There’s a chance that framework might build something. Lately they’ve been asking what to build next and modular phones were one of the most frequent answers. While their parts are not fully open source, their interfaces between the modules and the firmware are. For the laptops, you can already replace basically anything with a custom version.
It’s not even an issue with java. Apps ran fine on the original Android devices with single core CPUs and half a gig of RAM or less. It’s just that developers get lazier as more powerful hardware become available. Nobody cares about writing well optimized code anymore.
If Google and Apple required all apps to run smoothly on low end hardware from 5 years ago, we would be using our phones until the wear out rather than having to upgrade every couple of years if the batteries are replaceable.
Not sure why you get Apple into this. Apps on iOS have been natively compiled from the beginning and they are amazing at running stuff on older hardware. My current iPhone 12 Mini is over three years old and smoothly runs everything I throw at it. Before that I had a 2016 iPhone SE for about four years and only replaced it because I wanted something with a better camera (I’m a semi-professional photographer so I want something decent for when I see something cool and don’t have my big camera with me). I gave the SE to my mom and she used it for another two years until she decided she needed a bigger screen. It probably still works and it got its last OS update just two months ago.
As long as you don’t run something super hardware hungry, you can easily use an iPhone for at least five years without any problems. Even if the battery dies halfway through, there are dozens of repair shops around that will replace it for a reasonable price in case you’re not comfortable with opening up the phone on your own.
Software optimization is mostly not a language-level problem. I’ll be dailying my 3-year-old OnePlus 9 Pro until it starts missing out on security updates, but it will probably still be “usable” long after that. Support/updates aside, my 6-year-old galaxy s9 can still run most normal apps. Hell, I got the most recent lineageOS running on a pixel 2 XL from the year before that and it straight up felt fast as long as I wasn’t playing some super intensive game or something. This isn’t an android vs. iOS problem, it’s a “developers of [insert flashy new app here] either not bothering to put effort in to optimize their code or being forced to push out a minimum viable product ASAP” problem.
Android has actually employed a hybrid JIT/AOT compilation model for a long time.
The application bytecode is only interpreted on first run and afterwards if there’s no cached JIT compilation for it. The runtime AOT compiles well-known methods and then profiles the application to identify targets for asynchronous JIT compilation when the device is idle and charging (so no excess battery drain): https://source.android.com/docs/core/runtime/configure#how_art_works
Compiling on the device allows the use of profile-guided optimizations (PGO), as well as the use of any non-baseline CPU features the device has, like instruction set extensions or later revisions (e.g. ARMv8.5-A vs ARMv8).
If apps had to be distributed entirely as compiled object code, you’d either have to pre-compile artifacts for every different architecture and revision you plan to support, or choose a baseline to compile against and then use feature detection at runtime, which adds branches to potentially hot code paths.
It would also require the developer to manually gather profiling data if they wanted to utilize PGO, which may limit them to just the devices they have on-hand, or paying through the nose for a cloud testing service like that offered by Firebase.
This is not to mention the massive improvement to the developer experience from not having to wait several minutes for your app to compile to test out each change. Call it laziness all you want, but it’s risky to launch a platform when no one wants to develop apps for it.
Any experienced Android dev will tell you it does kinda suck anyways, but it’d suck way worse if it was all C++ instead. I’d take Android development over iOS development any day of the week though. XCode is one of the worst software products ever conceived, and you’re forced to use it to build anything for iOS.
I know about all this — I actually began implementing my own JVM language a few days ago. I know Android uses Dalvik btw. But I guess a lot of people can use this info; infodump is always good. I do that.
btw I actually have messed around with libgcc-jit and I think at least on x86, it makes zero difference. I once did a test:
– Find /e/ with MAWK -> 0.9s
– Find /e/ with JAWK -> 50s.
No shit! It’s seriously slow.
Now compare this with go-awk: 19s.
Go has reference counting and heap etc, basically a ‘compiled VM’. I think if you want fast code, ditch runtime.
Actually, Android doesn’t really use Dalvik anymore. They still use the bytecode format, but built a new runtime. The architecture of that runtime is detailed on the page I linked. IIRC, Dalvik didn’t cache JIT compilation results and had to redo it every time the application was run.
FWIW, I’ve heard libgcc-jit doesn’t generate particularly high quality code. If the AOT compiled code was compiled with aggressive optimizations and a specific CPU in mind, of course it’ll be faster. JIT compiled code can meet or exceed native performance, but it depends on a lot of variables.
As for mawk vs JAWK vs go-awk, a JIT is not going to fix bad code. If it were a true apples to apples comparison, I’d expect a difference of maybe 30-50%, not ~2 orders of magnitude. A performance gap that wide suggests fundamental differences between the different implementations, maybe bad cache locality or inefficient use of syscalls in the latter two.
On top of that, you’re not really comparing the languages or runtimes so much as their regular expression engines. Java’s isn’t particularly fast, and neither is Go’s. Compare that to Javascript and Perl, both languages with heavyweight runtimes, but which perform extraordinarily well on this benchmark thanks to their heavily optimized regex engines.
It looks like mawk uses its own bespoke regex engine, which is honestly quite impressive in that it performs that well. However, it only supports POSIX regular expressions, and doesn’t even implement braces, at least in the latest release listed on the site: https://github.com/ThomasDickey/mawk-20140914
(The author creates a new Github repo to mirror each release, which shows just how much they refuse to learn to use Git. That’s a respectable level of contempt right there.)
Meanwhile, Java’s regex engine is a lot more complex with more features, such as lookahead/behind and backreferences, but that complexity comes at a cost. Similarly, if go-awk is using Go’s regexp package, it’s using a much more complex regex engine than is strictly necessary. And Golang admits in their own FAQ that it’s not nearly as optimized as other engines like PCRE.
Thus, it’s really not an apples to apples comparison. I suspect that’s where most of the performance difference arises.
Go has reference counting and heap etc, basically a ‘compiled VM’.
This statement is completely wrong. Like, to a baffling degree. It kinda makes me wonder if you’re trolling.
Go doesn’t use any kind of VM, and has never used reference counting for memory management as far as I can tell. It compiles directly to native machine code which is executed directly by the processor, but the binary comes with a runtime baked in. This runtime includes a tracing garbage collector and manages the execution of goroutines and related things like non-blocking sockets.
Additionally, heap management is a core function of any program compiled for a modern operating system. Programs written in C and C++ use heap allocations constantly unless they’re specifically written to avoid them. And depending on what you’re doing and what you need, a C or C++ program could end up with a more heavyweight collective of runtime dependencies than the JVM itself.
At the end of the day, trying to write the fastest code possible isn’t usually the most productive approach. When you have a job to do, you’re going to welcome any tool that makes that job easier.
This statement is completely wrong. Like, to a baffling degree. It kinda makes me wonder if you’re trolling.
No I just struggle at getting my meaning across + these stuff are new to me. What I meant was ‘Go does memory management LIKE a VM does’. Like ‘baking in the GC’. Does that make sense? Or am I still wrong?
Issue is incentive. Developers use what they are told by more senior developers and most rewrites and tech debt work is deemed unprofitable and dropped.
They use shit like electron to write things once. Its always the worst experience but it seems to management on paper to be a huge win.
I’m still waiting for modular phones to be more mainstream. Tired of the ewaste. Tired of the anti consumer practices. Tired of planned obsolescence.
We need some that are fully open source software and hardware. I’m tired of all of the proprietary crap.
closest we’re getting for now is Fairphone
Wasn’t fairphone a subscription-based service?
There’s a chance that framework might build something. Lately they’ve been asking what to build next and modular phones were one of the most frequent answers. While their parts are not fully open source, their interfaces between the modules and the firmware are. For the laptops, you can already replace basically anything with a custom version.
I think that happens when app developers learn2optimize. Stop using interpreted bytecode languages on small processors!
It’s not even an issue with java. Apps ran fine on the original Android devices with single core CPUs and half a gig of RAM or less. It’s just that developers get lazier as more powerful hardware become available. Nobody cares about writing well optimized code anymore.
If Google and Apple required all apps to run smoothly on low end hardware from 5 years ago, we would be using our phones until the wear out rather than having to upgrade every couple of years if the batteries are replaceable.
Not sure why you get Apple into this. Apps on iOS have been natively compiled from the beginning and they are amazing at running stuff on older hardware. My current iPhone 12 Mini is over three years old and smoothly runs everything I throw at it. Before that I had a 2016 iPhone SE for about four years and only replaced it because I wanted something with a better camera (I’m a semi-professional photographer so I want something decent for when I see something cool and don’t have my big camera with me). I gave the SE to my mom and she used it for another two years until she decided she needed a bigger screen. It probably still works and it got its last OS update just two months ago.
As long as you don’t run something super hardware hungry, you can easily use an iPhone for at least five years without any problems. Even if the battery dies halfway through, there are dozens of repair shops around that will replace it for a reasonable price in case you’re not comfortable with opening up the phone on your own.
Software optimization is mostly not a language-level problem. I’ll be dailying my 3-year-old OnePlus 9 Pro until it starts missing out on security updates, but it will probably still be “usable” long after that. Support/updates aside, my 6-year-old galaxy s9 can still run most normal apps. Hell, I got the most recent lineageOS running on a pixel 2 XL from the year before that and it straight up felt fast as long as I wasn’t playing some super intensive game or something. This isn’t an android vs. iOS problem, it’s a “developers of [insert flashy new app here] either not bothering to put effort in to optimize their code or being forced to push out a minimum viable product ASAP” problem.
Edit: fixed my hyphen use
Android has actually employed a hybrid JIT/AOT compilation model for a long time.
The application bytecode is only interpreted on first run and afterwards if there’s no cached JIT compilation for it. The runtime AOT compiles well-known methods and then profiles the application to identify targets for asynchronous JIT compilation when the device is idle and charging (so no excess battery drain): https://source.android.com/docs/core/runtime/configure#how_art_works
Compiling on the device allows the use of profile-guided optimizations (PGO), as well as the use of any non-baseline CPU features the device has, like instruction set extensions or later revisions (e.g. ARMv8.5-A vs ARMv8).
If apps had to be distributed entirely as compiled object code, you’d either have to pre-compile artifacts for every different architecture and revision you plan to support, or choose a baseline to compile against and then use feature detection at runtime, which adds branches to potentially hot code paths.
It would also require the developer to manually gather profiling data if they wanted to utilize PGO, which may limit them to just the devices they have on-hand, or paying through the nose for a cloud testing service like that offered by Firebase.
This is not to mention the massive improvement to the developer experience from not having to wait several minutes for your app to compile to test out each change. Call it laziness all you want, but it’s risky to launch a platform when no one wants to develop apps for it.
Any experienced Android dev will tell you it does kinda suck anyways, but it’d suck way worse if it was all C++ instead. I’d take Android development over iOS development any day of the week though. XCode is one of the worst software products ever conceived, and you’re forced to use it to build anything for iOS.
I know about all this — I actually began implementing my own JVM language a few days ago. I know Android uses Dalvik btw. But I guess a lot of people can use this info; infodump is always good. I do that.
btw I actually have messed around with libgcc-jit and I think at least on x86, it makes zero difference. I once did a test:
– Find /e/ with MAWK -> 0.9s – Find /e/ with JAWK -> 50s.
No shit! It’s seriously slow.
Now compare this with go-awk: 19s.
Go has reference counting and heap etc, basically a ‘compiled VM’. I think if you want fast code, ditch runtime.
Actually, Android doesn’t really use Dalvik anymore. They still use the bytecode format, but built a new runtime. The architecture of that runtime is detailed on the page I linked. IIRC, Dalvik didn’t cache JIT compilation results and had to redo it every time the application was run.
FWIW, I’ve heard libgcc-jit doesn’t generate particularly high quality code. If the AOT compiled code was compiled with aggressive optimizations and a specific CPU in mind, of course it’ll be faster. JIT compiled code can meet or exceed native performance, but it depends on a lot of variables.
As for mawk vs JAWK vs go-awk, a JIT is not going to fix bad code. If it were a true apples to apples comparison, I’d expect a difference of maybe 30-50%, not ~2 orders of magnitude. A performance gap that wide suggests fundamental differences between the different implementations, maybe bad cache locality or inefficient use of syscalls in the latter two.
On top of that, you’re not really comparing the languages or runtimes so much as their regular expression engines. Java’s isn’t particularly fast, and neither is Go’s. Compare that to Javascript and Perl, both languages with heavyweight runtimes, but which perform extraordinarily well on this benchmark thanks to their heavily optimized regex engines.
It looks like mawk uses its own bespoke regex engine, which is honestly quite impressive in that it performs that well. However, it only supports POSIX regular expressions, and doesn’t even implement braces, at least in the latest release listed on the site: https://github.com/ThomasDickey/mawk-20140914
(The author creates a new Github repo to mirror each release, which shows just how much they refuse to learn to use Git. That’s a respectable level of contempt right there.)
Meanwhile, Java’s regex engine is a lot more complex with more features, such as lookahead/behind and backreferences, but that complexity comes at a cost. Similarly, if go-awk is using Go’s
regexppackage, it’s using a much more complex regex engine than is strictly necessary. And Golang admits in their own FAQ that it’s not nearly as optimized as other engines like PCRE.Thus, it’s really not an apples to apples comparison. I suspect that’s where most of the performance difference arises.
This statement is completely wrong. Like, to a baffling degree. It kinda makes me wonder if you’re trolling.
Go doesn’t use any kind of VM, and has never used reference counting for memory management as far as I can tell. It compiles directly to native machine code which is executed directly by the processor, but the binary comes with a runtime baked in. This runtime includes a tracing garbage collector and manages the execution of goroutines and related things like non-blocking sockets.
Additionally, heap management is a core function of any program compiled for a modern operating system. Programs written in C and C++ use heap allocations constantly unless they’re specifically written to avoid them. And depending on what you’re doing and what you need, a C or C++ program could end up with a more heavyweight collective of runtime dependencies than the JVM itself.
At the end of the day, trying to write the fastest code possible isn’t usually the most productive approach. When you have a job to do, you’re going to welcome any tool that makes that job easier.
No I just struggle at getting my meaning across + these stuff are new to me. What I meant was ‘Go does memory management LIKE a VM does’. Like ‘baking in the GC’. Does that make sense? Or am I still wrong?
Issue is incentive. Developers use what they are told by more senior developers and most rewrites and tech debt work is deemed unprofitable and dropped.
They use shit like electron to write things once. Its always the worst experience but it seems to management on paper to be a huge win.