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Superpy distributes python programs across a cluster of machines or across multiple processors on a single machine. This is a coarse-grained form of parallelism in the sense that remote tasks generally run in separate processes and do not share memory with the caller. Key features of superpy include:
The ultimate vision for superpy is that you:
For smaller deployments, you can use superpy to take advantage of multiple processors on a single machine or multiple machines to maximize computing power. What makes superpy different than the many other excellent parallel processing packages already available for python? The superpy package is designed to allow sending jobs across a large number of machines (both Windows and LINUX). This requires the ability to monitor, debug, and otherwise get information about the status of jobs. While superpy is currently used in production for a number of different purposes, there are still many features we want to add. For a list of future plans and opportunities to help out or add to the discussion, please visit http://code.google.com/p/superpy/wiki/HelpImproveSuperpy. For a quick example of some of the the things superpy can do, check out http://code.google.com/p/superpy/wiki/Demos or in particular the demo application PyFog at http://code.google.com/p/superpy/wiki/PyFog. To install, you can use easy_install to try superpy via "easy_install superpy" or download a python egg from downloads. Of course, you will need python installed and if you are using windows, you should also install the python windows tools from http://sourceforge.net/projects/pywin32/files. See http://code.google.com/p/superpy/wiki/InstallFAQ if you have more questions about installation. |