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 <[email protected]> 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 >> >>
