----- Original Message ----- From: "Robert Bradshaw" <rober...@math.washington.edu> Sent: Wednesday, December 29, 2010 4:47 PM
[...] >> Regarding Justin's suggestion, before trying Cython (which, according to >> http://wiki.cython.org/tutorials/numpy , seems to require a bit of work >> to >> handle numpy arrays properly) > > Cython doesn't have to be that complicated. For your example, you just > have to unroll the vectorization (and account for the fact that the > result is mutated in place, which was your original goal). Thanks, but the full de-vectorization forces to give up any use of BLAS (I suppose that for array products numpy relies on its routines). In my tests, the performance in terms of speed is more or less the same as the original pure-numpy code (which may be made less memory-hungry with the chunking suggested by Josef). Instead, it would be nice to have a native function able to perform evaluation of arbitrary numpy expressions without converting the intermediate results in Python format (a sort of "better weave.blitz", able to understand slicing, broadcasting rules etc.). That would give us the best of both worlds: code execution at BLAS speeds, and savings in unnecessary conversions and temporary variable allocations. Such "numpy calculator" could also be a simple interpreter, avoiding the complexities and site dependencies deriving from the use of a C compiler: it should build temporary C data structures for the parameters in input, call the relevant C ATLAS/BLAS/LAPACK functions in the right order (possibly allocating temporary C arrays), and convert only the final result back to a Python object. Enzo _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
