I'm not very familar with cuda, thus I wld like to ask if you have any
guesses on what is leading to my on-device segfault?
I'm guessing that saving the ctx in the GPU thread class, pushing and
poping it before I run my code earlier might have caused it.
If so, is there any way I can avoid it?
Hi,
I'm trying to do some updates to a state which is a binary array. gputid is
a GPU thread class (https://wiki.tiker.net/PyCuda/Examples/MultipleThreads)
and it stores the state and the index of the array to be updated in another
class which can be accessed with gputid.mp.x_gpu and
Zhangsheng Lai writes:
> Hi,
>
> I'm trying to do some updates to a state which is a binary array. gputid is
> a GPU thread class (https://wiki.tiker.net/PyCuda/Examples/MultipleThreads)
> and it stores the state and the index of the array to be updated in another
> class
I've installed Cuda 8.0 next to my 9.1 version on advice from the nvidia
forum. This did not solve my problem however.
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My goal is to speed up my python FEA (finite elements analysis) with my
quadro GPU. I however have issues when I import pycuda.autoinit or
pycuda.driver into my python code. See the example from my Console:
**code
In [6] import pycuda.autoinit
Traceback (most recent call last):
File "", line
MarbHarmsen writes:
> My goal is to speed up my python FEA (finite elements analysis) with my
> quadro GPU. I however have issues when I import pycuda.autoinit or
> pycuda.driver into my python code. See the example from my Console:
>
> **code
> In [6] import
I've found my mistake. My cuda installation was correct. I had forgotten
however to install the python cudatoolkit-8.0 package in anaconda however. I
used:
user@device:~$ conda install cudatoolkit=8.0
and it solved all my problems
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