I do aim to use only one context (I don't know why I would want more than one; the
stuff I'm doing is mostly serial but the memory shouldn't leave the GPU).
On 31. Jul 2018, at 09:16, Bogdan Opanchuk
First of all, are you using multiple contexts or a single one? If you only have one
context, `Thread(pycuda.autoinit.context)` should be enough for Reikna (don't know
about scikit-cuda, though).
Now if you have several contexts, things become more complicated. CUDA maintains a global
context stack, if you want to use a specific context, you have to make sure it is on top
of the stack (using corresponding PyCUDA functions). For Reikna, you must use a `Thread`
object created for a specific context (or anything derived from it) only when that context
On Tue, Jul 31, 2018 at 5:07 PM, Rasmus Diederichsen <rasmusdiederichsen(a)gmail.com
I don't know cuda really and use the 3 libraries mentioned in the subject. I time and
again run into CUDA errors of the form "Invalid resource handle" which appear in
some cases, but not in others, i.e. the same function will error or not depending on what
ran before it, even if totally unrelated. I gather from this that it has to do with state
in CUDA, possibly mismanaging contexts. But since it's hard to reproduce without all
my code, I can't open issues about this, so hopefully someone can just give me general
I am using the `pycuda.autoinit.context` for all the Reikna `Thread`s.
Neither reikna nor scikit-cuda seem to have functionality for setting context globally or
passing it to functions
I have tried using my own context instead, but meet the same issues
What are the steps in general to ensure these issues don't come up? How can I handle
this when multiple files are in play?
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