。第 1 部分可以将数据放入输入队列，第 2 部分可以将数据从输入队列中拉出并将结果放入输出队列，第 3 部分可以将结果从输出队列中拉出并写出。
这似乎是并发编程中最基本的模式，但我仍然无法解决它，所以让我们编写一个规范的示例来说明如何使用多处理来完成此操作。
这是一个示例问题:给定一个 CSV file以整数行作为输入，计算它们的总和。把问题分成三部分，都可以并行运行:
- 将输入文件处理成原始数据(整数列表/可迭代)
- 并行计算数据的总和
- 输出总和
以下是解决这三个任务的传统单进程绑定(bind) Python 程序:
#!/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:])
让我们使用这个程序并重写它以使用多处理来并行化上述三个部分。下面是这个新的并行程序的框架，需要充实以解决评论中的部分:
#!/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，以及another piece of code that can generate example CSV files用于测试目的，可以是 found on github .
如果您能提供任何有关并发专家如何解决此问题的见解，我将不胜感激。
以下是我在考虑这个问题时遇到的一些问题。解决任何/所有问题的奖励积分:
- 我应该有子进程来读取数据并将其放入队列中，还是主进程可以在读取所有输入之前不阻塞地执行此操作？
- 同样，我应该有一个子进程来从已处理队列中写出结果，还是主进程可以这样做而不必等待所有结果？
- 我应该使用 processes pool求和运算？
- 如果是，我应该在池上调用什么方法来让它开始处理进入输入队列的结果，而不阻塞输入和输出进程？ apply_async() ? map_async() ? imap() ? imap_unordered() ?
- 假设我们不需要在数据进入时从输入和输出队列中抽出，而是可以等到所有输入都被解析并计算出所有结果(例如，因为我们知道所有输入和输出都适合系统内存)。我们是否应该以任何方式更改算法(例如，不要在 I/O 的同时运行任何进程)？
最佳答案
我的解决方案有一个额外的花里胡哨，以确保输出的顺序与输入的顺序相同。我使用 multiprocessing.queue 在进程之间发送数据，发送停止消息，以便每个进程都知道退出检查队列。我认为来源中的评论应该清楚地说明发生了什么，但如果不让我知道。
#!/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:])
关于python - 使用 Python 多处理解决令人尴尬的并行问题，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/2359253/