This is not specific to pycuda, but to cuda itself. It is a new
feature (~unified addresse space) that they have that request this. I
don't know how to disable this feature as I don't use it.
On Wed, May 23, 2012 at 10:35 AM, <wood(a)synchroverge.com> wrote:
I'm working on a reasonably large piece of Python software which uses PyCUDA
for the performance-critical section of the code. I've been experiencing a
memory leak, and while trying to track it down I've noticed that PyCUDA has
a large virtual memory footprint -- somewhere in the ballpark of 36GB even
when no arrays have yet been allocated. Is this typical for PyCUDA, or is
there perhaps something wrong with my setup?
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