Stripping out the dictionary stuff in search and doing it in a single array pass has knocked me down to elapsed time: 1.305257143 seconds (183884144 bytes allocated, 3.85% gc time)
Changing to bitarrays would be the real fix, and I got halfway, but converting the code was so painful I might just write my own solver based on the same bruteforce idea. On Tuesday, July 1, 2014 7:58:44 PM UTC-4, andy hayden wrote: > > Ha, I had exactly the same issue (I pushed a perf update with a commented > out impl), I assumed it was something (very) wrong in my understanding of > control flow! > > I don't think see how it would rely on anything, ordering (?)... > perplexing. > > On Tuesday, 1 July 2014 16:45:04 UTC-7, Iain Dunning wrote: >> >> Yeah we changed the example, so best to take it from the one in the >> release version... >> >> I removed the dictionary from search() but its now no longer solving all >> the problems(!) - does the algorithm rely somehow on the way the dictionary >> is constructed? >> >> >> On Tuesday, July 1, 2014 6:59:02 PM UTC-4, andy hayden wrote: >>> >>> I was using Cbc. >>> >>> SolveModel is a copy and paste job from JuMP (from the last release >>> rather than master) so may not work with JuMP from master - I couldn't get >>> the version from master working since it was incompatible with the JuMP >>> release I had! It'd be great to just be able to just include the file, but >>> I couldn't get that working so I just pasted it in (I should probably clean >>> Bench as I made quite a mess, apologies about that.)... so it may be you >>> need to update SolveModel from JuMP master/your version of JuMP to get >>> Bench working. >>> >>> It's amazing how some small tweaks like this go so far, there's a few >>> other bits that are obvious even to me (but I just couldn't get working). >>> >>> >>> On 1 July 2014 15:47, Iain Dunning <[email protected]> wrote: >>> >>>> JuMP won't be getting any faster, its entirely limited by the speed of >>>> the MIP solver. Which one did you use? >>>> >>>> >>>> On Tuesday, July 1, 2014 6:47:04 PM UTC-4, Iain Dunning wrote: >>>>> >>>>> I was unable to run Bench.jl (ERROR: varzm! not defined), but, on my >>>>> computer just using runtests.jl, a fixed seed, and total time for 100 >>>>> random >>>>> >>>>> *Initial >>>>> elapsed time: 1.641434988 seconds (282491732 bytes allocated, 5.99% gc >>>>> time) >>>>> >>>>> *Change globals to const >>>>> elapsed time: 1.563094028 seconds (261818132 bytes allocated, 6.61% gc >>>>> time) >>>>> >>>>> * Changing from using a Dict{Int64, *} for the Grid types to just a >>>>> Vector{*}, as well as those other globals >>>>> elapsed time: 1.373703078 seconds (191864592 bytes allocated, 4.91% gc >>>>> time) >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> On Tuesday, July 1, 2014 6:27:15 PM UTC-4, andy hayden wrote: >>>>>> >>>>>> Bench.jl has a bench_compare method which returns a DataFrame of >>>>>> times (I then divide the median of Python vs Julia columns), I'll add >>>>>> this >>>>>> output to the Bench script as it's useful to see (would be nice to add >>>>>> more >>>>>> stats, as it's just a DataFrame of all the solved puzzles in seconds). >>>>>> By >>>>>> default it runs a hundred random sudoku's on Julia, Python, and JuMP >>>>>> (the >>>>>> same on each)... >>>>>> >>>>>> Thanks Steven: Making those const makes a huge difference, Julia wins >>>>>> (from 20% slower to 10% faster for me with just that change). >>>>>> I will have a play and see how your other suggestions play out. >>>>>> >>>>>> I was also very impressed with JuMP here... and it may be the latest >>>>>> is even faster (I'm using the version from the last release rather than >>>>>> master, and it has changed since then). >>>>>> >>>>>> >>>>>> On Tuesday, 1 July 2014 15:11:27 UTC-7, Iain Dunning wrote: >>>>>>> >>>>>>> I'm working on improving this, but I'm not sure how you are >>>>>>> measuring that 20% slower - can you be more specific? >>>>>>> >>>>>>> On Tuesday, July 1, 2014 1:37:00 PM UTC-4, andy hayden wrote: >>>>>>>> >>>>>>>> I recently ported Norvig's Solve Every Sudoku Puzzle >>>>>>>> <http://norvig.com/sudoku.html> to Julia: https://github.com/ >>>>>>>> hayd/Sudoku.jl >>>>>>>> >>>>>>>> Some simple benchmarks suggest my Julia implementation solves >>>>>>>> around 20% slower* than the Python version, and 3 times faster than >>>>>>>> the >>>>>>>> implementation on JuMP (vendorized from the latest release), against >>>>>>>> the >>>>>>>> random puzzles. I tried to include the solver from >>>>>>>> attractivechaos/plb >>>>>>>> <https://github.com/attractivechaos/plb/tree/master/sudoku> but >>>>>>>> couldn't get it working for comparison... >>>>>>>> >>>>>>>> I'm new to Julia so would love to hear people's thoughts / any >>>>>>>> performance tips! >>>>>>>> I've not delved too deeply into the Profile, but @time suggests 10% >>>>>>>> of time is GC. >>>>>>>> >>>>>>>> **I'm sure I've lost some performance in translation which could be >>>>>>>> easily sped up...* >>>>>>>> >>>>>>>> Best, >>>>>>>> Andy >>>>>>>> >>>>>>> >>>
