maybe | deciding whether you really want

That remark isn't an actual error, just a warning. The real error can be found in the nested Compile Swift source files under the top level section with the same name. Expand the logs for this command and you should see the actual error.

Combining my comment and the comment by @khelwood:

TL;DR:
When analysing the bytecode for the two comparisons, it reveals the 'time' and 'time' strings are assigned to the same object. Therefore, an up-front identity check (at C-level) is the reason for the increased comparison speed.

The reason for the same object assignment is that, as an implementation detail, CPython interns strings which contain only 'name characters' (i.e. alpha and underscore characters). This enables the object's identity check.

Bytecode:

This function were removed in TensorFlow version 2.6. According to the keras in rstudio reference

update to

It looks like GCC's naïveté about store-forwarding stalls is hurting its auto-vectorization strategy here. See also Store forwarding by example for some practical benchmarks on Intel with hardware performance counters, and What are the costs of failed store-to-load forwarding on x86? Also Agner Fog's x86 optimization guides.

(gcc -O3 enables -ftree-vectorize and a few other options not included by -O2, e.g. if-conversion to branchless cmov, which is another way -O3 can hurt with data patterns GCC didn't expect. By comparison, Clang enables auto-vectorization even at -O2, although some of its optimizations are still only on at -O3.)

It's doing 64-bit loads (and branching to store or not) on pairs of ints. This means, if we swapped the last iteration, this load comes half from that store, half from fresh memory, so we get a store-forwarding stall after every swap. But bubble sort often has long chains of swapping every iteration as an element bubbles far, so this is really bad.

(Bubble sort is bad in general, especially if implemented naively without keeping the previous iteration's second element around in a register. It can be interesting to analyze the asm details of exactly why it sucks, so it is fair enough for wanting to try.)

Anyway, this is pretty clearly an anti-optimization you should report on GCC Bugzilla with the "missed-optimization" keyword. Scalar loads are cheap, and store-forwarding stalls are costly. (Can modern x86 implementations store-forward from more than one prior store? no, nor can microarchitectures other than in-order Atom efficiently load when it partially overlaps with one previous store, and partially from data that has to come from the L1d cache.)

Even better would be to keep buf[x+1] in a register and use it as buf[x] in the next iteration, avoiding a store and load. (Like good hand-written asm bubble sort examples, a few of which exist on Stack Overflow.)

If it wasn't for the store-forwarding stalls (which AFAIK GCC doesn't know about in its cost model), this strategy might be about break-even. SSE 4.1 for a branchless pmind / pmaxd comparator might be interesting, but that would mean always storing and the C source doesn't do that.

If this strategy of double-width load had any merit, it would be better implemented with pure integer on a 64-bit machine like x86-64, where you can operate on just the low 32 bits with garbage (or valuable data) in the upper half. E.g.,

Unfortunately (?), the easiest way to know for sure what those categories map to is to look at OpenJDK source code. The NMT tag you are looking for is mtServiceability. This would show that "serviceability" are basically diagnostic interfaces in JDK/JVM: JVMTI, heap dumps, etc.

But the same kind of thing is clear from observing that stack trace sample you are showing mentions ThreadStackTrace::dump_stack_at_safepoint -- that is something that dumps the thread information, for example for jstack, heap dump, etc. If you have a suspicion for the memory leak in that code, you might try to build a MCVE demonstrating it, and submitting the bug against OpenJDK, or showing it to a fellow OpenJDK developer. You probably know better what your application is doing to cause thread dumps, focus there.

That being said, I don't see any obvious memory leaks in StackFrameInfo, neither can I reproduce any leak with stress tests, so maybe what you are seeing is "just" thread dumping over the larger and larger thread stacks. Or you capture it when thread dump is happening. Or... It is hard to say without the MCVE.

Update: After playing with MCVE, I realized that it reproduces with 17.0.1, but not with either mainline development JDK, or JDK 18 EA, or JDK 17.0.2 EA. I tested with 17.0.2 EA before, so was not seeing it, dang. Bisection between 17.0.1 and 17.0.2 EA shows it was fixed with JDK-8273902 backport. 17.0.2 releases this week, so the bug should disappear after you upgrade.

This is what solved it for me on my M1.

1. Go to Android Studio Preview and download the latest Canary build for Apple chip (Chipmunk). Don't worry this is just to get through the initial setup.
2. Unpack it, run it, let it install all the SDK components, accept licenses, etc as usual.
3. Once it's done, simply close it and delete it.

Now when you start your stable Android Studio (Arctic Fox) you should not see the error.

Laziness to the rescue. We can recursively define a list of all the sub-automata, such that their transitions index into that same list:

The very links you included in your question allow you to see the source code of each implementation which tells you that, no, there is no difference.

In fact elementAtOrNull literally just calls getOrNull.

If you're just going to reuse a property from a superclass but treat it as a narrower type, you should probably use the declare property modifier in the subclass instead of re-declaring the field: