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Welcome to the PyPubSub / PMS project, an implementation of a pure python networked publish subscribe mechanism. Central is a message broker which dispatches messages between applications.

[IPython-user] ipython1 and farm tasking Brian Granger ellisonbg.net@gmail.... Wed Feb 27 16:29:03 CST 2008 Previous message: [IPython-user] ipython1 and farm tasking Next message: [IPython-user] yet another leopard/readline question Messages sorted by: [ date ] [ thread ] [ subject ] [ author ] Alex, First, I would suggest updating your ipython1 install from our svn repository. We are about to push out a major new version and the documentation is _much_ better. Also, there are many new features that will hopefully help you. Here is a simple example (using the latest svn of ipython1): In [1]: from ipython1.kernel import client In [2]: mec = client.MultiEngineClient(('127.0.0.1',10105)) In [3]: tc = client.TaskClient(('127.0.0.1',10113)) In [4]: def fold_package(x): ...: return 2.0*x ...: In [5]: mec.push_function(dict(fold_package=fold_package)) Out[5]: [None, None, None, None] In [6]: tasks = [client.Task("y=fold_package(x)",push={'x':x},pull=('y',)) for x in range(128)] In [7]: task_ids = [tc.run(t) for t in tasks] In [8]: tc.barrier(task_ids) In [9]: task_results = [tc.get_task_result(tid) for tid in task_ids] In [10]: results = [tr.ns.y for tr in task_results] In [11]: print results [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0, 24.0, 26.0, 28.0, 30.0, 32.0, 34.0, 36.0, 38.0, 40.0, 42.0, 44.0, 46.0, 48.0, 50.0, 52.0, 54.0, 56.0, 58.0, 60.0, 62.0, 64.0, 66.0, 68.0, 70.0, 72.0, 74.0, 76.0, 78.0, 80.0, 82.0, 84.0, 86.0, 88.0, 90.0, 92.0, 94.0, 96.0, 98.0, 100.0, 102.0, 104.0, 106.0, 108.0, 110.0, 112.0, 114.0, 116.0, 118.0, 120.0, 122.0, 124.0, 126.0, 128.0, 130.0, 132.0, 134.0, 136.0, 138.0, 140.0, 142.0, 144.0, 146.0, 148.0, 150.0, 152.0, 154.0, 156.0, 158.0, 160.0, 162.0, 164.0, 166.0, 168.0, 170.0, 172.0, 174.0, 176.0, 178.0, 180.0, 182.0, 184.0, 186.0, 188.0, 190.0, 192.0, 194.0, 196.0, 198.0, 200.0, 202.0, 204.0, 206.0, 208.0, 210.0, 212.0, 214.0, 216.0, 218.0, 220.0, 222.0, 224.0, 226.0, 228.0, 230.0, 232.0, 234.0, 236.0, 238.0, 240.0, 242.0, 244.0, 246.0, 248.0, 250.0, 252.0, 254.0] Or if you don't need load balancing: # This sends the fold_package function for you! results = mec.map(fold_package, range(128)) Let us know if you run into other problems. Cheers, Brian

Pypar does not require the Python interpreter to be modified or recompiled: Parallel python programs use the standard Python and need merely import the pypar module. This means for example that you can upgrade Python independently of your parallel codes.

Pypar is an efficient but easy-to-use module that allows programs/scripts written in the Python programming language to run in parallel on multiple processors and communicate using message passing. Pypar provides bindings to an important subset of the message passing interface standard MPI. Other Python MPI bindings available from other developers include: PyMPI, Scientific Python and pythonMPI.

Re: Kill process based on window name (win32) Thank you Roger. Your advice did the trick. For anyone interested, the basic code to terminate a process (politely) would be something like this (hwnd is retrieved using win32gui.EnumerateWindows): # Get the window's process id's t, p = win32process.GetWindowThreadProcessId(hwnd) # Ask window nicely to close win32gui.PostMessage(hwnd, win32con.WM_CLOSE, 0, 0) # Allow some time for app to close time.sleep(10) # If app didn't close, force close try: handle = win32api.OpenProcess(win32con.PROCESS_TERMINATE, 0, p) if handle: win32api.TerminateProcess(handle,0) win32api.CloseHandle(handle) except: pass:

PyACTS- http://www.pyacts.org -- PyACTS is a collection of Python Modules that are very useful to Parallel Computing in a High Performance Computing environment. This packages incorporates several modules like PyBLACS (allows communication data for Linear Algebra), PyPBLAS (distributed Matrix Operations) and PyScaLAPACK (solve linear systems and get the eigenvalue problems). These libraries are part of PyACTS project that provide interfaces to the ACTS Collection. Also is provided a parrallel interpreter for using this package that implements message-based parallel programming using MPI.

Document options Document options requiring JavaScript are not displayed Rate this pageHelp us improve this contentLevel: IntroductoryDavid Mertz (mertz@gnosis.cx), Applied Metaphysician, Gnosis Software, Inc. 01 Mar 2001Although users usually think of Python as a procedural and object-oriented language, it actually contains everything you need for a completely functional approach to programming. This article discusses general concepts of functional programming, and illustrates ways of implementing functional techniques in Python. We'd better start with the hardest question: "What is functional programming (FP), anyway?" One answer would be to say that FP is what you do when you program in languages like Lisp, Scheme, Haskell, ML, OCAML, Clean, Mercury, or Erlang (or a few others). That is a safe answer, but not one that clarifies very much. Unfortunately, it is hard to get a consistent opinion on just what FP is, even from functional programmers themselves. A story about elephants and blind men seems apropos here. It is also safe to contrast FP with "imperative programming" (what you do in languages like C, Pascal, C++, Java, Perl, Awk, TCL, and most others, at least for the most part).