Nitpicking here.

> JIT compiler can see that a function has never been passed a null object

It cannot. It can only see that function has never been passed a null object so far.

And the cost of such optimization might be that edge cases are way slower than anticipated. But what if those edge cases are actually the only thing that provide value for your business?

Imagine you're building a monitoring system that alerts very rarely, but reaction time is really critical for you. Do you really want to JIT compiler to optimize the alerting branch out because it hasn't seen any alerts yet?

My bet is that JIT compiler rarely does those optimizations by default, because it cannot know which branches in your code contain business value. I don't need my code to be twice as fast on average but twice as slow on critical branches.

Now, what would be great if profiler would make suggestions about how to modify your code for performance based on usage. But those should be approved and commented by humans, because humans know whether those are valuable optimizations or a fad.

> so far

This is truly a nit pick.

> Imagine you're building a monitoring system that alerts very rarely, but reaction time is really critical for you. Do you really want to JIT compiler to optimize the alerting branch out because it hasn't seen any alerts yet?

Yes I do, because once the constraints are invalidated the the runtime system will instruct the JIT to recompile the code with the now uncommon branch [0].

[0] https://shipilev.net/jvm/anatomy-quarks/29-uncommon-traps/

>> JIT compiler can see that a function has never been passed a null object

>>... so far.

>This is truly a nit pick.

It's not even a nitpick, it's just wrong - your original statement is true: you said "has never been". The "so far" reply you received mistakenly implies you said "will never be" (given that HotSpot will deoptimise+reoptimise if its assumptions are invalidated)

That said, the remainder of their point I think is reasonable in the extremely specific (I think to the point of being unrealistic, given this seems like a hand-optimised assembly level of perf importance they have alluded to) scenario they mentioned - the JIT optimiser can't know that the user wants to optimise an extremely rare branch for performance to the point where the overwhelmingly common case should be degraded.

Another useful jumping off point to explore for HotSpot's performance techniques is [1]

1: https://wiki.openjdk.org/display/HotSpot/PerformanceTechniqu...

Can you show me how exactly does the optimization/deoptimization of unused branch look like? You need detect that you got into an unused branch and that requires... a branch!

In order to be able to optimize presumably dead branches you need a primitive that:

1. Can detect entering "dead" branches

2. Is faster than branch.

I'm not saying JIT optimizations are not possible, JIT compiler can totally choose to inline function call based on frequency or loop length. It can make "better" time/space complexity decisions (though "better" is still going to be controversial).

But optimizing branches because they were not called in runtime seems like common a myth. Like previous commenter who confused startup optimization with code optimization.

1) You catch the SEGV signal you get when failing to read the address, then it is complicated, but it is similar to the mechanism used to reach safe points (also does not use jumps).

2) If there are no nulls, it is faster not to do a branch.

https://shipilev.net/jvm/anatomy-quarks/25-implicit-null-che...

> Java specification says that NullPointerException would be thrown when we access the null object fields. Does this mean the JVM has to always employ runtime checks for nullity?

Once again, we're talking about how JIT can be faster than AOT, not how to reduce JIT overhead. Those are two different types of optimization. You will never be faster than AOT just by reducing JIT overhead. Both links that were provided talk about JIT overhead alone.

This one talks about internal JVM optimizations. Not about optimizations JIT is capable of doing for your code.

What exactly are we even comparing when talking about NullPointerException? To my knowledge, most AOT-compiled languages don't even have NPE (not in Java sense at least). It's apples to oranges comparison.

If we have the code "if (x != null) { x.foo = 42; }", then it can rewritten as x.foo = 42 (with some extra magic in the runtime). If x is not null it will be faster if the cost of a branch is higher than zero. If x is null, the (slow) trick with SEGV can be used.

The trick of catching the SEGV can also be used by an AOT compiler (https://llvm.org/docs/FaultMaps.html), but the AOT compiler would need profiling data to do this optimization that is more expensive to do if the variable x happens to be null often. Even if you have profile data for your application, and that profile data will correspond to your application profile of this particular run (on average), you can not handle the case when x != null for five hours and then is set to null for five hours. If you use an AOT compiler you would have exponential blow-up of code generations for combinations of variables that can be null (if you would try to compile all combinations), and you would basically reinvent a JIT compiler --- badly.

Theoretically, a JIT can do anything an AOT can do, but the reverse is not true.

The article is talking about optimizations the JIT is capable of doing for your code. It is well written, and although it is talking about code throwing a NullPointerException, I think you can see that the same optimization can be done for my example at the top (that is applicable to c++ as well). So my comparison is not apples to oranges.

But your observation that most compiled languages do not have NullPointerExceptions is interesting. It could very much be because that it is too expensive with an AOT, have you thought about that?

Also, in an AOT-way you couldn’t really play the same SEGFAULT trick multiple times without some expensive tracking of where did it come from, where to return to, etc, at which point you are doing borderline JIT compiler with precached method bodies.

Well I guess the point is that with JIT you can do a lot of tricks based on the fact that a function always gets passed a null (or anything relatively constant) in practice even if that wouldn't be statically probable.

If that assumption gets violated a JIT can deopt and adjust later. In AOT you can only make the assumptions based on what the code can statically tell you.

Feels like we're going in circles. I guess that's what you get for nitpicking :)

JIT can indeed do a lot of tricks to reduce JIT overhead. But you can't present that as a benefit of JIT over AOT: AOT doesn't have JIT overhead at all.

JIT can definitely trade space for time. I bet that JIT will inline/memoize certain calls based on call frequency.

The only thing I'm arguing against is that low call frequency (like zero branch executions) somehow provides room for optimization compared to AOT. The only optimizations you can do in this case are optimizations for JIT overhead itself.

Anything beyond that is simply not possible: you can't eliminate a branch and detect the fact that you were supposed to enter eliminated branch at the same time: the simplest mechanism that allows you to do that is branching!

One trick that OpenJDK uses is optimization of null checks. Since in Java memory has to be initialized, null pointers will have the value 0. If a given function using a null check gets called many times, and all these times it was called with a non-null object the JVM can compile the branch totally away. If the function is finally called with ‘null’ the compiled code will try to load the memory at address zero, causing the TLB to trigger and the OS to send a segfault. This can be handled safely by the JVM which will interpret it in accordance with the above and will deoptimize the code (with all possible side effects reverted) and either run it in interpreted mode or an optimized mode that does contain the null check “properly”.

How will the runtime system know to invalidate and recompile the code, if the alert check isn't being done on every call?

At some level, the check for the alert has to be done on every call. If it isn't being done, the one time there should be an alert it will be missed. If it is being done, where is it being done if not in the optimised JITted code, and how is that code even more optimised than the JITted version would be? Why not just put that optimisation in the JITted code?

The terms you'll want to read up on are JIT speculation and deoptimisation. There's a whole range of things that can be done like trapping the null derefrence instead of an explicit check, once hot code is inlined the check could be lifted out several call frames outside of loops. Then you can ignore the check later.

All sortsa stuff can happen.

We're talking about optimizations that JIT can do compared to AOT.

The link you shared talks about skipping compilation for certain branches to speed up the JIT compilation. Such optimization makes startup faster, but it doesn't make code execution faster. AOT compiled languages don't have slow startup problem in the first place.

It's like saying paper books are worse than e-ink readers because you cannot charge paper books.

Eliding a branch makes not only compilation faster, but the resulting code as well. That’s how for example some logging statements become zero-cost when the debug level is set lower than their corresponding one. JIT compiled languages don’t care too deeply about startup, they shine with longer runtimes.

Are you sure you're not confusing zero-cost with almost zero-cost? Even zerolog talks about negligible costs after most aggressive optimizations, not zero: https://github.com/obsidiandynamics/zerolog

If you're just using constants in your code to set logging level, AOT compilation can do exactly the same optimizations.

AOT would require recompilation and deployment. JIT can do it for the same code with final params.

When the log level is not final, branch prediction Will ensure JIT code is faster.

JIT compilers do exactly that. Not only do they have more information, they are also allowed to -- and very frequently do -- perform speculative optimisations. If proven wrong, they incur "deoptimisations" and then recompile with more information.

I don't believe this is right. The optimization as described does not preserve the correct program behavior unless you have a guard to do the null check anyway to fallback to the slow path

edit: just read kaba0 answer below and in fact it's possible via tlb miss, really cool