Bruce Labitt <[email protected]> writes: > I'm trying to port an FDTD code to pycuda and have run into a problem. The > error occurs when using slices. > > Known error? > > All variables are gpuarray. Fails in main time loop. > > In [4]: run fdtd_pycuda.py > --------------------------------------------------------------------------- > RuntimeError Traceback (most recent call last) > /usr/lib/python2.7/dist-packages/IPython/utils/py3compat.pyc in > execfile(fname, *where) > 202 else: > 203 filename = fname > --> 204 __builtin__.execfile(filename, *where) > > /home/bruce/FDTD/fdtd_pycuda.py in <module>() > 554 > 555 bx[1:ie_tot,:,:] = D1hx[1:ie_tot,:,:] * bx[1:ie_tot,:,:] - > D2hx[1:ie_tot,:,:] * \ > --> 556 ( ( ez[1:ie_tot, 1:jh_tot, :] - ez[1:ie_tot, 0:je_tot, > :]) - ( ey[1:ie_tot,:,1:kh_tot] - ey[1:ie_tot,:,0:ke_tot] ) ) / delta > 557 """ > 558 above line generates RunTimeError: only contiguous arrays > may be used as arguments to this operation > > /usr/local/lib/python2.7/dist-packages/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/gpuarray.pyc > in __sub__(self, other) > 425 if isinstance(other, GPUArray): > 426 result = self._new_like_me(_get_common_dtype(self, > other)) > --> 427 return self._axpbyz(1, other, -1, result) > 428 else: > 429 if other == 0: > > /usr/local/lib/python2.7/dist-packages/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/gpuarray.pyc > in _axpbyz(self, selffac, other, otherfac, out, add_timer, stream) > 308 assert self.shape == other.shape > 309 if not self.flags.forc or not other.flags.forc: > --> 310 raise RuntimeError("only contiguous arrays may " > 311 "be used as arguments to this operation") > 312 > > RuntimeError: only contiguous arrays may be used as arguments to this > operation > > > Evaluating the following generates the RuntimeError. > > In [17]: ( ey[1:ie_tot,:,1:kh_tot] - ey[1:ie_tot,:,0:ke_tot] ) > --------------------------------------------------------------------------- > RuntimeError Traceback (most recent call last) > <ipython-input-17-1c6dfb98d933> in <module>() > ----> 1 ( ey[1:ie_tot,:,1:kh_tot] - ey[1:ie_tot,:,0:ke_tot] ) > > /usr/local/lib/python2.7/dist-packages/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/gpuarray.pyc > in __sub__(self, other) > 425 if isinstance(other, GPUArray): > 426 result = self._new_like_me(_get_common_dtype(self, > other)) > --> 427 return self._axpbyz(1, other, -1, result) > 428 else: > 429 if other == 0: > > /usr/local/lib/python2.7/dist-packages/pycuda-2014.1-py2.7-linux-x86_64.egg/pycuda/gpuarray.pyc > in _axpbyz(self, selffac, other, otherfac, out, add_timer, stream) > 308 assert self.shape == other.shape > 309 if not self.flags.forc or not other.flags.forc: > --> 310 raise RuntimeError("only contiguous arrays may " > 311 "be used as arguments to this operation") > 312 > > RuntimeError: only contiguous arrays may be used as arguments to this > operation > > ey.shape = (111, 110, 111) > kh_tot = 111 > ke_tot = 110 > > Shapes are appropriate. > > Work arounds for slices? Straight numpy implementation works fine.
None yet. I'd appreciate patches, but for now the linear algebra functionality in PyCUDA only works on contiguous arrays. Andreas
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