speeding up matrix multiplication (newbie question)
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Date:   (:) 
From:  Martin Jambon <martin.jambon@e...> 
Subject:  Re: [Camllist] speeding up matrix multiplication (newbie question) 
Erick Matsen wrote: > Wow, once again I am amazed by the vitality of this list. Thank you > for your suggestions. > > Here is the context: we are interested in calculating the likelihood > of taxonomic placement of short "metagenomics" sequence fragments from > unknown organisms in the ocean. We start by assuming a model of > sequence evolution, which is a reversible Markov chain. The taxonomy > is represented as a tree, and the sequence information is a collection > of likelihoods of sequence identities. As we move up the tree, these > sequences "evolve" by getting multiplied by the exponentiated > instantaneous Markov matrix. > > The matrices are of the size of the sequence model: 4x4 when looking > at nucleotides, and 20x20 when looking at proteins. > > The bottleneck is (I misspoke before) that we are multiplying many > length4 or length20 vectors by a collection of matrices which > represent the time evolution of those sequences as follows. > > Outer loop: > modify the amount of time each markov process runs > exponentiate the rate matrices to get transition matrices > > Recur over the tree, starting at the leaves: > at a node, multiply all of the daughter likelihood vectors together > return the multiplication of that product by the trasition matrix > (bottleneck!) > > The trees are on the order of 50 leaves, and there are about 500 > likelihood vectors done at once. > > All of this gets run on a big cluster of Xeons. It's not worth > parallelizing because we are running many instances of this process > already, which fills up the cluster nodes. > > So far I have been doing the simplest thing possible, which is just to > multiply the matrices out like \sum_j a_ij v_j. Um, this is a bit > embarassing. > > let mul_vec m v = > if Array.length v <> n_cols m then > failwith "mul_vec: matrix size and vector size don't match!"; > let result = Array.create (n_rows m) N.zero in > for i=0 to (n_rows m)1 do > for j=0 to (n_cols m)1 do > result.(i) < N.add result.(i) (N.mul (get m i j) v.(j)) > done; > done; > result > > I have implemented it in a functorial way for flexibility. N is the > number class. How much improvement might I hope for if I make a > dedicated float vector multiplication function? I'm sorry, I know > nothing about "boxing." Where can I read about that? Depending on the savings, you can afford to spend more or less time optimizing this. Here are some simple things to consider: In the OCaml land, try first getting rid of the functor (or use a defunctorizer; ocamldefun?). Limit memory accesses, by doing something like: for i = 0 to m  1 do let a_i = m.(i) in for j = 0 to n  1 do let a_ij = a_i.(j) in (* instead of a.(i).(j) *) ... done done Also you can use Array.unsafe_get where it really matters. You can also use bigarrays and implement the loop in C. It could be fun. I'm not sure how much it saves. Martin