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Hello,
I have a question regarding how PyCUDA interacts with CUDA 4.x's
support for sharing contexts across threads.
Broadly speaking I wish to create an analogue of CUDA streams that
also support invoking arbitrary Python functions (as opposed to just
CUDA kernels and memcpy operations).
My idea is to associate a Python thread with each CUDA stream in my
application and use a Queue (import Queue) to submit either CUDA
kernels or Python functions to the queue with the core code being
along the lines of:
def queue_worker(q, comm, stream):
while True:
item = q.get()
if item_is_a_cuda_kernel:
item(stream=stream)
stream.synchronize()
elif item_is_a_mpireq:
comm.Prequest.startall(item)
comm.Prequest.waitall(item)
else:
item()
q.task_done()
Allowing one to do:
q1, q2 = Queue(), Queue()
t1 = Thread(target=queue_worker, args=(q1, comm, a_stream1)
t2 = Thread(target=queue_worker, args=(q2, comm, a_stream2)
t1.start()
t2.start()
# Stick items into the queue for the thread to consume
However, this is only meaningful if it is possible to share a PyCUDA
context between threads. Can someone update me on if this is possible
at all (on the CUDA driver level) and if PyCUDA supports this?
Regards, Freddie.
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