4 ms·
Octave occasionally runs faster than Julia for transliterated vectorized vector and matrix operations. Julia then runs much faster when those operations are dev
by grayclhn 12y ago
Octave occasionally runs faster than Julia for transliterated vectorized vector and matrix operations. Julia then runs much faster when those operations are devectorized or replaced with builtin functions. But that's a pretty common source of these issues (at least on the mailing list.)
- ViralBShah 12y agoIt would be good to know where we are slow, so that we can fix it. Can you for an issue with an example on github?
- grayclhn 12y agoI'll do it right now. These aren't issues I've personally run across, though, just stuff on julia-users. edit: Nevermind! On rereading the mailing list thread (especially this message [1]), I might have mischaracterized the issue slightly. It probably came from speed issues with small integer powers (i.e., there are faster ways to exponentiate small integer powers than there are for general exponentiation that Julia hadn't been using but other software like matlab and octave were), and that issue in Julia seems to be fixed now.[2] [1] https://groups.google.com/d/msg/julia-users/n3LfteWJAd4/jag6AZNvSEsJ https://groups.google.com/d/msg/julia-users/n3LfteWJAd4/jag6... [2] https://github.com/JuliaLang/julia/issues/2741 https://github.com/JuliaLang/julia/issues/2741