The generated llvm code is very complicated, which is a sign of something not very good going on.
This could be good to add as a performance test, and to DataStructures.jl On Sunday, May 11, 2014 8:27:46 PM UTC-4, Iain Dunning wrote: > > So the FloatingPoint -> Float64 is probably a good idea, but isn't the > bottleneck. > > I profiled the random access section: > t[1] > @profile for i in 1:n > t[rand(1:n)] > end > Profile.print() > > and all the work is going on in lines 84,87, and 88, as you'd expect. > What I can't figure out is why it is allocating any memory... > > On Sunday, May 11, 2014 7:59:42 PM UTC-4, Yuri Vishnevsky wrote: >> >> Oddly, that makes it *slower*... >> >> julia> benchmark(1000000) >> Timing 1000000 insert operations. >> elapsed time: 11.637004929 seconds (1324768744 bytes allocated) >> Timing 1000000 random access operations. >> elapsed time: 15.640938079 seconds (1047763544 bytes allocated) >> Timing 1000000 remove operations. >> elapsed time: 9.440363056 seconds (1138842368 bytes allocated) >> >> (vs. 7s, 16s, and 5s with FloatingPoint priorities, respectively.) >> >> – Yuri >> >> On Sunday, May 11, 2014 7:39:17 PM UTC-4, John Myles White wrote: >>> >>> Try changing FloatingPoint to Float64 and you may seem a substantial >>> performance boost. >>> >>> — John >>> >>> On May 11, 2014, at 4:32 PM, Yuri Vishnevsky <yuri...@gmail.com> wrote: >>> >>> > I'm working on a Julia implementation of a Treap, a data structure >>> that maintains a sorted collection of elements and allows insertion, >>> deletion, and random access in O(log n) time ( >>> http://en.wikipedia.org/wiki/Treap). >>> > >>> > So far I've implemented the basic functions, but performance is far >>> slower than I'd like and I am having trouble understanding why. >>> > >>> > Here's the gist: https://gist.github.com/yurivish/aff46c190c1ac538c46f >>> > >>> > I've written a small benchmark script to try and diagnose the problem. >>> It reports lots of memory use for larger collections, even during calls to >>> getindex(). Here's the output for several runs; the function, which is >>> included in the gist, creates and queries a list of Int64s. >>> > >>> > julia> benchmark(100) >>> > Timing 100 insert operations. >>> > elapsed time: 0.000188415 seconds (11200 bytes allocated) >>> > Timing 100 random access operations. >>> > elapsed time: 0.000294615 seconds (0 bytes allocated) >>> > Timing 100 remove operations. >>> > elapsed time: 6.6692e-5 seconds (0 bytes allocated) >>> > >>> > julia> benchmark(1000) >>> > Timing 1000 insert operations. >>> > elapsed time: 0.0024881 seconds (208080 bytes allocated) >>> > Timing 1000 random access operations. >>> > elapsed time: 0.003553674 seconds (95776 bytes allocated) >>> > Timing 1000 remove operations. >>> > elapsed time: 0.001143906 seconds (74496 bytes allocated) >>> > >>> > julia> benchmark(1000000) >>> > Timing 1000000 insert operations. >>> > elapsed time: 6.920983574 seconds (521837776 bytes allocated) >>> > Timing 1000000 random access operations. >>> > elapsed time: 16.232631535 seconds (1010908560 bytes allocated) >>> > Timing 1000000 remove operations. >>> > elapsed time: 5.399104537 seconds (387715296 bytes allocated) >>> > >>> > Any insight into potential improvements to (or problems with) the code >>> would be appreciated. >>> > >>> > Cheers, >>> > Yuri >>> >>>
