Actually I was able to get the go ahead from my boss to create a gist of the code I've produced. I've modified it a little bit, as some of the things in there are really relevant.
http://nbviewer.ipython.org/gist/dwil/5cc31d1fea141740cf96 Any comments for optimizing this a little be more would be appreciated :) On Friday, April 24, 2015 at 11:32:09 AM UTC-3, Duane Wilson wrote: > > It will definitely help to have the code you're using for this. I've have > and, am currently doing, a lot of work in Julia on non-dominated sorting, > so I would be happy to give any help I can. I have a relatively performant > sorting algorithm I've written in Julia and getting 6000+ points down to > near 0.5s should be possible. > > > > > On Friday, April 24, 2015 at 10:27:08 AM UTC-3, Patrick O'Leary wrote: >> >> It's helpful if you can post the full code; gist.github.com is a good >> place to drop snippets. >> >> From what I can see here, there are two things: >> >> (1) No idea if this is in global scope. If so, that's a problem. >> >> (2) push!() will grow and allocate as needed. It does overallocate so you >> won't get a new allocation on every single push, but if you know how big >> the final result will be, you can use the sizehint() method to avoid the >> repeated reallocations. >> >> Please read the Performance Tips page for a longer description of (1) and >> other information: >> http://julia.readthedocs.org/en/latest/manual/performance-tips/ >> >> >> On Friday, April 24, 2015 at 7:47:38 AM UTC-5, Ronan Chagas wrote: >>> >>> Hi guys! >>> >>> I am coding MGEO algorithm into a Julia module. >>> MGEO is a multiobjective evolutionary algorithm that was proposed by a >>> researcher at my institution. >>> >>> I have already a version of MGEO in C++ ( >>> https://github.com/ronisbr/mgeocpp). >>> >>> The problem is that Julia version is very slow compared to C++. >>> When I tried a dummy example, it took 0.6s in C++ and 60s in Julia. >>> >>> After some analysis, I realized that the problem is the loop through a >>> list. >>> The algorithm store a list of points (the Pareto frontier) and for every >>> iteration the algorithm must go through every point in this list comparing >>> each one with the new candidate to be at the frontier. >>> The problem is that, for this dummy example, the process is repeated >>> 128,000 times and the number of points in the frontier at the end is 6,000+. >>> >>> Each point in the list is an instance of this type: >>> >>> type ParetoPoint >>> # Design variables. >>> vars::Array{Float64,1} >>> # Objective functions. >>> f::Array{Float64, 1} >>> end >>> >>> I am creating the list (the Pareto frontier) as follows >>> >>> paretoFrontier = ParetoPoint[] >>> >>> and pushing new points using >>> >>> push!(paretoFrontier, candidatePoint) >>> >>> in which candidatePoint is an instance of ParetoPoint. >>> >>> Can anyone help me to optimize this code? >>> >>> Thanks, >>> Ronan >>> >>
