Le lundi 10 novembre 2014 à 13:03 -0800, Joshua Tokle a écrit : > Hello! I'm trying to replace an existing matlab code with julia and > I'm having trouble matching the performance of the original code. The > matlab code is here: > > https://github.com/jotok/InventorDisambiguator/blob/julia/Disambig.m > > The program clusters inventors from a database of patent applications. > The input data is a sparse boolean matrix (named XX in the script), > where each row defines an inventor and each column defines a feature. > For example, the jth column might correspond to a feature "first name > is John". If there is a 1 in the XX[i, j], this means that inventor > i's first name is John. Given an inventor i, we find similar inventors > by identifying rows in the matrix that agree with XX[i, :] on a given > column and then applying element-wise boolean operations to the rows. > In the code, for a given value of `index`, C_lastname holds the unique > column in XX corresponding to a "last name" feature such that > XX[index, :] equals 1. C_firstname holds the unique column in XX > corresponding to a "first name" feature such that XX[index, :] equals > 1. And so on. The following code snippet finds all rows in the matrix > that agree with XX[index, :] on full name and one of patent assignee > name, inventory city, or patent class: > > lump_index_2 = step & ((C_assignee | C_city | C_class)) > > The `step` variable is an indicator that's used to prevent the same > inventors from being considered multiple times. My attempt at a > literal translation of this code to julia is here: > > https://github.com/jotok/InventorDisambiguator/blob/julia/disambig.jl > > The matrix X is of type SparseMatrixCSC{Int64, Int64}. Boolean > operations aren't supported for sparse matrices in julia, so I fake it > with integer arithmetic. The line that corresponds to the matlab code > above is > > lump_index_2 = find(step .* (C_name .* (C_assignee + C_city + C_class))) You should be able to get a speedup by replacing this line with an explicit `for` loop. First, you'll avoid memory allocation (one for each + or .* operation). Second, you'll be able to return as soon as the index is found, instead of computing the value for all elements (IIUC you're only looking for one index, right?).
My two cents > The reason I grouped it this way is that initially `step` will be a > "sparse" vector of all 1's, and I thought it might help to do the > truly sparse arithmetic first. > > I've been testing this code on a Windows 2008 Server. The test data > contains 45,763 inventors and 274,578 possible features (in other > words, XX is an 45,763 x 274,58 sparse matrix). The matlab program > consistently takes about 70 seconds to run on this data. The julia > version shows a lot of variation: it's taken as little as 60 seconds > and as much as 10 minutes. However, most runs take around 3.5 to 4 > minutes. I pasted one output from the sampling profiler here [1]. If > I'm reading this correctly, it looks like the program is spending most > of its time performing element-wise multiplication of the indicator > vectors I described above. > > I would be grateful for any suggestions that would bring the > performance of the julia program in line with the matlab version. I've > heard that the last time the matlab code was run on the full data set > it took a couple days, so a slow-down of 3-4x is a signficant burden. > I did attempt to write a more idiomatic julia version using Dicts and > Sets, but it's slower than the version that uses sparse matrix > operations: > > https://github.com/jotok/InventorDisambiguator/blob/julia/disambig2.jl > > Thank you! > Josh > > > [1] https://gist.github.com/jotok/6b469a1dc0ff9529caf5 > >
