this post was submitted on 11 Jun 2025
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For those who don't want to open threads, it's a link to a paper on energy efficiency of programming languages.
Results
Also the difference between TS and JS doesn't make sense at first glance. 🤷♂️ I guess I need to read the research.
My first thought is perhaps the TS is not targeting ESNext so they're getting hit with polyfills or something
It does, the "compiler" adds a bunch of extra garbage for extra safety that really does have an impact.
I thought the idea of TS is that it strongly types everything so that the JS interpreter doesn't waste all of its time trying to figure out the best way to store a variable in RAM.
TS is compiled to JS, so the JS interpreter isn't privy to the type information. TS is basically a robust static analysis tool
The code is ultimately ran in a JS interpreter. AFAIK TS transpiles into JS, there's no TS specific interpreter. But such a huge difference is unexpected to me.
Care to elaborate?
I guess we can take the overhead of rust considering all the advantages. Go however... can't even.
Even Haskell is higher on the list than Go, which surprises me a lot
But Go has go faster stripes in the logo! Google wouldn't make false advertising, would they?
Now we just need a language with flames in the logq
For Lua I think it's just for the interpreted version, I've heard that LuaJIT is amazingly fast (comparable to C++ code), and that's what for example Löve (game engine) uses, and probably many other projects as well.
WASM would be interesting as well, because lots of stuff can be compiled to it to run on the web
Indeed, here's an example - my climate-system model web-app, written in scala running (mainly) in wasm
(note: that was compiled with scala-js 1.17, they say latest 1.19 does wasm faster, I didn't yet compare).
[ Edit: note wasm variant only works with most recent browsers, maybe with experimental options set - if not try without ?wasm ]
I have no clue what I am looking at but it is absolutely mesmerizing.
Oh, it's designed for a big desktop screen, although it just happens to work on mobile devices too - their compute power is enough, but to understand the interactions of complex systems, we need space.
I would be interested in how things like MATLAB and octave compare to R and python. But I guess it doesn't matter as much because the relative time of those being run in a data analysis or research context is probably relatively low compared to production code.
Is there a lot of computation-intensive code being written in pure Python? My impression was that the numpy/pandas/polars etc kind of stuff was powered by languages like fortran, rust and c++.
Looking at the Energy/Time ratios (lower is better) on page 15 is also interesting, it gives an idea of how "power hungry per CPU cycle" each language might be. Python's very high
Every time I get surprised by the efficiency of Lisp! I guess they mean Common Lisp there, not Clojure or any modern dialect.
For Haskell to land that low on the list tells me they either couldn't find a good Haskell programmer and/or weren't using GHC.
Wonder what they used for the JS state since it's dependent on the runtime.
Does the paper take into account the energy required to compile the code, the complexity of debugging and thus the required re-compilations after making small changes? Because IMHO that should all be part of the equation.
It's a good question, but I think the amount of time spent compiling a language is going to be pretty tiny compared to the amount of time the application is running.
Still - "energy efficiency" may be the worst metric to use when choosing a language.
Energy efficiency strongly correlates to datacentre costs.
And battery costs, including charging time, for a lot of devices. Users generally aren't happy with devices that run out of juice all the time.
They compile each benchmark solution as needed, following the CLBG guidelines, but they do not measure or report the energy consumed during the compilation step.
Time to write our own paper with regex and compiler flags.