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On 10/11/22, Pierre Chapuis <> wrote:

> I won't publish this code (at least not now) but porting from Teal was
> straightforward - basically replacing instances of "number" with "float" and
> tweaking a few things. I just had to add a small Teal type signature file
> ( so I can call the Pallene code from Teal. This straightforward port
> gave me a 4x speedup (compared to Teal but that is basically plain Lua).

Coincidentally, I just posted results of a little benchmark for my
program in the Pallene discussion section on Github. I've been
experimenting with Pallene for the last year on a side-project (as
time permits). The program is a back tester for stocks and options
strategies. As such, the program is mostly iterating through arrays of
floats and doing simple math on the numbers, with a lot of control
flow stuff to implement specific trading strategies.

Here are the results from Lua 5.4, LuaJIT, and Pallene.

Lua 5.4
real 37m43.191s
user 35m22.021s
sys 0m0.568s

LuaJIT 2.1.0-beta3:
real 20m13.698s
user 17m9.879s
sys 0m43.243s

real 11m33.475s
user 9m11.986s
sys 0m0.511s

I know Pallene isn't trying to beat LuaJIT, but I was impressed with
this result. I don't know why my program worked out this way. If I had
to guess as to the reason, the program is complicated enough that it
is probably hard to unroll and vectorize a lot of things, and probably
a the majority of the time is dealing with the control flow.

I also find this result encouraging for Pallene because I think my
program is probably more reflective of programs that will be written
in the real world, and not a micro-benchmark. My hope is that a lot of
regular programs will enjoy similar performance benefits.

I have a variant of the above run that utilizes Lua Lanes, but I was
unable to use it for this benchmark because I was getting segmentation
faults with (only) LuaJIT on that code. This might be another subtle
advantage for Pallene because I have no idea how to debug this kind of
problem with LuaJIT.