Re: multithreading concept
sturlamolden wrote: [...] If you want to utilize the computing power of multiple CPUs, you should use multiple processes instead of threads. On Python this is mandatory due to the GIL. In any other language it it highly recommended. The de-factor standard for parallel multiprocessing (MPI) uses multiple processes, even on SMPs. That doesn't really work in Python. There have been projects to allow Pythonic coordination of processes -- POSH had some good ideas -- but none have reached fruition. There's nothing like a close thing to a good defacto standard in the area. Microsoft's Win32 threads can claim to get as close as anything. -- --Bryan -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
On 8 Mar, 10:48, Bryan Olson [EMAIL PROTECTED] wrote: That doesn't really work in Python. There have been projects to allow Pythonic coordination of processes -- POSH had some good ideas -- but none have reached fruition. What makes all of the following not Pythonic...? http://wiki.python.org/moin/ParallelProcessing Things like the CSP paradigm have sort of made their way into the Python language itself, via enhancements to the yield keyword, which has the dubious distinction of being a keyword which appears to return a value. I'm sure one could define Pythonic as being you can write code like you do now (but not like any of the ways encouraged by the aforementioned solutions) and it just works over multiple processors/ cores, but that's a view which is somewhat detached from the practicalities (and favoured practices) of concurrent programming, especially given the few guarantees Python would be able to provide to make such a thing work effectively. Paul -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
Paul Boddie [EMAIL PROTECTED] writes: What makes all of the following not Pythonic...? http://wiki.python.org/moin/ParallelProcessing I'd say mainly that they don't allow sharing data between processes except through expensive IPC mechanisms involving system calls. I'm sure one could define Pythonic as being you can write code like you do now (but not like any of the ways encouraged by the aforementioned solutions) and it just works over multiple processors/ cores, but that's a view which is somewhat detached from the practicalities (and favoured practices) of concurrent programming, especially given the few guarantees Python would be able to provide to make such a thing work effectively. Really, the existence of the GIL comes as an unpleasant surprise to progrmamers used to multi-threaded programming in other languages whose synchronization features outwardly look about the same as Python's. Somehow those other languages manage to use multiple CPU's based on those features, without needing a GIL. We are looking at a Python implementation wart, not practicalities inherent in the nature of concurrency. -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
Carl J. Van Arsdall wrote: Not necessarily, if he's on a full duplex ethernet connection, then there is some parallelity he can take advantage of. He has upstream and downstream. Partly agreed. There is one bus to the network device, and CPU should be very much faster than the network device itself, so I estimate there'll be no gain. Regards, Björn -- BOFH excuse #353: Second-system effect. -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
I am sorry if I sound foolish. Suppose I split my Net application code using parallel python into several processes based upon the number of CPU available. That means a single socket descriptor is distributed across all processes. Is parallelity can be acheived using the processes send/recv on the single socket multiplexed across all the processes.. I haven't tried it yet - would like to have any past experience related to this. - Original Message - From: Carl J. Van Arsdall [EMAIL PROTECTED] To: python-list@python.org Sent: Thursday, February 08, 2007 3:44 PM Subject: Re: multithreading concept Bjoern Schliessmann wrote: [snip] What makes you think that'll be faster? Remember: - If you have one CPU, there is no parallelity at all. - If you do have multiple CPUs but only one network device, there is no parallel networking. Not necessarily, if he's on a full duplex ethernet connection, then there is some parallelity he can take advantage of. He has upstream and downstream. -c -- Carl J. Van Arsdall [EMAIL PROTECTED] Build and Release MontaVista Software -- http://mail.python.org/mailman/listinfo/python-list -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
S.Mohideen [EMAIL PROTECTED] writes: Suppose I split my Net application code using parallel python into several processes based upon the number of CPU available. That means a single socket descriptor is distributed across all processes. Is parallelity can be acheived using the processes send/recv on the single socket multiplexed across all the processes.. I haven't tried it yet - would like to have any past experience related to this. In Linux, you can open the socket before forking and then use it in the child processes; there is also a way to pass open sockets from one process to another, but the Python lib currently does not support that feature. It's worth adding and there's an open RFE for it, but it hasn't been important enough that anyone's bothered coding it so far. -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
On Feb 9, 4:00 pm, S.Mohideen [EMAIL PROTECTED] wrote: I am sorry if I sound foolish. Suppose I split my Net application code using parallel python into several processes based upon the number of CPU available. That means a single socket descriptor is distributed across all processes. Is parallelity can be acheived using the processes send/recv on the single socket multiplexed across all the processes.. I haven't tried it yet - would like to have any past experience related to this. Is CPU or network the speed limiting factor in your application? There are two kinds of problems: You have a 'CPU-bound problem' if you need to worry about 'flops'. You have an 'I/O bound' problem if you worry about 'bits per second'. If your application is I/O bound, adding more CPUs to the task will not help. The network connection does not become any faster just because two CPUs share the few computations that need to be performed. Python releases the GIL around all i/o operations in the standard library, such as reading from a socket or writing to socket. If this is what you need to 'parallelize', you can just use threads and ignore the GIL. Python's threads can handle concurrent I/O perfectly well. Remember that Google and YouTube use Python, and the GIL is not a show stopper for them. The GIL locks the process to one CPU. You need to get around this if the power of one CPU or CPU core limits the speed of the application. This can be the case in e.g. digital image processing, certain computer games, and scientific programming. I have yet to see a CPU- bound 'Net application', though. If you are running Windows: take a look at the CPU usage in the task manager. Does it say that one of the CPUs is running at full speed for longer periods of time? If not, there is noting to gained from using multiple CPUs. -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
Bjoern Schliessmann wrote: [snip] What makes you think that'll be faster? Remember: - If you have one CPU, there is no parallelity at all. - If you do have multiple CPUs but only one network device, there is no parallel networking. Not necessarily, if he's on a full duplex ethernet connection, then there is some parallelity he can take advantage of. He has upstream and downstream. -c -- Carl J. Van Arsdall [EMAIL PROTECTED] Build and Release MontaVista Software -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
On Feb 7, 2:53 am, S.Mohideen [EMAIL PROTECTED] wrote: Python is praised about - me too. But at one instance it fails. It fails to behave as a true multi-threaded application. That means utilizing all the CPUs parallely in the SMP efficiently stays as a dream for a Python Programmer. This has been discussed to death before. Win32 threads and pthreads (which is what Python normally uses, depending on the platform) are designed to stay idle most of the time. They are therefore not a tool for utilizing the power of multiple CPUs, but rather make certain kind of programming tasks easier to program (i.e. non-blocking I/O, responsive UIs). The GIL is not a problem in this context. If threads stay idle most of the time, the GIL does not harm. If you want to utilize the computing power of multiple CPUs, you should use multiple processes instead of threads. On Python this is mandatory due to the GIL. In any other language it it highly recommended. The de-factor standard for parallel multiprocessing (MPI) uses multiple processes, even on SMPs. Anyone with serious intentions of using multiple processors for parallel computing should use multiple processes and fast IPC - not multiple threads, shared memory and synchronization objects - even if the language is plain C. With multiple threads, a lot of time is wasted doing context switches and book keeping for the thread synchronization. In addition, obscure and often very difficult to identify bugs are introduced. There are a Python binding for MPI (mpi4py) and a similar pure Python project (Parallel Python) that will take care of all these details for you. Discussion threads say its due to GIL - global interpreter lock. But nobody has mentioned any alternative to that apart from suggestions like Code it in C and POSH (http://poshmodule.sf.net). Is there any other way we can make Python programs really multithreaded in real sense. As mentioned, use MPI or Parallel Python. MPI is by far the more mature, but Parallel Python could be easier for a pythoneer. Multithreading has different use. -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
On 7 Feb, 02:53, S.Mohideen [EMAIL PROTECTED] wrote: Python is praised about - me too. But at one instance it fails. It fails to behave as a true multi-threaded application. That means utilizing all the CPUs parallely in the SMP efficiently stays as a dream for a Python Programmer. Take a look at the Python Wiki for information on parallel processing with Python: http://wiki.python.org/moin/ParallelProcessing Pure CPython code may not be able to use more than one CPU merely through the use of threads (Jython and IronPython are different, though), but using all the CPUs or cores in an SMP system is not exactly a mere dream, as many of the projects listed on the above page demonstrate. Paul -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
sturlamolden wrote: On Feb 7, 2:53 am, S.Mohideen [EMAIL PROTECTED] wrote: This has been discussed to death before. Win32 threads and pthreads (which is what Python normally uses, depending on the platform) are designed to stay idle most of the time. They are therefore not a tool for utilizing the power of multiple CPUs, but rather make certain kind of programming tasks easier to program (i.e. non-blocking I/O, responsive UIs). Multithread compute-bound programs on multiple CPUs are how you get heavy number-crunching work done on multiprocessors. Of course, that's not something you use Python for, at least not until it gets a real compiler. It's also the direction games are going. The XBox 360 forced game developers to go that way, since it's a 3-CPU shared memory multiprocessor. That translates directly to multicore desktops and laptops. I went to a talk at Stanford last week by one of Intel's CPU architects, and he said we're going have hundreds of CPUs per chip reasonably soon. Python needs to get ready. John Nagle -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
John Nagle wrote: sturlamolden wrote: On Feb 7, 2:53 am, S.Mohideen [EMAIL PROTECTED] wrote: This has been discussed to death before. Win32 threads and pthreads (which is what Python normally uses, depending on the platform) are designed to stay idle most of the time. They are therefore not a tool for utilizing the power of multiple CPUs, but rather make certain kind of programming tasks easier to program (i.e. non-blocking I/O, responsive UIs). Multithread compute-bound programs on multiple CPUs are how you get heavy number-crunching work done on multiprocessors. Of course, that's not something you use Python for, at least not until it gets a real compiler. It's also the direction games are going. The XBox 360 forced game developers to go that way, since it's a 3-CPU shared memory multiprocessor. That translates directly to multicore desktops and laptops. I went to a talk at Stanford last week by one of Intel's CPU architects, and he said we're going have hundreds of CPUs per chip reasonably soon. Python needs to get ready. Define Python. Does it include you? What does it need to do to get ready. How do you plan to help? regards Steve -- Steve Holden +44 150 684 7255 +1 800 494 3119 Holden Web LLC/Ltd http://www.holdenweb.com Skype: holdenweb http://del.icio.us/steve.holden Blog of Note: http://holdenweb.blogspot.com See you at PyCon? http://us.pycon.org/TX2007 -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
On Feb 7, 6:17 pm, John Nagle [EMAIL PROTECTED] wrote: Multithread compute-bound programs on multiple CPUs are how you get heavy number-crunching work done on multiprocessors. In the scientific community, heavy CPU-bound tasks are either parallelized using MPI and/or written in Fortran 90/95 and parallelized using an expensive vectorizing compiler. Of course, that's not something you use Python for, at least not until it gets a real compiler. That is also not correct: 1. Using Python does not change the complexity of the algorithm. Big-O is still the same, and Big-O is still the main determinant of performance. 2. I value my own time more than extra CPU cycles (and so does those who pay my salary). If Python is to slow, it is less expensive to compensate by using more CPUs than using a less productive language like Java or C++. 3. Only isolated bottlenecks really gain from being statically compiled. These are usually very small parts of the program. They can be identified with a profiler (intuition usually do not work very well here) and rewritten in Pyrex, Fortran 95, C or assembly. 4. There is NumPy and SciPy, which can make Python fast enough for most CPU-bound tasks. http://www.scipy.org/PerformancePython 5. Premature optimization is the root of all evil in computer science. (Donald Knuth) 6. Pyrex (the compiler you asked for) does actually exist. C and Fortran compilers can produce efficient code because they know the type of each variable. We have do a Python compiler that can do the same thing. It is called 'Pyrex' and extends Python with static types. Pyrex can therefore produce code that are just as efficient as hand-tuned C (see the link above). One can take the bad-performing Python code, add type declarations to the variables that Pyrex needs to generate efficient code (but all variables need not be declared), and leave the rest to the compiler. But this is only required for very small portions of the code. Transforming time-critical Python code to Pyrex is child's play. First make it work, then make it fast. At the University of Oslo, the HPC centre has been running Python courses for its clients. Python does not perform any worse than C or Fortran, one just has to know (1) how to use it, (2) when to use it, and (3) when not to use it. 99% of benchmarks showing bad performance with Python is due to programmers not understanding which operations are expensive in interpreted languages, and trying to use Python as if it were C++. The typical example would be code that use a loop instead of using the built-in function 'map' or a vectorized array expression with NumPy. It's also the direction games are going. I believe that is due to ignorance. Threads are implemented to be in an idle blocking state 99% of the time. The XBox 360 forced game developers to go that way, since it's a 3-CPU shared memory multiprocessor. That translates directly to multicore desktops and laptops. MPI works on SMPs. MPI does not use threads on SMPs because it performs worse than using multiple processes. -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
sturlamolden [EMAIL PROTECTED] writes: On Feb 7, 6:17 pm, John Nagle [EMAIL PROTECTED] wrote: [...] MPI does not use threads on SMPs because it performs worse than using multiple processes. I fail to see how threads in general could perform worse than processes. I do understand that processes are inherently more safe/secure, but when it comes to speed I really can't imagine why it could happen that threads perform worse (poor threads implementation and programming practices aside). Care to give some references? -- Sergei. -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
On Feb 7, 8:03 pm, Sergei Organov [EMAIL PROTECTED] wrote: I fail to see how threads in general could perform worse than processes. I do understand that processes are inherently more safe/secure, but when it comes to speed I really can't imagine why it could happen that threads perform worse (poor threads implementation and programming practices aside). Care to give some references? I believe Nick Maclaren explained that to you (and me) on January 10 and 11 this group. As far as I have understood the issue, it has to do with poor threads implementations. Look that up on Google groups and re-read the discussion (or ask Nick Maclaren as he is far more competent than me). http://groups.google.com/group/comp.lang.python/browse_frm/thread/332083cdc8bc44b -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
Paul Boddie wrote: [snip] Take a look at the Python Wiki for information on parallel processing with Python: http://wiki.python.org/moin/ParallelProcessing What a great resource! That one is book marked for sure. I was wondering if anyone here had any opinions on some of the technologies listed in there. I've used a couple, but there are some that I've never seen before. In particular, has anyone used rthread before? It looks like something I may use (now orwhen it matures), are there opinions on it? Under the cluster computing section, has anyone tried any of the other technologies? I've only used Pyro and i love it, but I'd like opinions and experiences with other technologies if anyone has anything to say. -c -- Carl J. Van Arsdall [EMAIL PROTECTED] Build and Release MontaVista Software -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
I would like to add my problem in this thread. I have a network application in Python which sends and recv using a single socket. There is a dictionary on which I store/read data values. I want to seperate the send and recv functionality on two different processes so that the parallel execution becomes fast. Is there any way to do so, so that the Dict's consitency is not lost(able to read write) and also the performance improves. I am looking upon the MPI4Py module to see if it does the job for me. Any ideas would be appreciated. - Original Message - From: Sergei Organov [EMAIL PROTECTED] To: python-list@python.org Sent: Wednesday, February 07, 2007 1:03 PM Subject: Re: multithreading concept sturlamolden [EMAIL PROTECTED] writes: On Feb 7, 6:17 pm, John Nagle [EMAIL PROTECTED] wrote: [...] MPI does not use threads on SMPs because it performs worse than using multiple processes. I fail to see how threads in general could perform worse than processes. I do understand that processes are inherently more safe/secure, but when it comes to speed I really can't imagine why it could happen that threads perform worse (poor threads implementation and programming practices aside). Care to give some references? -- Sergei. -- http://mail.python.org/mailman/listinfo/python-list -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
S.Mohideen wrote: I would like to add my problem in this thread. I have a network application in Python which sends and recv using a single socket. There is a dictionary on which I store/read data values. I want to seperate the send and recv functionality on two different processes so that the parallel execution becomes fast. Is there any way to do so, so that the Dict's consitency is not lost(able to read write) and also the performance improves. I am looking upon the MPI4Py module to see if it does the job for me. Any ideas would be appreciated. Well, from your description so far I think that MPI is going to be a bit of overkill. I think you should consider threads or processors with shared memory/objects (POSH). Then take a look at a producer/consumer program to see how it works, that should get you to where you need to go. HTH -carl -- Carl J. Van Arsdall [EMAIL PROTECTED] Build and Release MontaVista Software -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
S.Mohideen wrote: There is a dictionary on which I store/read data values. I want to seperate the send and recv functionality on two different processes so that the parallel execution becomes fast. What makes you think that'll be faster? Remember: - If you have one CPU, there is no parallelity at all. - If you do have multiple CPUs but only one network device, there is no parallel networking. Regards, Björn -- BOFH excuse #188: ..disk or the processor is on fire. -- http://mail.python.org/mailman/listinfo/python-list
Re: multithreading concept
On Feb 7, 1:53 am, S.Mohideen [EMAIL PROTECTED] wrote: Hi Folks, Python is praised about - me too. But at one instance it fails. It fails to behave as a true multi-threaded application. That means utilizing all the CPUs parallely in the SMP efficiently stays as a dream for a Python Programmer. Discussion threads say its due to GIL - global interpreter lock. But nobody has mentioned any alternative to that apart from suggestions like Code it in C and POSH (http://poshmodule.sf.net). Is there any other way we can make Python programs really multithreaded in real sense. Moin Actually their are a *lot* more suggestions discussions to be found. I myself move towards the parallel processing is difficult. If you think it's easy then your either lucky or theorising. Whilst it would be nice to have threads==native threads for completeness sake, I'm quit happy to run concurrent communicating processes, as on my machines the OS helps me to see what's happening to the processes, and stops processes trampling over shared data. -Paddy. -- http://mail.python.org/mailman/listinfo/python-list
