Checker Framework VS compiler-explorer

I’m not familiar enough with kotlin to comment fully but from your description the checker framework [0] appears to do the same thing in Java.

I confess I’m not fond of checker framework. I find the error messages can be obtuse but it is very effective.

0 - https://checkerframework.org/

It is interesting! I experimented with creating a bad borrow checker for Java using annotations from

https://checkerframework.org/

It supports some level of substructural types using must-call annotations,

https://checkerframework.org/manual/#resource-leak-checker

Lombok is not a compiler extension. Compiler extensions, aka annotation processors, are offered only specific capabilities that ensure that they preserve the Java language specification. Particularly, code that compiles successfully with an extension also compiles without it (perhaps requiring other classes to be available) and it compiles down to the same bytecode. Annotation processors are used to implement pluggable type systems (e.g. https://checkerframework.org) or to generate other classes (e.g. https://immutables.github.io/).

Unlike compiler extensions, Lombok compiles source files that do not conform to the Java language specification. Lombok is an alternative Java Platform language, like Clojure or Kotlin or Scala, except that it's a superset of the Java language. However, rather than forking `javac` source code and modifying it to compile Lombok source files, the Lombok compiler modifies `javac`'s operation by hacking into its internals and modifying them as it runs to compile Lombok sources rather than Java sources.

Having alternative Java Platform languages is perfectly fine. The problem with Lombok is that it doesn't present itself as such but as a library or a compiler extension even though it violates the Java language specification in ways that compiler extensions are forbidden from doing.

And then I found (thanks Oracle), https://checkerframework.org/ zomg, this thing is awesome. Pluggable Type Systems!

Java should adopt something like the Checker Framework Nullness Checker in its first-party tooling.

https://github.com/typetools/checker-framework

And modern (real, non-android) Java via project amber and so on has gone more and more quasi functional with its immutability and sealed and record types for effective sum types, as well as its pretty cool type-use annotation extensible static type checks.

Checkerframework - a really academic take, and as one might expect from such a thing, backed by tons of papers and analysed to perfection. Specifically, this is the only framework I'm aware of that realizes nullity is a little more complicated than just a boolean yes-or-no; just like generics actually have 4 flavours for any given type: List, List, List, and List are all 4 important and unique, and nullity is no different. Specifically, it can occur that you want to write a method that ought to accept both lists of nullable strings as well as list of nonnull strings, and needs to 'convey' this nullity again on its output. You can either attempt to lift along the existing generics system in java which I think is your intent, but it's not actually all that easy to do this. After all, T extends @Nullable Number super @NonNull Number, or whatnot, isn't legal java. So you.. really just can't do that. Checker Framework solves this problem by introducing the @PolyNull annotation, which still isn't perfect but covers almost all real world use cases you can think of. I'm missing any acknowledgement in your documentation. An oversight, or, something you hadn't thought of yet? You're in good company: Both eclipse and intellij's engineers, when I asked them about it, just hadn't realized it was a thing. Point is: If you think the primary problem with e.g. eclipse's and intellij's take is that they lack academic rigour - checkerframework has you beat.

NullAway or the Checker Framework should greatly help eliminate the issue. Also, when Java gets value types you should be able to define your own non nullable value types and use them safely.

At least on android arm64, looks like a `dmb ishst` is emitted after the constructor, which allows future loads to not need an explicit barrier. Removing `final` from the field causes that barrier to not be emitted.

https://godbolt.org/#g:!((g:!((g:!((h:codeEditor,i:(filename...

You said You won't get "extreme performance" from C++ because it is buried under the weight of decades of compatibility hacks.

Now your whole comment is about vector behavior. You haven't talked about what 'decades of compatibility hacks' are holding back performance. Whatever behavior you want from a vector is not a language limitation.

You could write your own vector and be done with it, although I'm still not sure what you mean, since once you reserve capacity a vector still doubles capacity when you overrun it. The reason this is never a performance obstacle is that if you're going to use more memory anyway, you reserve more up front. This is what any normal programmer does and they move on.

Show what you mean here:

https://godbolt.org/

I've never used ISPC. It's somewhat interesting although since it's Intel focused of course it's not actually portable.

I guess now the goal posts are shifting. First it was that "C++ as a language has performance limitations" now it's "rust has a vector that has a function I want and also I want SIMD stuff that doesn't exist. It does exist? not like that!"

Try to stay on track. You said there were "decades of compatibility hacks" holding back C++ performance then you went down a rabbit hole that has nothing to do with supporting that.

C++ Insights is available online at https://cppinsights.io/

It is also available at a touch of a button within the most excellent https://godbolt.org/

along side the button that takes your code sample to https://quick-bench.com/

Those sites and https://cppreference.com/ are what I'm using constantly while coding.

I recently discovered https://whitebox.systems/ It's a local app with a $69 one-time charge. And, it only really works with "C With Classes" style functions. But, it looks promising as another productivity boost.

[P&H RISC] https://www.google.com/books/edition/_/e8DvDwAAQBAJ

Compiler Explorer by Matt Godbolt [Godbolt] can help better understand what code a compiler generates under different circumstances.

[Godbolt] https://godbolt.org

The official CPU architecture manuals from CPU vendors are surprisingly readable and information-rich. I only read the fragments that I need or that I am interested in and move on. Here is the Intel’s one [Intel]. I use the Combined Volume Set, which is a huge PDF comprising all the ten volumes. It is easier to search in when it’s all in one file. I can open several copies on different pages to make navigation easier.

Intel also has a whole optimization reference manual [Intel] (scroll down, it’s all on the same page). The manual helps understand what exactly the CPU is doing.

[Intel] https://www.intel.com/content/www/us/en/developer/articles/t...

Personally, I believe in automated benchmarks that measure end-to-end what is actually important and notify you when a change impacts performance for the worse.

Let's compile it with https://godbolt.org/, turn on some optimisations and inspect the IR (-O2 -emit-llvm). Copying out the part that corresponds to the while loop:

  4:

resources were extra useful when building deeper intuitions about GPU performance for ML models at work and in graduate school.

- CMU's "Deep Learning Systems" Course is hosted online and has YouTube lectures online. While not generally relevant to software performance, it is especially useful for engineers interested in building strong fundamentals that will serve them well when taking ML models into production environments: https://dlsyscourse.org/

- Compiler Explorer is a tool that allows you easily input some code in and check how the assembly output maps to the source. I think this is exceptionally useful for beginner/intermediate programmers who are familiar with one compiled high-level language and have not been exposed to reading lots of assembly. It is also great for testing how different compiler flags affect assembly output. Many people used to coding in C and C++ probably know about this, but I still run into people who haven't so I share it whenever performance comes up: https://godbolt.org/

To really understand what's going on here we can look at the compiled assembly code. I'm working on a Mac and can do this using the objdump tool. Compiler Explorer is also a handy tool but doesn't seem to support Arm assembly which is what Rust will use when compiling on Apple Silicon.

Play around with it in godbolt if you're really curious: https://godbolt.org/