You guys are right. If I disable the gc it will use all the virtual RAM in my test.
The application I have been running these tests for is a port of a program written in a LISP-based tool running on Unix. It does a mass of stress calculations. The port has been written using a python-based toolkit I am responsible for. This toolkit offers much of the same functionlity as the LISP tool. It is based around the use of demand-driven/declarative programming. When the porting project started no one realised just how much memory the heaviest of the test cases used. It uses 40+ GB on an HP Unix machine. It is easy to see now that the port should have been written differently, but it is essentially complete now. This has lead me to see if a hardware solution can be found using 64 bit windows machnes. I will try running one the tests next to see what impact disabling the gc will have. Thanks, Rob. On 9 December 2010 22:44, John Nagle <na...@animats.com> wrote: > On 12/8/2010 10:42 PM, Dennis Lee Bieber wrote: > >> On Wed, 8 Dec 2010 14:44:30 +0000, Rob Randall<rob.randa...@gmail.com> >> declaimed the following in gmane.comp.python.general: >> >> I am trying to understand how much memory is available to a 64 bit python >>> process running under Windows XP 64 bit. >>> >>> When I run tests just creating a series of large dictionaries containing >>> string keys and float values I do not seem to be able to grow the process >>> beyond the amount of RAM present. >>> >> > If you get to the point where you need multi-gigabyte Python > dictionaries, you may be using the wrong tool for the job. > If it's simply that you need to manage a large amount of data, > that's what databases are for. > > If this is some super high performance application that needs to keep a > big database in memory for performance reasons, CPython > is probably too slow. For that, something like Google's BigTable > may be more appropriate, and will scale to terabytes if necessary. > > John Nagle > > -- > http://mail.python.org/mailman/listinfo/python-list >
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