David M. Cooke wrote:
I've done a little bit of work along these lines. I have a module I call vector3 [*] which has 2- and 3-dimensional immutable vectors, using either ints or doubles. It's as fast as I could make it, while keeping it all written in Pyrex. I find it very convenient for anything vector-related. Konrad Hinsen has something similiar in the development version of his ScientificPython package.
Also, I've also done some playing around with a n-dimensional vector type (restricted to doubles). My best attempts make it ~4-5x faster than numpy (and 2x faster than Numeric) for vectors of dimension 10 on simple ops like + and *, 2x faster than numpy for dimension 1000, and approaching 1x as you make the vectors larger. Indexing is about 3x faster than numpy, and 1.4x faster than Numeric. So that gives I think some idea of the maximum speed-up possible.
I think the speedups mostly come from the utter lack of any polymorphism: it handles vectors of doubles only, and only as contiguous vectors (no strides).
This is excellent, thanks for the pointer. I can see uses for vectors (still 1-d, no strides, etc) with more than 3 elements, and perhaps fixed-size (no reshaping, no striding) 2-d arrays (matrices), but this looks like a good starting point. Sandbox material?