I'm encountering this error as I run my code on the same docker environment
but on different workstations.
```
Traceback (most recent call last):
File "simple_peer.py", line 76, in
tslr_gpu, lr_gpu = mp.initialise()
File "/root/distributed-mpp/naive/mccullochpitts.py", line 102, in
initia
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 gputid.mp.neu
n avoid it?
Many thanks,
Zhangsheng
On 12 May 2018 at 12:34, Andreas Kloeckner wrote:
> 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/PyCud
Hi,
I'm trying to create different GPU arrays on different GPUs.
```
import pycuda
import pycuda.driver as cuda
from pycuda.compiler import SourceModule
import pycuda.curandom as curandom
d = 2 ** 15
cuda.init()
dev1 = cuda.Device(1)
ctx1 = dev1.make_context()
curng1 = curandom.XORWOWRandomNum
and get
the x2 values when ctx2 is active, not when ctx1 is active.
On 24 May 2018 at 18:56, Andreas Kloeckner wrote:
> Zhangsheng Lai writes:
> > with the setup above, I tried to check by poping ctx2 and pushing ctx1,
> can
> > I access x1 and not x2 and vice versa, poppi
