It's easily imaginable that there are new CPU features that would help with building an efficient Java VM, if that's the CPU's primary purpose. Just from the top of my head, one might want a form of finer-grainer memory virtualization that could enable very cheap concurrent garbage collection.
But having Java bytecode as the actual instruction set architecture doesn't sound too useful. It's true that any modern processor has a "compilation step" into microcode anyway, so in an abstract sense, that might as well be some kind of bytecode. But given the high-level nature of Java's bytecode instructions in particular, there are certainly some optimizations that are easy to do in a software JIT, and that just aren't practical to do in hardware during instruction decode.
What I can imagine is a purpose-built CPU that would make the JIT's job a lot easier and faster than compiling for x86 or ARM. Such a machine wouldn't execute raw Java bytecode, rather, something a tiny bit more low-level.
> What I can imagine is a purpose-built CPU that would make the JIT's job a lot easier and faster than compiling for x86 or ARM. Such a machine wouldn't execute raw Java bytecode, rather, something a tiny bit more low-level.
This is approximately exactly what Azul Systems did, doing a bog-standard RISC with hardware GC barriers and transactional memory. Cliff Click gave an excellent talk on it [0] and makes your argument around 20:14.
Running Java workloads is very important for most CPUs these days, and both ARM and Intel consult with the Java team on new features (although Java's needs aren't much different from those of C++). But while you're right that with modern JITs, executing Java bytecode directly isn't too helpful, our concurrent collectors are already very efficient (they could, perhaps, take advantage of new address masking features).
I think there's some disconnect between how people imagine GCs work and how the JVMs newest garbage collectors actually work. Rather than exacting a performance cost, they're more often a performance boost compared to more manual or eager memory management techniques, especially for the workloads of large, concurrent servers. The only real cost is in memory footprint, but even that is often misunderstood, as covered beautifully in this recent ISMM talk (that I would recommend to anyone interested in memory management of any kind): https://youtu.be/mLNFVNXbw7I. The key is that moving-tracing collectors can turn available RAM into CPU cycles, and some memory management techniques under-utilise available RAM.
So, the guys at Azul actually had this sort of business plan back in 2005, but they found that it was unsustainable and turned their attention to the software side, where they have done great work. I remember having a discussion with someone about Java processors and my common was just “Lisp machines.” It’s very difficult to outperform code running on commodity processor architectures. That train is so big and moving so fast, you really have to pick your niche (e.g. GPUs) to deliver something that outperforms it. Too much investment ($$$ and brainpower) flowing that direction. Even if you’re successful for one generation, you need to grow sales and have multiple designs in the pipeline at once. It’s nearly impossible.
That said, I do see opportunities to add “assistance hardware” to commodity architectures. Given the massive shift to managed runtimes, all of which use GC, over the last couple decades, it’s shocking to me that nobody has added a “store barrier” instruction or something like that. You don’t need to process Java in hardware or even do full GC in hardware, but there are little helps you could give that would make a big difference, similar to what was done with “multimedia” and crypto instructions in x86 originally.
There are also load and store barriers which add work when accessing objects from the heap. In many cases, adding work in the parallel path is good if it allows you to avoid single-threaded sections, but not in all cases. Single-threaded programs with a lot of reads can be pretty significantly impacted by barriers,
Sure, but other forms of memory management are costly, too. Even if you allocate everything from the OS upfront and then pool stuff, you still need to spend some computational work on the pool [1]. Working with bounded memory necessarily requires spending at least some CPU on memory management. It's not that the alternative to barriers is zero CPU spent on memory management.
> The Parallel GC is still useful sometimes!
Certainly for batch-processing programs.
BTW, the paper you linked is already at least somewhat out of date, as it's from 2021. The implementation of the GCs in the JDK changes very quickly. The newest GC in the JDK (and one that may be appropriate for a very large portion of programs) didn't even exist back then, and even G1 has changed a lot since. (Many performance evaluations of HotSpot implementation details may be out of date after two years.)
[1]: The cheapest, which is similar in some ways to moving-tracing collectors, especially in how it can convert RAM to CPU, is arenas, but they can have other kinds of costs.
The difference with manual memory management or parallel GC is that concurrent GCs create a performance penalty on every reads and writes (modulo what the JIT can elide). That performance penalty is absolutely measurable even with the most recent GCs. If you look at the assembly produced for the same code running with ZGC and Parallel, you’ll see that read instructions translate to way more cpu instructions in the former. We were just looking at a bug (in our code) at work this week, on Java 25 that was exposed by the new G1 barrier late expansion.
Different applications will see different overall performance changes (positive or negative) with different GCs. I agree with you that most applications (especially realistic multi threaded ones representative of the kind of work that people do on the JVM) benefit from the amazing GC technology that the JVM brings. It is absolutely not the case however that the only negative impact is on memory footprint.
> The difference with manual memory management or parallel GC is that concurrent GCs create a performance penalty on every reads and writes
Not on every read and write, but it could be on every load and store of a reference (i.e. reading a reference from the heap to a register or writing a reference from a register to the heap). But what difference does it make where exactly the cost is? What matters is how much CPU is spent on memory management (directly or indirectly) in total and how much latency memory management can add. You are right that the low-latency collectors do use up more CPU overall than a parallel STW collector, but so does manual memory management (unless you use arenas well).
> It's true that any modern processor has a "compilation step" into microcode anyway, so in an abstract sense, that might as well be some kind of bytecode.
This.
> What I can imagine is a purpose-built CPU that would make the JIT's job a lot easier and faster than compiling for x86 or ARM. Such a machine wouldn't execute raw Java bytecode, rather, something a tiny bit more low-level.
My prediction is that eventually a lot of software will be written in such a way that it runs in "kernel mode" using a memory-safe VM to avoid context switches, so reading/writing to pipes, and accessing pages corresponding to files reduces down to function calls, which easily happen billions of times per second, as opposed to "system calls" or page faults which only happen 10 or 20 million times per second due to context switching.
This is basically what eBPF is used for today. I don't know if it will expand to be the VM that I'm predicting, or if kernel WASM [1] or something else will take over.
From there, it seems logical that CPU manufacturers would provide compilers ("CPU drivers"?) that turn bytecode into "microcode" or whatever the CPU circuitry expects to be in the CPU during execution, skipping the ISA. This compilation could be done in the form of JIT, though it could also be done AOT, either during installation (I believe ART in Android already does something similar [0], though it currently emits standard ISA code such as aarch64) or at the start of execution when it finds that there's no compilation cache entry for the bytecode blob (the cache could be in memory or on disk, managed by the OS).
Doing some of the compilation to "microcode" in regular software before execution rather than using special CPU code during execution should allow for more advanced optimisations. If there are patterns where this is not the case (eg, where branch prediction depends on runtime feedback), the compilation output can still emit something analogous to what the ISAs represent today. The other advantage is of course that CPU manufacturers are more free to perform hardware-specific optimisations, because the compiler isn't targeting a common ISA.
Jazelle worked for its target market (or at least, I've never seen anyone claim otherwise).
But its target market wasn't "faster java". Instead Jazelle promised better performance than an interpreter, with lower power draw than an interpreter, but without the memory footprint and complexity of a JIT. It was never meant to be faster than a JIT.
Jazelle made a lot of sense in the early 2000s where dumb phones where running J2ME applets on devices with only 1-4MB of memory, but we quickly moved onto smartphones with 64MB+ of memory, and it just made more sense to use a proper JIT.
---------
JavaStation might as well been vaporware. Sure, the product line existed, but the promised "Super JavaStation" with a "java coprocessor" never arrived, so you were really just paying sun for a standard computer with Java pre-installed.
I briefly worked in a team that implemented a JVM on a mobile OS (before the iPhone) and one of the senior devs said Jazelle was in effect very inefficient because of all the context switching between ARM mode and Jazelle mode.
Turned out a carefully tuned ARM JVM was in practice th best
The JavaStation is what led me to this. They sucked, Java OS sucked, and the whole idea was DOA precisely because they didn't do something like this and instead decided to make a shitty sparc machine for the 5 people that wanted a Java branded thin client
I never understood why AOT never took off for Java. The write once run anywhere quickly faded as an argument, the number of platforms that a software package needs to support is rather small.
Developers pay for tools gladly when the pricing model isn’t based on how much money you’re making.
I’m happy to drop a fixed 200e/mo on Claude but I’d never sign paperwork that required us to track user installs and deliver $0.02 per install to someone
Especially not if those kind of contracts don't survive an acquisition because then your acquisition is most likely dead in the water. The acquirer would have to re-negotiate the license and with a little luck they'd be screwed over because they have nowhere else to go.
That is something that I never understood, that that's even legal. You enter into an agreement (let's call it a contract, because that's how the other side treats it) and then, retroactively they get to pull the rug right out from under you.
I made the 'FOSS all the way' decision somewhere in '96 or so but unfortunately our bookkeeping system and our own software package only worked on Windows (this was an end-user thing) so we had to keep one windows machine around. I was pretty happy when we finally switched it off.
The funny thing is that I wouldn't even know where to start to develop on/for mac or windows, Linux just feels so much more powerful in that sense. Yes, it has some sharp edges but for the most part it is the best thing that could have happened to the world of software development.
I have done native cross-platform projects in https://wxwidgets.org/ and https://quasar.dev/ . Fine for basic interfaces, but static linking on Win64 gets dicey with lgpl libraries etc. YMMV
For iOS targets, one must use a MacOS environment with a non-free Apple developer account.
Personally, I like Apache 2.0, and standard quality of life *nix build tools. Everything Windows runs off a frozen VM backing image KVM COW file now, as even Microsoft can no longer resist the urge to break things. =3
Depends on the use-case, anyone that has seen the commercial host scaling cost of options like MATLAB usually ported to another language. lesson learned...
Commercial licensing is simply a variable cost, and if there is another FOSS option most people will make the right call. Some commercial licenses are just Faustian bargains, that can cost serious money to escape. =3
If we ignore gcj was never production ready, and basically the only good case that Red-Hat sponsored was to compile Eclipse, which was usually slower than
using the JIT anyway.
And that around 2009, most of the team left the project, some went to OpenJDK, others elsewhere, while GCC kept it around because gcj unit tests stressed parts of the GCC that weren't tested by other frontends, until the decision came to remove it completly.
As side note, I expect a similar outcome to gccgo, abandoned since Go added generics support.
Actually you do indirectly, via Windows licenses, Office, Azure, Visual Studio Professional and Ultimate licenses, C# DevKit.
Also you are forgetting AOT first came with NGEN, .NET Native, commercial, and on Mono side, Xamarin had some price points for AOT optimiztions, if I recall correctly.
However this is a moot point, you also don't pay for GraalVM, OpenJ9, or Android.
> I never understood why AOT never took off for Java.
GraalVM native images certainly are being adopted, the creation of native binaries via GraalVM is seamlessly integrated into stacks like Quarkus or Spring Boot. One small example would be kcctl, a CLI client for Kafka Connect (https://github.com/kcctl/kcctl/). I guess it boils down to the question of what constitutes "taking off" for you?
But it's also not that native images are unambiguously superior to running on the JVM. Build times definitely leave to be desired, not all 3rd party libraries can easily be used, not all GCs are supported, the closed world assumption is not always practical, peak performance may also be better with JIT. So the way I see it, AOT compiled apps are seen as a tactical tool by the Java community currently, utilized when their advantages (e.g. fast start-up) matter.
That said, interesting work is happening in OpenJDK's Project Leyden, which aims to move more work to AOT while being less disruptive to the development experience than GraalVM native binaries. Arguably, if you're using CDS, you are using AOT.
It simply defaults to an open world where you could just load a class from any source at any time to subclass something, or straight up apply some transformation to classes as they load via instrumentation. And defaults matter, so AOT compilation is not completely trivial (though it's not too bad either with GraalVM's native image, given that the framework you use (if any) supports it).
Meanwhile most "AOT-first" languages assume a closed-world where everything "that could ever exist" is already known fully.
I'm not sure how much Hotspot can do this, but JIT means you can target different CPUs, taking advantage of specific extensions or CPU quirks. It can also mean better cache performance because you don't need branches to handle different chips, so the branch is gone and the code is smaller.
We did see a recent attempt to do hardware-based memory management again with Vypercore, but they ran out of money.
I think part of the problem with any performance-related microarchitectural innovation is that unless you are one of the big players (i.e. Qualcomm, Apple, Intel, AMD, Nvidia) then you already have a significant performance disadvantage just due to access to process nodes and design manpower. So unless you have an absolutely insane performance trick, it's still not going to make sense to buy your chip.
They have the volume as well, if you do carve out a niche they’ll just add it and roll over you.
That’s held for decades though I think it only really worked when computers where doubling in speed every 12-18 months, for a while they scaled horizontally (more cores) over radical IPC improvements so we might see the rise of proper co-processors again (but nothing stops the successful ones getting put on die, like Strix Point is already heading).
It's easily imaginable that there are new CPU features that would help with building an efficient Java VM, if that's the CPU's primary purpose. Just from the top of my head, one might want a form of finer-grainer memory virtualization that could enable very cheap concurrent garbage collection.
But having Java bytecode as the actual instruction set architecture doesn't sound too useful. It's true that any modern processor has a "compilation step" into microcode anyway, so in an abstract sense, that might as well be some kind of bytecode. But given the high-level nature of Java's bytecode instructions in particular, there are certainly some optimizations that are easy to do in a software JIT, and that just aren't practical to do in hardware during instruction decode.
What I can imagine is a purpose-built CPU that would make the JIT's job a lot easier and faster than compiling for x86 or ARM. Such a machine wouldn't execute raw Java bytecode, rather, something a tiny bit more low-level.
> What I can imagine is a purpose-built CPU that would make the JIT's job a lot easier and faster than compiling for x86 or ARM. Such a machine wouldn't execute raw Java bytecode, rather, something a tiny bit more low-level.
This is approximately exactly what Azul Systems did, doing a bog-standard RISC with hardware GC barriers and transactional memory. Cliff Click gave an excellent talk on it [0] and makes your argument around 20:14.
[0] https://www.youtube.com/watch?v=5uljtqyBLxI
I imagine that's where the request for finer grained virtualization comes from
Running Java workloads is very important for most CPUs these days, and both ARM and Intel consult with the Java team on new features (although Java's needs aren't much different from those of C++). But while you're right that with modern JITs, executing Java bytecode directly isn't too helpful, our concurrent collectors are already very efficient (they could, perhaps, take advantage of new address masking features).
I think there's some disconnect between how people imagine GCs work and how the JVMs newest garbage collectors actually work. Rather than exacting a performance cost, they're more often a performance boost compared to more manual or eager memory management techniques, especially for the workloads of large, concurrent servers. The only real cost is in memory footprint, but even that is often misunderstood, as covered beautifully in this recent ISMM talk (that I would recommend to anyone interested in memory management of any kind): https://youtu.be/mLNFVNXbw7I. The key is that moving-tracing collectors can turn available RAM into CPU cycles, and some memory management techniques under-utilise available RAM.
So, the guys at Azul actually had this sort of business plan back in 2005, but they found that it was unsustainable and turned their attention to the software side, where they have done great work. I remember having a discussion with someone about Java processors and my common was just “Lisp machines.” It’s very difficult to outperform code running on commodity processor architectures. That train is so big and moving so fast, you really have to pick your niche (e.g. GPUs) to deliver something that outperforms it. Too much investment ($$$ and brainpower) flowing that direction. Even if you’re successful for one generation, you need to grow sales and have multiple designs in the pipeline at once. It’s nearly impossible.
That said, I do see opportunities to add “assistance hardware” to commodity architectures. Given the massive shift to managed runtimes, all of which use GC, over the last couple decades, it’s shocking to me that nobody has added a “store barrier” instruction or something like that. You don’t need to process Java in hardware or even do full GC in hardware, but there are little helps you could give that would make a big difference, similar to what was done with “multimedia” and crypto instructions in x86 originally.
> The only real cost is in memory footprint
There are also load and store barriers which add work when accessing objects from the heap. In many cases, adding work in the parallel path is good if it allows you to avoid single-threaded sections, but not in all cases. Single-threaded programs with a lot of reads can be pretty significantly impacted by barriers,
https://rodrigo-bruno.github.io/mentoring/77998-Carlos-Gonca...
The Parallel GC is still useful sometimes!
Sure, but other forms of memory management are costly, too. Even if you allocate everything from the OS upfront and then pool stuff, you still need to spend some computational work on the pool [1]. Working with bounded memory necessarily requires spending at least some CPU on memory management. It's not that the alternative to barriers is zero CPU spent on memory management.
> The Parallel GC is still useful sometimes!
Certainly for batch-processing programs.
BTW, the paper you linked is already at least somewhat out of date, as it's from 2021. The implementation of the GCs in the JDK changes very quickly. The newest GC in the JDK (and one that may be appropriate for a very large portion of programs) didn't even exist back then, and even G1 has changed a lot since. (Many performance evaluations of HotSpot implementation details may be out of date after two years.)
[1]: The cheapest, which is similar in some ways to moving-tracing collectors, especially in how it can convert RAM to CPU, is arenas, but they can have other kinds of costs.
The difference with manual memory management or parallel GC is that concurrent GCs create a performance penalty on every reads and writes (modulo what the JIT can elide). That performance penalty is absolutely measurable even with the most recent GCs. If you look at the assembly produced for the same code running with ZGC and Parallel, you’ll see that read instructions translate to way more cpu instructions in the former. We were just looking at a bug (in our code) at work this week, on Java 25 that was exposed by the new G1 barrier late expansion.
Different applications will see different overall performance changes (positive or negative) with different GCs. I agree with you that most applications (especially realistic multi threaded ones representative of the kind of work that people do on the JVM) benefit from the amazing GC technology that the JVM brings. It is absolutely not the case however that the only negative impact is on memory footprint.
> The difference with manual memory management or parallel GC is that concurrent GCs create a performance penalty on every reads and writes
Not on every read and write, but it could be on every load and store of a reference (i.e. reading a reference from the heap to a register or writing a reference from a register to the heap). But what difference does it make where exactly the cost is? What matters is how much CPU is spent on memory management (directly or indirectly) in total and how much latency memory management can add. You are right that the low-latency collectors do use up more CPU overall than a parallel STW collector, but so does manual memory management (unless you use arenas well).
> It's true that any modern processor has a "compilation step" into microcode anyway, so in an abstract sense, that might as well be some kind of bytecode.
This.
> What I can imagine is a purpose-built CPU that would make the JIT's job a lot easier and faster than compiling for x86 or ARM. Such a machine wouldn't execute raw Java bytecode, rather, something a tiny bit more low-level.
My prediction is that eventually a lot of software will be written in such a way that it runs in "kernel mode" using a memory-safe VM to avoid context switches, so reading/writing to pipes, and accessing pages corresponding to files reduces down to function calls, which easily happen billions of times per second, as opposed to "system calls" or page faults which only happen 10 or 20 million times per second due to context switching.
This is basically what eBPF is used for today. I don't know if it will expand to be the VM that I'm predicting, or if kernel WASM [1] or something else will take over.
From there, it seems logical that CPU manufacturers would provide compilers ("CPU drivers"?) that turn bytecode into "microcode" or whatever the CPU circuitry expects to be in the CPU during execution, skipping the ISA. This compilation could be done in the form of JIT, though it could also be done AOT, either during installation (I believe ART in Android already does something similar [0], though it currently emits standard ISA code such as aarch64) or at the start of execution when it finds that there's no compilation cache entry for the bytecode blob (the cache could be in memory or on disk, managed by the OS).
Doing some of the compilation to "microcode" in regular software before execution rather than using special CPU code during execution should allow for more advanced optimisations. If there are patterns where this is not the case (eg, where branch prediction depends on runtime feedback), the compilation output can still emit something analogous to what the ISAs represent today. The other advantage is of course that CPU manufacturers are more free to perform hardware-specific optimisations, because the compiler isn't targeting a common ISA.
Anyway, these are my crazy predictions.
[0] https://source.android.com/docs/core/runtime/jit-compiler
[1] https://github.com/wasmerio/kernel-wasm (outdated)
I know it prolly isn't the most practical, but neither were lisp machines and we still love them
What happened to
1. Sun's JavaStation, 2. ARM's Jazelle, ??? 3. Profit!
Jazelle worked for its target market (or at least, I've never seen anyone claim otherwise).
But its target market wasn't "faster java". Instead Jazelle promised better performance than an interpreter, with lower power draw than an interpreter, but without the memory footprint and complexity of a JIT. It was never meant to be faster than a JIT.
Jazelle made a lot of sense in the early 2000s where dumb phones where running J2ME applets on devices with only 1-4MB of memory, but we quickly moved onto smartphones with 64MB+ of memory, and it just made more sense to use a proper JIT.
---------
JavaStation might as well been vaporware. Sure, the product line existed, but the promised "Super JavaStation" with a "java coprocessor" never arrived, so you were really just paying sun for a standard computer with Java pre-installed.
I briefly worked in a team that implemented a JVM on a mobile OS (before the iPhone) and one of the senior devs said Jazelle was in effect very inefficient because of all the context switching between ARM mode and Jazelle mode. Turned out a carefully tuned ARM JVM was in practice th best
The JavaStation is what led me to this. They sucked, Java OS sucked, and the whole idea was DOA precisely because they didn't do something like this and instead decided to make a shitty sparc machine for the 5 people that wanted a Java branded thin client
It's more like JITs got good.
I never understood why AOT never took off for Java. The write once run anywhere quickly faded as an argument, the number of platforms that a software package needs to support is rather small.
Because developers don't like to pay for tools.
https://en.wikipedia.org/wiki/Excelsior_JET
https://www.ptc.com/en/products/developer-tools/perc
https://www.aicas.com/products-services/jamaicavm/
It is now getting adopted because GraalVM and OpenJ9 are available for free.
Also while not being proper Java, Android does AOT since version 5, mixed JIT/AOT since version 7.
EDIT: Fixed the sentence regarding Android versions.
Developers pay for tools gladly when the pricing model isn’t based on how much money you’re making.
I’m happy to drop a fixed 200e/mo on Claude but I’d never sign paperwork that required us to track user installs and deliver $0.02 per install to someone
Especially not if those kind of contracts don't survive an acquisition because then your acquisition is most likely dead in the water. The acquirer would have to re-negotiate the license and with a little luck they'd be screwed over because they have nowhere else to go.
I have seen worse, where people updated the EULA 6 months after being paid $14k/seat.
Now it is FOSS all the way... lesson learned... =3
https://www.youtube.com/watch?v=WpE_xMRiCLE
That is something that I never understood, that that's even legal. You enter into an agreement (let's call it a contract, because that's how the other side treats it) and then, retroactively they get to pull the rug right out from under you.
I made the 'FOSS all the way' decision somewhere in '96 or so but unfortunately our bookkeeping system and our own software package only worked on Windows (this was an end-user thing) so we had to keep one windows machine around. I was pretty happy when we finally switched it off.
The funny thing is that I wouldn't even know where to start to develop on/for mac or windows, Linux just feels so much more powerful in that sense. Yes, it has some sharp edges but for the most part it is the best thing that could have happened to the world of software development.
I have done native cross-platform projects in https://wxwidgets.org/ and https://quasar.dev/ . Fine for basic interfaces, but static linking on Win64 gets dicey with lgpl libraries etc. YMMV For iOS targets, one must use a MacOS environment with a non-free Apple developer account.
Personally, I like Apache 2.0, and standard quality of life *nix build tools. Everything Windows runs off a frozen VM backing image KVM COW file now, as even Microsoft can no longer resist the urge to break things. =3
I think what they do is correct. We also need to get paid this way.
Depends on the use-case, anyone that has seen the commercial host scaling cost of options like MATLAB usually ported to another language. lesson learned...
Commercial licensing is simply a variable cost, and if there is another FOSS option most people will make the right call. Some commercial licenses are just Faustian bargains, that can cost serious money to escape. =3
You could do AOT Java using gcj, it didn't need commercial tools.
If we ignore gcj was never production ready, and basically the only good case that Red-Hat sponsored was to compile Eclipse, which was usually slower than using the JIT anyway.
And that around 2009, most of the team left the project, some went to OpenJDK, others elsewhere, while GCC kept it around because gcj unit tests stressed parts of the GCC that weren't tested by other frontends, until the decision came to remove it completly.
As side note, I expect a similar outcome to gccgo, abandoned since Go added generics support.
You don't have to pay for dotnet AOT.
Actually you do indirectly, via Windows licenses, Office, Azure, Visual Studio Professional and Ultimate licenses, C# DevKit.
Also you are forgetting AOT first came with NGEN, .NET Native, commercial, and on Mono side, Xamarin had some price points for AOT optimiztions, if I recall correctly.
However this is a moot point, you also don't pay for GraalVM, OpenJ9, or Android.
You don’t have to pay for Java AOT either. Graal is free.
> I never understood why AOT never took off for Java.
GraalVM native images certainly are being adopted, the creation of native binaries via GraalVM is seamlessly integrated into stacks like Quarkus or Spring Boot. One small example would be kcctl, a CLI client for Kafka Connect (https://github.com/kcctl/kcctl/). I guess it boils down to the question of what constitutes "taking off" for you?
But it's also not that native images are unambiguously superior to running on the JVM. Build times definitely leave to be desired, not all 3rd party libraries can easily be used, not all GCs are supported, the closed world assumption is not always practical, peak performance may also be better with JIT. So the way I see it, AOT compiled apps are seen as a tactical tool by the Java community currently, utilized when their advantages (e.g. fast start-up) matter.
That said, interesting work is happening in OpenJDK's Project Leyden, which aims to move more work to AOT while being less disruptive to the development experience than GraalVM native binaries. Arguably, if you're using CDS, you are using AOT.
Well, one aspect is how dynamic the platform is.
It simply defaults to an open world where you could just load a class from any source at any time to subclass something, or straight up apply some transformation to classes as they load via instrumentation. And defaults matter, so AOT compilation is not completely trivial (though it's not too bad either with GraalVM's native image, given that the framework you use (if any) supports it).
Meanwhile most "AOT-first" languages assume a closed-world where everything "that could ever exist" is already known fully.
Except when they support dynamic linking they pay the indirect call cost that JITs can remove.
I'm not sure how much Hotspot can do this, but JIT means you can target different CPUs, taking advantage of specific extensions or CPU quirks. It can also mean better cache performance because you don't need branches to handle different chips, so the branch is gone and the code is smaller.
dynamic class loading is a major issue, and it's an integral feature. Realistically, there are very few cases that AOT and Java make sense.
People want to run things other than Java.
We did see a recent attempt to do hardware-based memory management again with Vypercore, but they ran out of money.
I think part of the problem with any performance-related microarchitectural innovation is that unless you are one of the big players (i.e. Qualcomm, Apple, Intel, AMD, Nvidia) then you already have a significant performance disadvantage just due to access to process nodes and design manpower. So unless you have an absolutely insane performance trick, it's still not going to make sense to buy your chip.
They have the volume as well, if you do carve out a niche they’ll just add it and roll over you.
That’s held for decades though I think it only really worked when computers where doubling in speed every 12-18 months, for a while they scaled horizontally (more cores) over radical IPC improvements so we might see the rise of proper co-processors again (but nothing stops the successful ones getting put on die, like Strix Point is already heading).
(2016)
Related: JOP: A Java Optimized Processor for Embedded Real-Time Systems (2005) [0]
It's an implementation of the Java virtual machine in hardware, also FPGA-based, see chapter 7.1 Hardware Platforms.
[0] https://backend.orbit.dtu.dk/ws/files/4127855/thesis.pdf