A Thursday 23 July 2009 21:17:17 escriguéreu: [clip] > > Well, if done properly, I/O in PyTables should not take much more than > > numpy.memmap (in fact, it can be faster in many occasions). You just > > need to read/write arrays following the contiguous direction, i.e. the > > most to the right among those orthogonal to the 'main' dimension (in > > PyTables jargon). > > I think we are. > > The slow part of our code is a single line of Python: > `numpy.dot(matrix, vector)`. `matrix` is a tall (thousands by 50) > PyTables Array or CArray. `vector` is a row sliced out of it (1 by > 50).
For better speed, you can try to read a *complete* row, and call numpy.dot(row, column) for every row in the matrix. More optimizations can be done by reading several rows in one shot for computing several rows at once. HTH, -- Francesc Alted ------------------------------------------------------------------------------ Let Crystal Reports handle the reporting - Free Crystal Reports 2008 30-Day trial. Simplify your report design, integration and deployment - and focus on what you do best, core application coding. Discover what's new with Crystal Reports now. http://p.sf.net/sfu/bobj-july _______________________________________________ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users
