为什么我的 Python 程序平均每个进程只占用 33% 的 CPU？ 如何让 Python 使用所有可用的 CPU？

Question

I use Python 2.5.4. My computer: CPU AMD Phenom X3 720BE, Mainboard 780G, 4GB RAM, Windows 7 32 bit.

I use Python threading but can not make every python.exe process consume 100% CPU. Why are they using only about 33-34% on average?.

I wish to direct all available computer resources toward these large calculations so as to complete them as quickly as possible.

EDIT: Thanks everybody. Now I'm using Parallel Python and everything works well. My CPU now always at 100%. Thanks all!

Answer 1

It appears that you have a 3-core CPU. If you want to use more than one CPU core in native Python code, you have to spawn multiple processes. (Two or more Python threads cannot run concurrently on different CPUs)

As R. Pate said, Python's multiprocessing module is one way. However, I would suggest looking at Parallel Python instead. It takes care of distributing tasks and message-passing. You can even run tasks on many separate computers with little change to your code.

Using it is quite simple:

import pp

def parallel_function(arg):
    return arg

job_server = pp.Server() 

# Define your jobs
job1 = job_server.submit(parallel_function, ("foo",))
job2 = job_server.submit(parallel_function, ("bar",))

# Compute and retrieve answers for the jobs.
print job1()
print job2()

Answer 2

Try the multiprocessing module, as Python, while it has real, native threads, will restrict their concurrent use while the GIL is held. Another alternative, and something you should look at if you need real speed, is writing a C extension module and calling functions in it from Python. You can release the GIL in those C functions.

Also see David Beazley 's Mindblowing GIL .

Answer 3

Global Interpreter Lock

The reasons of employing such a lock include:

 * increased speed of single-threaded programs (no necessity to acquire or release locks on all data structures separately) * easy integration of C libraries that usually are not thread-safe. 

Applications written in languages with a GIL have to use separate processes (ie interpreters) to achieve full concurrency, as each interpreter has its own GIL.

Answer 4

From CPU usage it looks like you're still running on a single core. Try running a trivial calculation with 3 or more threads with same threading code and see if it utilizes all cores. If it doesn't, something might be wrong with your threading code.

Answer 5

那Stackless Python怎么样？

Answer 6

您可能在其他地方遇到瓶颈问题，例如硬盘驱动器（分页）或内存访问。

Answer 7

You should perform some Operating System and Python monitoring to determine where the bottle neck is.

Here is some info for windows 7:

Performance Monitor : You can use Windows Performance Monitor to examine how programs you run affect your computer's performance, both in real time and by collecting log data for later analysis. ( Control Panel-> All Control Panel Items->Performance Information and Tools-> Advanced Tools- > View Performance Monitor)

Resource Monitor : Windows Resource Monitor is a system tool that allows you to view information about the use of hardware (CPU, memory, disk, and network) and software (file handles and modules) resources in real time. You can use Resource Monitor to start, stop, suspend, and resume processes and services. ( Control Panel-> All Control Panel Items->Performance Information and Tools-> Advanced Tools- > View Resource Monitor )

Answer 8

I solved the problems that led me to this post by running a second script manually. This post helped me run multiple python scripts at the same time .

I managed to execute in the newly-opened terminal window typing a command there. Not as convenient as shift + enter but does the job.