On Sun, Mar 1, 2009 at 23:56, Andreas Klöckner <[email protected]>wrote:
> On Sonntag 01 März 2009, you wrote: > > > 1) Careful, terminology trap. F-contiguous != contiguous (what you call > > > nonlinear) != C-contiguous. > > > > cuda is C, no? is "F" something other than fortran? > > Nicely set up. :) True, "F" is Fortran. But that's just a convenient name. > Call them column- and row-major if you wish. There's good reason for both, > even in a pure C program. > > > I think there should be some sort of check. > > I already knew *your* opinion. :) :) I don't know how appropriate it would be, but perhaps you could post the question on the nvidia cuda forums? > > But maybe all of this can wait > > for your codepy python-aware C structures? I'd like to see some tutorial > > examples on that when you have time. > > Not sure how codepy would help here. I'll try to find time to write a > tutorial. I guess I didn't see the row-major and column-major argument above -- I did notice that the "erroneous" arrays before ndarray.copy() were transposed. On further thought, if one had Python representations of C data structures, arrays could be annotated as row-major or column-major (with C indexing -- last index most contiguous -- by default). Then, instead of doing memcpy, perhaps a programmer could name a struct field (which would be an array in this case) and copy to that, or (as I am now) copy to an array of fixed-size structs. regards, Nicholas
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