Python Multiprocessing with PyCUDA

参考：https://stackoverflow.com/questions/5904872/python-multiprocessing-with-pycuda

You need to get all your bananas lined up on the CUDA side of things first, then think about the best way to get this done in Python [shameless rep whoring, I know].

The CUDA multi-GPU model is pretty straightforward pre 4.0 - each GPU has its own context, and each context must be established by a different host thread. So the idea in pseudocode is:

Application starts, process uses the API to determine the number of usable GPUS (beware things like compute mode in Linux)
Application launches a new host thread per GPU, passing a GPU id. Each thread implicitly/explicitly calls equivalent of cuCtxCreate() passing the GPU id it has been assigned
Profit!

In Python, this might look something like this:

import threading
from pycuda import driverclass gpuThread(threading.Thread):def __init__(self, gpuid):threading.Thread.__init__(self)self.ctx  = driver.Device(gpuid).make_context()self.device = self.ctx.get_device()def run(self):print "%s has device %s, api version %s"  \% (self.getName(), self.device.name(), self.ctx.get_api_version())# Profit!def join(self):self.ctx.detach()threading.Thread.join(self)driver.init()
ngpus = driver.Device.count()
for i in range(ngpus):t = gpuThread(i)t.start()t.join()

This assumes it is safe to just establish a context without any checking of the device beforehand. Ideally you would check the compute mode to make sure it is safe to try, then use an exception handler in case a device is busy. But hopefully this gives the basic idea.

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Python Multiprocessing with PyCUDA

Python Multiprocessing with PyCUDA

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