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.XORWOWRandomNumberGenerator() x1 = curng1.gen_normal((d,d), dtype = np.float32) # so x1 is stored in GPU 1 memory ctx1.pop() # clearing ctx of GPU1 dev2 = cuda.Device(1) ctx2 = dev2.make_context() curng2 = curandom.XORWOWRandomNumberGenerator() x2 = curng2.gen_normal((d,d), dtype = np.float32) # so x2 is stored in GPU 2 ``` with the setup above, I tried to check by poping ctx2 and pushing ctx1, can I access x1 and not x2 and vice versa, popping ctx1 and pushing ctx2, I can access x2 and not x1. However, I realise that I can access x1 and x2 in both contexts. Thus I'm wondering if my assumptions of x1 stored in GPU1 and x2 stored in GPU2 are correct, or if it is actually the UVA and peer access that allows me to access both x1 and x2 even if only one of the two ctx is active. Thanks, Zhangsheng
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