alt Hacker News> JIT compilation has the potential to exceed native compiled speeds
The battlecry of Java developers riding their tortoises.
Don’t we have decades of real-world experience showing native code almost always performs better?
For most things it doesn’t matter, but it always rubs me the wrong way when people mention this about JIT since it almost never works that way in the real world (you can look at web framework benchmarks as an easy example)
Replies
Yeah I've heard this my whole career, and while it sounds great it's been long enough that we'd be able to list some major examples by now.
What are the real world chances that a) one's compiled code benefits strongly from runtime data flow analysis AND b) no one did that analysis at the compilation stage?
Some sort of crazy off label use is the only situation I think qualifies and that's not enough.
Only if it doesn't make use of dynamic linking, reflection and is written to take advantage of value types.
AOT compilers without PGO data usually tend to perform worse when those conditions aren't met.
Which is why the best of both worlds is using JIT caches that survive execution runs.
It's not that surprising to people who are old enough to have lived through the "reality" of "interpreted languages will never be faster than about 2x compiled languages".
The idea that an absurdly dynamic language like JS, where all objects are arbitrary property bags with prototypical dependency chains that are runtime mutable, would execute at a tech budget under 2x raw performance was just a matter of fact impossibility.
Until it wasn't. And the technology reason it ended up happening was research that was done in the 80s.
It's not surprising to me that it hasn't happened yet. This stuff is not easy to engineer and implement. Even the research isn't really there yet. Most of the modern dynamic language JIT ideas which came to the fore in the mid 200X's were directly adapting research work on Self from about two decades prior.
Dynamic runtime optimization isn't too hot in research right now, and it never was to be honest. Most of the language theory folks tend to lean more in the type theory direction.
The industry attention too has shifted away. Browsers were cutting edge a while back and there was a lot of investment in core research tech associated with that, but that's shifting more to the AI space now.
Overall the market value prop and the landscape for it just doesn't quite exist yet. Hard things are hard.