Just finished some basic tests comparing the lua jit and Julia for the kinds of statistical functions we commonly compute. It essentially loads 70K 1 minute bar records and computes a sma(14) and sma(600) for every row in the file. This time I included source code so others can figure out what I missed. It is admittedly a simplified case but I have found that if this function runs fast the rest of our system tends to run fast so I consider it a realistic starting benchmark.
http://bayesanalytic.com/lua_jit_faster_than_julia_stock_prediction/ The results were not what I expected. I expected Julia to blow away lua even with a jit due to the fact that I could allocate memory for result arrays in typed arrays in Julia as blocks and couldn't figure out how to do the same in lua. In addition the lua array index access seem more like a hash rather than a pure numeric array index which should give Julia a substantial advantage when looping across items in an array. What I found is that Lua jit out performed Julia in all but 1 test even if you don't consider Julia's horrible start-up performance. I am hoping that somebody finds a mistake that would make Julia out perform as I really want to love it. I like the Julia community I also really like the multi-dispatch function system. The Julia community seems to be working at a incredible velocity but Julia's poor error messages, slow startup time and letting lua beat them makes me skeptical for investing in it for larger projects. On the other-hand Lua has been around for a long time and is used as a scripting engine in many games and consoles and is unlikely to go away anytime soon. If any of you produce a better Julia version that performs better then let me know and I will add it to the original article. If any of you have a chance to port the same code to Python to using pypy, Java, Scala, C then let me know and I will add it to the original article.
