Hello, > > If you are after numerical calculations for mxn matrices, then look at > `flomat` > which uses the same underlying C/Fortran library that NumPy uses. > > https://docs.racket-lang.org/manual-flomat/index.html > > If you need rrays with more dimensions than 2 use `math/array`. > > If you need simple computation of 1-dimensional data, you can use > standard Racket vectors (or flomats or arrays).
I didn't know about `flomat'. Thanks for (yet another) good hint. However, for my project I needed really fast matrix multiplication (and other basic linear algebra functions). It turned out that most of the available options are sub-optimal at best. To my surprise `flomat` is one of those. Mostly I am concerned with 3D and 4D matrices and vectors. During the past few months I hacked together a fairly optimized module[1] for performing these operations that defines algebras for given dimension during macro expansion stage and all the procedures are constructed in a way that helps Racket (mainly CS) perform all operations unboxed. In the repository, there is a benchmark script, which yields the following (self-explanatory) results: ==== Welcome to Racket v7.9.0.5 [cs]. # 1000000 iterations ## flalgebra mat3x3*mat3x3! cpu time: 13 real time: 13 gc time: 0 mat3x3*mat3x3 cpu time: 23 real time: 23 gc time: 4 ## math/matrix matrix*matrix cpu time: 55892 real time: 55911 gc time: 463 ## math/typed/matrix matrix*matrix cpu time: 6861 real time: 6862 gc time: 1045 ## flomat matrix*matrix! cpu time: 887 real time: 887 gc time: 4 matrix*matrix cpu time: 1825 real time: 1825 gc time: 7 ==== Welcome to Racket v7.9 [bc]. # 1000000 iterations ## flalgebra mat3x3*mat3x3! cpu time: 145 real time: 145 gc time: 7 mat3x3*mat3x3 cpu time: 163 real time: 163 gc time: 2 ## math/matrix matrix*matrix cpu time: 53817 real time: 53788 gc time: 733 ## math/typed/matrix matrix*matrix cpu time: 3852 real time: 3851 gc time: 730 ## flomat matrix*matrix! cpu time: 745 real time: 745 gc time: 1 matrix*matrix cpu time: 1621 real time: 1620 gc time: 1 What puzzles me the most: when I read `flomat' documentation, I thought it must beat my implementation by far margin - it's a native code for really basic number crunching. When using the `times!' variant of matrix multiplication, I would expect it to outperform anything implemented in in pure Racket. Is there something I miss or is it really the case, that carefully crafted Racket implementation is the fastest option right now? I actually wanted to write a similar email some time ago - in the spring - about the typed math/matrix variant. But I was almost certain I am doing something wrong and should figure it out. Now I am more and more convinced that the performance of all number crunching libraries - as they can be used in Racket - might be the real issue here. Cheers, Dominik [1] https://gitlab.com/racketeer/flalgebra -- You received this message because you are subscribed to the Google Groups "Racket Users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/racket-users/c94bf70d-1563-62d5-7632-bbaade7eb97f%40trustica.cz.
