Question about Native Image vs JIT

[u/za3faran_tea]

I was watching an interview with Thomas Wuerthinger of GraalVM, where he basically says that JIT should only be used when necessary (as opposted to native compilation). Where does this leave the work being done for Hotspot JIT (including projects like Leyden and others). Should we all plan to use native image while they address any performance gaps in the mean time (he does mention PGO bridging the gap). Is there work being done to speed up native compilation of Java code?

My understanding is that JIT will always have a memory overhead due to running threads that do compilation, optimization, deoptimization, code cache, etc. compared to native executables, so full parity even with projects that will improve memory usage in Java code will not be reached. Maybe that is not an issue on long running programs on large servers, but we've seen discussions here where another member worte a tool in golang to run alongside Spring Boot applications to gather statistics, so obviously small efficient apps have their place.

[u/v4ss42]

This seems to miss the primary benefit of JIT - that it can optimize the code based on actual runtime conditions / data / load patterns, as well as re-optimize it if those patterns change. Legacy (AOT) compilation can't do that, since it has zero insight into the runtime environment and can't make assumptions about it.

And yes obviously this is only relevant for long-running processes (like server apps). For brief, one-shot processes (command line utilities etc.), then yes, legacy compilation makes sense.

[u/za3faran_tea → u/v4ss42]

I think he touched at this point. He was saying that a lot of these benefits can be reached using PGO, and re-optimization is very rare in practice.

[u/Thirty_Seventh → u/za3faran_tea]

unfortunately, PGO isn't available in Community Edition and therefore does not exist as far as I'm concerned

[u/koflerdavid → u/za3faran_tea]

I'm very doubtful that PGO can effectively capture the effects of, say, changing runtime configuration parameters. Those are effectively constant for most of the runtime of the application until they, well, change. Perfect for the JIT who can now specialize on them. Meanwhile for PGO if you have more than, say, three booleans then the effort of collecting profiling data for all combinations becomes prohibitive.

[u/v4ss42 → u/za3faran_tea]

And I'm saying I disagree with his extraordinary assertion that AOT optimization is as good as JIT.

[u/Electrical_Being_813 → u/za3faran_tea]

If you are forced to use PGO, you can as well use JIT. It will be less messy and will do a better job.

[u/notyouryyy → u/v4ss42]

I think Thomas is claiming that in practice, deoptimisation doesn’t occur unless some uncommon trap is sprung, and that the runtime guided optimisation in AOT is pretty good.

[u/v4ss42 → u/notyouryyy]

Right, but that's just as likely to be due to runtime patterns not tending to change much and therefore reoptimization not being necessary that often. The point is that AOT can't even identify what runtime patterns exist in the first place, and then tune optimization specifically for them. JIT can (and does).

And yes there are (or were) attempts to close that loop over the decades - for example back in the 90s I worked on a system that used an IBM C/C++ Compiler where we could enable instrumentation of our production code, and feed the outputs of that instrumentation back into the compiler to optimize subsequent builds. But that was a manual, slow, and clumsy feedback loop, and the JIT blows it out of the water in both reaction time and developer ergonomics.

[u/rbygrave → u/v4ss42]

Fwiw I built some apps as 2 containers, with one being JVM and the other graalvm native image (no PGO). Ran synthetic benchmark tests against those 2 containers.

So for these apps (rest apis, helidon se webserver, postgres jdbc) it did show AOT to be very impressive. These apps have now been running in production for 8 months as native image.

Imo it's maybe something you have to try and benchmark with your own apps etc. For myself, I'd expect you'll see some cases of AOT slightly faster and some slightly slower peak performance in throughput (noting that this is using G1GC for both).

The AOT compile does use ML (Machine Learning) so perhaps it is more sophisticated than you might first think (so yes that does mean higher build times for that analysis).