Looks cool Dragan, thanks for sharing! I would strongly encourage you to wrap this up and make it work as a core.matrix implementation. That would considerably improve interoperability with other libraries / tools in the Clojure numerical space e.g. it could be used as a drop-in replacement for Clatrix in Incanter 2.0 for example.
The work to do this is fairly simple - you just need to implement a few mandatory protocols from clojure.core.matrix.protocols. Most of the protocols are optional: you only need to implement them if you want to provide a high performance override of the default behaviour. I'm particularly keen that we avoid API fragmentation in the Clojure numerical space (this has plagued many other language communities, e.g. Java). If every such library comes with its own API, then we won't be able to build a strong ecosystem of composable tools (which is in my view pretty fundamental to the Clojure style of development) Some quick thoughts on the benchmarks: a) I'm quite pleased to see that my little pure-Java matrix multiplication implementation comes within an order of magnitude of BLAS/ATLAS :-) thanks for giving me some further improvements to target! b) It looks like you are consistently about 2x faster than JBlas for large matrices - wondering what is causing the difference, is that because of copying? c) Would be interesting to see a few other operations: I do a lot of work with stochastic gradient descent for example so addition and multiply-and-add can be even more important than matrix multiply. On Tuesday, 13 January 2015 09:13:13 UTC+8, Dragan Djuric wrote: > > I am pleased to announce a first public release of new *very fast *native > matrix and linear algebra library for Clojure based on ATLAS BLAS. > Extensive *documentation* is at http://neanderthal.uncomplicate.org > See the benchmarks at > http://neanderthal.uncomplicate.org/articles/benchmarks.html. > > Neanderthal is a Clojure library that > > Main project goals are: > > - Be as fast as native ATLAS even for linear operations, with no > copying overhead. It is roughly 2x faster than jBLAS for large matrices, > and tens of times faster for small ones. Also faster than core.matrix for > small and large matrices! > - Fit well into idiomatic Clojure - Clojure programmers should be able > to use and understand Neanderthal like any regular Clojure library. > - Fit well into numerical computing literature - programmers should be > able to reuse existing widespread BLAS and LAPACK programming know-how and > easily translate it to Clojure code. > > Implemented features > > - Data structures: double vector, double general dense matrix (GE); > - BLAS Level 1, 2, and 3 routines; > - Various Clojure vector and matrix functions (transpositions, > submatrices etc.); > - Fast map, reduce and fold implementations for the provided > structures. > > On the TODO list > > - LAPACK routines; > - Banded, symmetric, triangular, and sparse matrices; > - Support for complex numbers; > - Support for single-precision floats. > > > Call for help: > Everything you need for Linux is in Clojars. If you know your way around > gcc on OS X, or around gcc and MinGW on Windows, and you are willing to > help providing the binary builds for those (or other) systems, please > contact me. There is an automatic build script, but gcc, atlas and other > build tools need to be properly set up on those systems. > > -- You received this message because you are subscribed to the Google Groups "Clojure" group. To post to this group, send email to clojure@googlegroups.com Note that posts from new members are moderated - please be patient with your first post. To unsubscribe from this group, send email to clojure+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/clojure?hl=en --- You received this message because you are subscribed to the Google Groups "Clojure" group. To unsubscribe from this group and stop receiving emails from it, send an email to clojure+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
