Hi Mark
To continue with library I just wrote a quick recursive matrix
multiplication. Since you mentioned about Strassen's algorithm so I went to
wikipedia ( http://en.wikipedia.org/wiki/Strassen_algorithm ) and wrote the
recursive algorithm using 4 multiplication but it's not very hard to modify
this to Strassen's algorithm. You can see code (
https://github.com/mukeshtiwari/Puzzles/blob/master/recursivematrixmultiplic...).
It's just quick code and it has lot of chance for improvement like
using
Data Parallel Haskell ) , some parallelism using Simon's monad-par library
or distributed parallelism using Cloud Haskell and sparse matrix
representation consideration.
Mukesh Tiwari
On Sat, Dec 1, 2012 at 3:28 AM, Mark Flamer
Thanks for all the replies, It sounds like there is enough interest and even some potential collaborators out there. I have created a few data structures to represent sparse vectors and matrices. The vector was a simple binary tree and the matrix a quad tree. As I suspected a standard IntMap was around 3X as fast as my binary tree, so I have switched to the IntMap for now. I was hoping to hold on to my quad tree for the matrix rep. because I like the elegance of the recursive algorithms like Strassen’s for multiplication. In the end it will most likely be best to have a few different matrix representations anyway. For instance, I know that compressed row form is most efficient for certain algorithms. I have a Gauss iterative solver working currently and am thinking of moving on to a parallel Conjugate gradient or direct solver using LU decomposition. I’m no expert in Haskell or numeric methods but I do my best. I’ll clean up what I have and open up the project on Github or Bitbucket. Any other thoughts or ideas are welcome. I’m currently using the Matrix Market files for testing. It would be nice to benchmark this against an industry standard solver in C or Fortran, without having to do a lot of work to set it up. Does anyone know of an easy way to do this? I’m just trying to get results to compare in orders of magnitude for now. It would be motivating to see these comparisons.
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