Python Multiprocessing with PyCUDA

Question

I\'ve got a problem that I want to split across multiple CUDA devices, but I suspect my current system architecture is holding me back;

What I\'ve set up is a GPU class

Answer 1

What you need is a multi-threaded implementation of the map built-in function. Here is one implementation. That, with a little modification to suit your particular needs, you get:

import threading

def cuda_map(args_list, gpu_instances):

    result = [None] * len(args_list)

    def task_wrapper(gpu_instance, task_indices):
        for i in task_indices:
            result[i] = gpu_instance.gpufunction(args_list[i])

    threads = [threading.Thread(
                    target=task_wrapper, 
                    args=(gpu_i, list(xrange(len(args_list)))[i::len(gpu_instances)])
              ) for i, gpu_i in enumerate(gpu_instances)]
    for t in threads:
        t.start()
    for t in threads:
        t.join()

    return result

It is more or less the same as what you have above, with the big difference being that you don't spend time waiting for each single completion of the gpufunction.

Answer 2

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:

1. Application starts, process uses the API to determine the number of usable GPUS (beware things like compute mode in Linux)
2. 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
3. Profit!

In Python, this might look something like this:

import threading
from pycuda import driver

class 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.