If at all possible, if someone with Python expertise could get things set up for you, would be good to compare also to Numba, Python 3.5 (should be released shortly), and PyPy3.
If anything shows that other languages are much better than Julia in some area, all the more incentive for us to step up our game. (No matter what, I still think Julia wins hands-down on expressiveness, LOC needed to write performant code). Another thing it might be useful to show in a graph on the JuliaLang page, is the LOC for each microbenchmark, as that's an important metric as well, that I've seen elsewhere graphed showing big advantage for Julia. On Wednesday, September 9, 2015 at 10:00:48 PM UTC-4, Tony Kelman wrote: > > We'll probably update to more recent versions of everything and re-run the > benchmarks once 0.4.0 final is released and someone gets some time to get > all the relevant software set up on the same machine. > > > On Saturday, September 5, 2015 at 12:03:32 PM UTC-7, Stefan Stoll wrote: >> >> Hi all, >> >> I am a veteran Matlab user and have recently started playing around with >> Julia. What a great language! >> >> Today I noticed that the micro-benchmark published on julialang.org is >> quite out of date, esp. what Matlab is concerned. The benchmark uses Matlab >> R2014a, but the most recent R2015b has a new execution engine that is >> significantly faster. >> >> For a fair comparison, I suggest rerunning the benchmark with the latest >> Matlab (and Julia) versions and updating the numbers on the website. >> >> Here's what I get when running the micro-benchmark function perf() on >> Matlab R2014b on my old laptop: >> matlab,fib,95.66057408 >> matlab,parse_int,151.79947331 >> matlab,mandel,12.45005290 >> matlab,quicksort,24.00013615 >> matlab,pi_sum,47.18823305 >> matlab,rand_mat_stat,62.70896132 >> matlab,rand_mat_mul,221.44602147 >> >> And here are the results with Matlab R2015b on the same laptop: >> matlab,fib,1.04503455 >> matlab,parse_int,95.14879485 >> matlab,mandel,3.11889527 >> matlab,quicksort,2.05846277 >> matlab,pi_sum,36.74599177 >> matlab,rand_mat_stat,100.73379335 >> matlab,rand_mat_mul,224.68040137 >> >> The biggest speedups are in the Fibonacci and quicksort tests. >> >> >>
