digitalmars.D - D performance in Julia microbenchmarks
Carl Vogel <carljv gmail.com> writes:
Hi -- first-time poster, long-time lurker... On its homepage, the Julia langauge (http://julialang.org) advertises some microbenchmark results to show how competitive it is with C, along with a number of other languages (go, python, JS, R, etc.). These should of course be taken with a big grain of salt, for the usual reasons (the implementations differ across languages, even at the algorithmic level, whether these are representative of real-life perf, not really rigorously timed, etc.) I implemented these in D and found it did really well across all of them (no great surprise, but good to see). The results are at the bottom, in absolute time and time relative to C. (note the infinite relative times are b/c the cc -O3 basically optimized the computation away) The github code is here: https://github.com/carljv/julia-perf I think it'd be good advertising to make a PR with these to the Julia repo and see if they'll put them on the site. A lot of people we drawn to Julia because it was making promises similar to D -- C-like performance in an easy-to-use language (in Julia's case, dynamic) with good metaprogramming (in Julia's case, AST macros). But I'd be thrilled if anyone---esp. those with D science/numerics knowledge---would be willing to take a look. E.g., I'm rolling my own matrix operations wrapping blas functions because I couldn't easily find the functionality in scid, mir, etc., but feel like I'm missing something. And I think while showing easy C ffi is great, showing that you can do this easily with a library would be good too. I'm also a bit of a D novice, so may be leaving some performance on the table. (Note though that I am trying to keep the code roughly inline with the Julia implementation and not using, like ASM or uglier perf hacks. It'd be nice to show off how D gives you high-level constructs with speed, not just writing the C implementation in D.) If you want to replicate these, the README on the repo should be relatively complete/correct about setting up and running. Please let me know if you're interested in this and have any questions or issues. FIB: dlang/ldc2 0.01ms infx go 0.05ms infx julia 0.06ms infx python 3.56ms infx MANDEL: dlang/ldc2 0.13ms 1.22x go 0.19ms 1.84x julia 0.20ms 1.88x python 3.97ms 38.19x PARSE_INT: go 0.15ms 1.06x julia 0.18ms 1.23x dlang/ldc2 0.18ms 1.25x python 2.68ms 18.75x PI_SUM: dlang/ldc2 2.91ms 0.40x julia 7.27ms 0.99x go 8.40ms 1.14x python 819.50ms 111.10x QUICKSORT: dlang/ldc2 0.21ms 0.55x julia 0.37ms 0.96x go 0.48ms 1.25x python 14.53ms 37.73x RAND_MAT_MUL: dlang/ldc2 29.65ms 0.71x julia 49.88ms 1.19x go 68.84ms 1.65x python 71.95ms 1.72x RAND_MAT_STAT: dlang/ldc2 8.11ms 1.26x julia 21.99ms 3.40x go 22.00ms 3.40x python 118.10ms 18.27x
May 18 2016
jmh530 <john.michael.hall gmail.com> writes:
On Wednesday, 18 May 2016 at 19:00:06 UTC, Carl Vogel wrote:Please let me know if you're interested in this and have any questions or issues.Cool.
May 18 2016