The latest development release of ScientificPython (2.7), which is available now at http://sourcesup.cru.fr/projects/scientific-py/ contains a new module for parallel computing using a master-slave model: a master process defines computational tasks, and any number of slave processes execute them. The main advantages of this approach are simplicity and the possibility to run a variable number of slave processes. Slave processes can even be killed during execution; as long as at least one slave process remains, the computation will terminate correctly. Here is a very simple example that illustrates parallel computation using the new module: from Scientific.DistributedComputing.MasterSlave \ import MasterProcess, SlaveProcess from Scientific import N import sys class Master(MasterProcess): def run(self): for i in range(5): # request a computation task of type "sqrt" task_id = self.requestTask("sqrt", float(i)) for i in range(5): # retrieve the result from a "sqrt" computation task task_id, tag, result = self.retrieveResult("sqrt") print result class SquareRoot(SlaveProcess): # implement the computation task "sqrt" def do_sqrt(self, x): return (x, N.sqrt(x)) if len(sys.argv) == 2 and sys.argv[1] == "master": master = True elif len(sys.argv) == 2 and sys.argv[1] == "slave": master = False else: print "Argument must be 'master' or 'slave'" raise SystemExit if master: process = Master("demo") else: process = SquareRoot("demo") process.start() The communication between processes is handled by Pyro (pyro.sourceforge.net), which has to be installed as well. Comments are welcome! -- --------------------------------------------------------------------- Konrad Hinsen Centre de Biophysique Moléculaire, CNRS Orléans Synchrotron Soleil - Division Expériences Saint Aubin - BP 48 91192 Gif sur Yvette Cedex, France Tel. +33-1 69 35 97 15 E-Mail: hinsen@cnrs-orleans.fr ---------------------------------------------------------------------