Hello all,
I am in the process of writing a multithreading program and what I was
wondering is a sleep command in an executing function will affect the
threads below it? Here is a basic example of what I mean.
def main():
temp_var = True
while temp_var == True:
if
t = threading.Threa d( target = func1, args = "String") #note
this is probably non functional (example purposes for the question
only)
t.start()
temp_var = t.isAlive()
else:
print "This line should display a lot"
sleep(2)
def func1(s):
print s
so the question I was wondering is if the sleep will pause the t
thread as well as the main function or is the thread independat of the
main function sleep?
Thanks in advance.
29  3217 
Jeffrey Maitland wrote: I am in the process of writing a multithreading program and what I was
wondering is a sleep command in an executing function will affect the
threads below it?
Note that the "executing function" is itself running inside a thread,
specifically the "main thread". The other threads are not "below" it in
any particular sense, more like beside it. (Or, to put it another way,
there is no such concept of relative position with threads in Python.)
Here is a basic example of what I mean.
[snip code] so the question I was wondering is if the sleep will pause the t
thread as well as the main function or is the thread independat of the
main function sleep?
Your code is very close to working already... why don't you just run it
and observe how it works?
In any case, the answer is "no, time.sleep() affects only the current
thread".
-Peter
On Mon, 04 Jul 2005 18:36:07 -0400, Peter Hansen <pe***@engcorp. com>
declaimed the following in comp.lang.pytho n:
In any case, the answer is "no, time.sleep() affects only the current
thread".
Heck, it is one of the means of getting number-crunchers to give
up the CPU to allow other threads/processes to run <G>
-- =============== =============== =============== =============== == <
wl*****@ix.netc om.com | Wulfraed Dennis Lee Bieber KD6MOG <
wu******@dm.net | Bestiaria Support Staff <
=============== =============== =============== =============== == <
Home Page: <http://www.dm.net/~wulfraed/> <
Overflow Page: <http://wlfraed.home.ne tcom.com/> <
Hello all,
First off Thanks for the responses on the sleep() part I suspected as
much but I wasn't 100% sure.
To continue this I just want to pre thank anyone that contributes to
this thread(lol).
Ok here goes. The problem I have is I had an application
(wrote/co-wrote) that has a long run time dependant on some variables
passed to it (mainly accuracy variables, the more accurate the longer
the run time - makes sense). However in the hopes to speed it up I
decided to write a threaded version of the program to try and speed
it up. How ever what I am noticing is that the threaded version is
taking as long possibly longer to run. The thing is the threaded
version is running on an 8 ia-64 proccessor system and it seems to
only be using 2 or 3 porcessors at about 30% (fluxiates). My guess is
that 6 threads are running they are using 30% sprox each of a 2 given
CPUS.
What I would like to do is have say 1 thread use as much of a given
CPU as possible and if a new thread is started (added) that if a CPU
is available use it instead of using the same cpu. That way it should
speed the application up. The standalone (non-threaded) app uses 90+
% of a single cpu when in this part of the algorithm, that is why I
split and threaded that part of the algorithm there to try and speed
it up because this part is repeated several times. I can post
generic code of what I am doing but can't post the actuall code
because of the confidentially of it. Thanks again in advance for any
and all comments (even any spitefull ones)
Jeff
Jeffrey Maitland wrote: The problem I have is I had an application
(wrote/co-wrote) that has a long run time dependant on some variables
passed to it (mainly accuracy variables, the more accurate the longer
the run time - makes sense). However in the hopes to speed it up I
decided to write a threaded version of the program to try and speed
it up. How ever what I am noticing is that the threaded version is
taking as long possibly longer to run. The thing is the threaded
version is running on an 8 ia-64 proccessor system and it seems to
only be using 2 or 3 porcessors at about 30% (fluxiates). My guess is
that 6 threads are running they are using 30% sprox each of a 2 given
CPUS.
In many ways, Python is an incredibly bad choice for deeply
multithreaded applications. One big problem is the global interpreter
lock; no matter how many CPUs you have, only one will run python code
at a time. (Many people who don't run on multiple CPUs anyway try to
wave this off as a non-problem, or at least worth the tradeoff in terms
of a simpler C API, but with multicore processors coming from every
direction I think the "let's pretend we don't have a problem" approach
may not work much longer.)
If the GIL isn't an issue (and in your case it clearly is), you'll
quickly find that there's little support for debugging multithreaded
applications, and even less for profiling.
Sometimes running multiple processes is an acceptable workaround; if
not, good luck with the rewrite in Java or something else with real
thread support. (IIRC Jython doesn't have a GIL; that might be an
option too.)
Python is a great tool but if you really need good threading support
you will have to look elsewhere.
-Jonathan
On 2005-07-05, Jeffrey Maitland <je***********@ gmail.com> wrote: Ok here goes. The problem I have is I had an application
(wrote/co-wrote) that has a long run time dependant on some variables
passed to it (mainly accuracy variables, the more accurate the longer
the run time - makes sense). However in the hopes to speed it up I
decided to write a threaded version of the program to try and speed
it up. How ever what I am noticing is that the threaded version is
taking as long possibly longer to run. The thing is the threaded
version is running on an 8 ia-64 proccessor system and it seems to
only be using 2 or 3 porcessors at about 30% (fluxiates). My guess is
that 6 threads are running they are using 30% sprox each of a 2 given
CPUS.
Because of the global interpreter lock, a multi-threaded python
program does not take advantage of multiple processors. No
matter how many CPUs you have, only one thread is allowed to
run at any point in time.
Multi-threading in Python is useful for simplifying the
architecture of a program that has to do multiple independent
tasks, but it isn't useful for actually running multiple
threads in parallel.
--
Grant Edwards grante Yow! Inside, I'm already
at SOBBING!
visi.com
On Tue, 5 Jul 2005 10:57:18 -0400, Jeffrey Maitland
<je***********@ gmail.com> declaimed the following in comp.lang.pytho n: it up. How ever what I am noticing is that the threaded version is
taking as long possibly longer to run. The thing is the threaded
Well, threading does add some overhead in terms of the task swap
time.
version is running on an 8 ia-64 proccessor system and it seems to
only be using 2 or 3 porcessors at about 30% (fluxiates). My guess is
that 6 threads are running they are using 30% sprox each of a 2 given
CPUS.
What I would like to do is have say 1 thread use as much of a given
CPU as possible and if a new thread is started (added) that if a CPU
is available use it instead of using the same cpu. That way it should
Don't think you can do that with Python... The Python runtime
interpreter itself is running on a single processor. The second thing is
the infamous "global interpreter lock" (pull up the Python documentation
and do a search for that phrase). Basically, even if the threads could
be assigned to processors, this lock means only one thread can be
performing Python operations at a time -- a C-language number crunching
module /could/ release the lock, then do its number crunching in
parallel, reacquiring the lock when it finishes so it can return its
result(s) as Python objects.
You might get the results you want by not using threads, instead
spawning off completely new Python invocations assigned to other
processors.
-- =============== =============== =============== =============== == <
wl*****@ix.netc om.com | Wulfraed Dennis Lee Bieber KD6MOG <
wu******@dm.net | Bestiaria Support Staff <
=============== =============== =============== =============== == <
Home Page: <http://www.dm.net/~wulfraed/> <
Overflow Page: <http://wlfraed.home.ne tcom.com/> <
Jeffrey Maitland <je***********@ gmail.com> writes: What I would like to do is have say 1 thread use as much of a given
CPU as possible and if a new thread is started (added) that if a CPU
is available use it instead of using the same cpu. That way it should
speed the application up. The standalone (non-threaded) app uses 90+
% of a single cpu when in this part of the algorithm, that is why I
split and threaded that part of the algorithm there to try and speed
it up because this part is repeated several times. I can post
generic code of what I am doing but can't post the actuall code
because of the confidentially of it. Thanks again in advance for any
and all comments (even any spitefull ones)
This kind of fine control over CPU allocation is very
plaform-specific. Some don't allow it at all. If your platform does,
the details on how you go about doing it will vary depending on the
platform.
<mike
--
Mike Meyer <mw*@mired.or g> http://www.mired.org/home/mwm/
Independent WWW/Perforce/FreeBSD/Unix consultant, email for more information.
Thanks.
I was hoping that python would allow for the cpu threading such in
Java etc.. but I guess not. (from the answers,and other findings) I
guess I will have to write this part of the code in something such as
java or c or something that allows for it then I can either wrap it in
python or avoid python for this part of the app.
Thanks for all the help.
On 2005-07-05, Dennis Lee Bieber <wl*****@ix.net com.com> wrote: Don't think you can do that with Python... The Python runtime
interpreter itself is running on a single processor.
I don't see how that can be. Under Linux at least, the Python
threading module uses "real" OS threads, so there are multiple
instances of the interpreter, right? Generally all but one of
them will be blocked on the GIL, but there are still multiple
interpreter threads (which can be on multiple different CPUs).
Or is the Python interpreter actually doing the context
switches itself?
The second thing is the infamous "global interpreter lock"
(pull up the Python documentation and do a search for that
phrase).
The GIL is the issue.
Basically, even if the threads could be assigned to
processors,
Can somebody explani why they can't?
this lock means only one thread can be performing Python
operations at a time -- a C-language number crunching module
/could/ release the lock, then do its number crunching in
parallel, reacquiring the lock when it finishes so it can
return its result(s) as Python objects.
True. Python can execute C code in parallel, but not Python
code.
Tou might get the results you want by not using threads,
instead spawning off completely new Python invocations
assigned to other processors.
That should work, but managing the inter-process communication
and syncronization is a pain.
--
Grant Edwards grante Yow! If Robert Di Niro
at assassinates Walter Slezak,
visi.com will Jodie Foster marry
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