Hi, this is definitely doable, but needs some work. Here are the tasks I identified:
- copy "memmap.py" from the official numpy, and write a unit test (there is a nice docstring in the previous file) - add support in numpy for buffers with a fixed address (in interp-level terms: a RWBuffer with a get_raw_address() method) - have buffer(mmap) return such a buffer, very very similar to array.ArrayType. The three tasks are quite independent, not too difficult, and could be a nice start for newcomers... I'll be happy to help. 2013/6/14 Mike Beller <[email protected]> > Hello > > Do you think it is likely that the memap capabilities of numpy will find > their way in to numpypy any time soon? > > It seems to me that some people think memap is a relatively unimportant > aspect of numpy. But I do not think so. Because of the way the linux IO > subsystem and virtual memory systems interact, memap enables numpy to have > high performance access to very large data sets -- it helps make numpy > relevant to "Big Data". > > The code to allow numpy to support memap doesn't seem very large. But, > while I have tried reading through the code, I really can not tell whether > the same is true for numpypy, or whether it is a large endeavor (for > example, due to some kind of pypy memory management architectural issue). > > I'm interested in any input on this. > > Mike Beller > > > > _______________________________________________ > pypy-dev mailing list > [email protected] > http://mail.python.org/mailman/listinfo/pypy-dev > > -- Amaury Forgeot d'Arc
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