Andrew McNabb <[email protected]> added the comment: I'm seeing something similar to this with numpypy in PyPy 1.9.0. I'm basically seeing an explosion of memory usage, but the function seems simple enough to make me doubt that it's lazy evaluation. Is there an easy way to tell if the problem I'm seeing is caused by lazy evaluation or by something else? Is there an easy way to disable lazy evaluation at run-time? I don't want to open a new report unless I'm sure that it's different from this bug. Thanks.
---------- nosy: +amcnabb status: resolved -> chatting ________________________________________ PyPy bug tracker <[email protected]> <https://bugs.pypy.org/issue1145> ________________________________________ _______________________________________________ pypy-issue mailing list [email protected] http://mail.python.org/mailman/listinfo/pypy-issue
