Hello, I am not sure if this problem related to pycuda gpuarray or sickits.cuda library so I just posted in both mailing list maybe I can find a solution about it.
my problem is that when I am trying to find a matrix transpose of matrix which has mad up by concatenation by 2 matrices I'm getting wrong result. To be more precises : a1=np.array([[1,3,4,5],[7,4,8,2],[7,5,0,9]],np.float64) temp=np.array([[3,4,5],[4,8,2],[5,0,9]],np.float64) a2=r2=np.c_[np.array([1,7,7],np.float64),temp] a1_gpu=gpuarray.to_gpu(a1) a2_gpu=gpuarray.to_gpu(a2) so far everything works fine and I have same value for all matrices. a1=a1=a1_gpu=a2_gpu : [ 1., 3., 4., 5.] [ 7., 4., 8., 2.] [ 7., 5., 0., 9.] but now import scikits.cuda.linalg as la np.all(la.transpose(a1_gpu).get())==a1.T) returns True but but False for np.all(la.transpose(a2_gpu).get())==a2.T) my la.transpose(a2_gpu) : [ 1., 4., 0.] [ 7., 5., 5.] [ 7., 4., 2.] [ 3., 8., 9.] by looking at a1 and la.transpose(a2_gpu) it looks like the problem is somehow related to memory storage! I am right? Do you have any idea? Thanks in advance
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