Question

我已经使用numba.SmartArrays为添加向量编写了此代码。我是第一次使用这个numba.SmartArrays。我不知道如何使用它。此代码不起作用，它会抛出错误。

    import numpy as np
    from numba import SmartArray,cuda, jit, uint32


    li1=np.uint32([1,2,3,4])
    li=np.uint32([1,2,3,4])
    b=SmartArray(li,where="host",copy=True)
    a=SmartArray(li1,where="host",copy=True)
    c=np.uint32([1,1,1,1])
    print type(li)
    print type(a)

    @cuda.jit('void(uint32[:],uint32[:],uint32[:])',type="gpu")
    def additionG(c,a,b):
        idx=cuda.threadIdx.x+cuda.blockDim.x*cuda.blockIdx.x

        if idx< len(a):
            a[idx]=c[idx]+b[idx]

    dA=cuda.to_device(a)
    dB=cuda.to_device(b)
    dC=cuda.to_device(c)
    additionG[1, 128](c,a,b)

    print a.__array__()

错误：

    <type 'numpy.ndarray'>
    <class 'numba.smartarray.SmartArray'>
    Traceback (most recent call last):
      File "C:\Users\hp-pc\My Documents\LiClipse Workspace\cuda\blowfishgpu_smart_arrays.py", line 20, in <module>
        dA=cuda.to_device(a)
      File "C:\Anaconda\lib\site-packages\numba\cuda\cudadrv\devices.py", line 257, in _require_cuda_context
        return fn(*args, **kws)
      File "C:\Anaconda\lib\site-packages\numba\cuda\api.py", line 55, in to_device
        to, new = devicearray.auto_device(obj, stream=stream, copy=copy)
      File "C:\Anaconda\lib\site-packages\numba\cuda\cudadrv\devicearray.py", line 403, in auto_device
        devobj.copy_to_device(obj, stream=stream)
      File "C:\Anaconda\lib\site-packages\numba\cuda\cudadrv\devicearray.py", line 148, in copy_to_device
        sz = min(_driver.host_memory_size(ary), self.alloc_size)
      File "C:\Anaconda\lib\site-packages\numba\cuda\cudadrv\driver.py", line 1348, in host_memory_size
        s, e = host_memory_extents(obj)
      File "C:\Anaconda\lib\site-packages\numba\cuda\cudadrv\driver.py", line 1333, in host_memory_extents
        return mviewbuf.memoryview_get_extents(obj)
    TypeError: expected a readable buffer object

Answer 1

自从我发布这个问题以来已经有一段时间了。仍然发布答案，以便将来可能会发现它有用。

import numpy as np
from numba import SmartArray,cuda, jit, uint32,autojit

li1=np.uint32([6,7,8,9])
li=np.uint32([1,2,3,4])

a=SmartArray(li1,where='host',copy=True)
b=SmartArray(li,where="host",copy=True)

c=np.uint32([1,1,1,1])

def additionG(a,c):
    idx=cuda.threadIdx.x+cuda.blockDim.x*cuda.blockIdx.x

    if idx < len(c):
        a[idx]=a[idx]+c[idx]

    cuda.syncthreads()

bpg=1
tpb=128
dC=cuda.to_device(c)
cfunc = cuda.jit()(additionG)
cfunc[bpg, tpb](a,dC)

print a.__array__()

Answer 2

在我看来cuda.to_device并不处理智能数组，这有点意义，因为智能数组应该取消明确的副本管理。

如果我对文档的阅读是正确的（我之前从未尝试过SmartArray），那么你应该能够改变这个

dA=cuda.to_device(a)
dB=cuda.to_device(b)
dC=cuda.to_device(c)
additionG[1, 128](c,a,b)

到

dC=cuda.to_device(c)
additionG[1, 128](dC,a.gpu(),b.gpu())

.gpu()方法应返回内核可以理解和访问的GPU驻留对象。

如何使用numba.SmartArrays添加矢量？

2 个答案: