memory usage of numpy arrays
Hi. I am using numpy arrays and when constructing an array I get a "cannot allocate memory for thread-local data: ABORT" The array i'm constructing is zeros((numVars, 2, numVars, 2), dtype=float) Where numVars is at about 2000. I was expecting the memory usage to be 2000*2000*2*2*8Bytes=128.000.000Bytes=128MBytes So why is that happening? What am I missing? Thanks for any help. -- View this message in context: http://old.nabble.com/memory-usage-of-numpy-arrays-tp27788048p27788048.html Sent from the Numpy-discussion mailing list archive at Nabble.com.
On Thu, Mar 4, 2010 at 19:10, kaby <kackvogel@gmail.com> wrote:
Hi. I am using numpy arrays and when constructing an array I get a "cannot allocate memory for thread-local data: ABORT" The array i'm constructing is zeros((numVars, 2, numVars, 2), dtype=float) Where numVars is at about 2000.
I was expecting the memory usage to be 2000*2000*2*2*8Bytes=128.000.000Bytes=128MBytes
So why is that happening? What am I missing?
It's not having a problem allocating the memory itself. There are some things under the covers that use thread-local storage, and numpy is apparently not able to do those things. Can you show us a complete, minimal, self-contained script that fails? Can you please run that script and copy-and-paste the full traceback? Can you tell us what platform you are on and how you built numpy? If you did not build numpy yourself, please tell us exactly where you got your binaries from (the URL to the package itself is necessary). Thanks. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco
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Robert Kern