"Remarkably, we had only put very basic thought into optimization as the Rust version was written. Even with just basic optimization, Rust was able to outperform the hyper hand-tuned Go version."
Note: that parenthetical is a very big caveat, because properly profile-optimized JVM executables can often achieve exceptional performance/development cost tradeoffs.
In addition however, ARC admits a substantial amount of memory-usage optimization given bytecode, which is now what developers provide to Apple on iOS. Not to mention potential optimizations by allowing Apple to serve last-minute compiled microarchitecture optimized binaries for each device (family).
To satiate the pedants... ARC is more of less GC where calls into the GC mechanism are compiled in statically and where there are at worst deterministic bounds on potential "stop the world" conditions.
While this may not be presently optimal because profile-guided approaches can deliver better performance by tuning allocation pool and collection time parameters, it's arguably a more consistent and statically analyzable approach that with improvement in compilers may yield better overall performance. It also provides tight bounds on "stop the world" situations, which also exist far less frequently on mobile platforms than in long running sever applications.
Beyond those theoretical bounds, it's certainly much easier to handle when you have an OS that is loading and unloading applications according to some policy. This is extremely relevant as most sensible apps are not actually long running.
I doubt that. It implies huge costs without giving any benefits of GC.
A typical GC have compaction, nearly stack-like fast allocation [1], ability to allocate a bunch of objects at once (just bump the heap pointer once for a whole bunch).
And both Perl and Swift do indeed perform abysmally, usually worse than both GC and manual languages [2].
> ARC is more of less GC
Well, no. A typical contemporary GC is generational, often concurrent, allowing fast allocation. ARC is just a primitive allocator with ref/deref attached.
[1] http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.49....
What? This doesn't make any sense. From the cache's POV stack and bump-allocated heap are the same thing. Both are continuous chunks of memory where the next value is being allocated right after the previous one.
The only difference between the stack and the bump-allocated heap is that the former has hardware support for pointer bumping and the latter has not. That's all.
The stack pointer moves both directions and the total range of that back-and-forth movement is typically in kilobytes, so it may fit fully in L1.
Just check with perf what happens when you iterate over an array of 100 MB several times and compare that to iterating over 10 kB several times. Both are contiguous but the performance difference is pretty dramatic.
Besides that, there is also an effect that the faster you allocate, the faster you run out of new gen space, and the faster you trigger minor collections. These are not free. The faster you do minor collections, the more likely it is for the objects to survive. And the cost is proportional to survival rate. That's why many Java apps tend to use pretty big new generation size, hoping that before collection happens, most of young objects die.
This is not just theory - I saw this just too many times, when reducing allocation rate to nearly zero caused significant speedups - by order of magnitude of more. Reducing memory traffic is also essential to get good multicore scaling. It doesn't matter each core has a separate tlab, when their total allocation rate is so high that they are saturating LLC - main memory link. It is easy to miss this problem by classic method profiling, because a program with such problem will manifest by just everything being magically slow, but no obvious bottleneck.
Yes, you are right about stack locality. It indeed moves back and forward making effective used memory region quite small.
> These are not free. The faster you do minor collections, the more likely it is for the objects to survive. And the cost is proportional to survival rate.
Yes, that's true. Immutable languages are doing way better here having small minor heaps (OCaml has 2MB on amd64) and very small survival rates (with many object being directly allocated on older heap if they are known to be lasting in advance).
Now I understand your point better and I agree.
This Apple marketing meme needs to die. Reference counting incurs arguably more cost than GC, or at least the cost is spread through-out processing.
[1]: <https://media.ccc.de/v/35c3-9670-safe_and_secure_drivers_in_...
I agree with you that most people tend to associate GC with something more advanced nowadays, like mark and sweep as you said in another comment, but it seems pointless to argue that ARC is not a form of GC.
Refcounting in the presence of threads is usually non-deterministic too.
Most people think of Python as GCed language, and it uses mostly RC.
Any runtime that uses mark & sweep today may elect to use RC for some subset of the heap at some point in a future design, if that makes more sense. The mix of marking GC vs refcounting GC shouldn't affect the semantics of the program.
If one goes with reference counting as GC implementation, then one should take the effort to use hazardous pointers and related optimizations.
Java is very fast and 3X slower is a pretty wild claim.
Here are some benchmarks; I'll leave to the experts out there to confirm or dismiss them.
https://benchmarksgame-team.pages.debian.net/benchmarksgame/...
There are only a few applications for which it isn’t acceptable.
Just look at all the massive apps built on Electron. People wouldn’t do that if it wasn’t effective.
I used to think that but now I’m not so sure. :(
— memory usage is less different for tiny programs that do allocate memory: reverse-complement, k-nucleotide, binary-trees, mandelbrot, regex-redux
I mean, those 1GB RAM 7 years old slow as molasses phones getting dust into drawers or being littered into landfills would scream if they didn't have to run everything behind a VM.
Google's spyware is becoming more invasive and thus more memory-hungry.
1: https://developer.android.com/reference/android/app/usage/Us...
2: https://developer.android.com/reference/android/service/auto...
And the "hardness" of writing lockless structures is strongly offset by libraries, so unless you're doing something very exotic, it is rarely a real problem.
If anything the gap is increasing not shrinking. JVM is terrible at memory access patterns due to the design of the language, and designing for memory is increasingly critical for maximum performance on modern systems. All the clever JIT'ing in the world can't save you from the constant pointer chasing, poor cache locality, and poor prefetching.
The gap won't shrink until Java has value types. Which is on the roadmap, yes, but still doesn't exist just yet.
The problem with those benchmarks is if you look at the Java code you'll see it's highly non-idiomatic. Almost no classes or allocations. They almost all exclusively use primitives and raw arrays. Even then it still doesn't match the performance on average of the C (or similar) versions, but if you add the real-world structure you'd find in any substantial project that performance drops off.
I don't know what you mean by "copy-only types." I'm not finding any reference to that terminology in the context of language design.
Sorry about the confusion. I meant for the quotes around "copy-only" to indicate that it wasn't really a standard term, but I marked "value types" the same way, so that didn't really work. By "copy-only" I meant something you couldn't have more than one reference to: every name (variable) to which you assign the data would have its own independent copy.
That's not really a requirement of value types, no. C# has value types (structs) and you can have references to them as well (ref & out params).
In general though yes it would be typically copied around, same as an 'int' is. Particularly if Java doesn't add something equivalent to ref/out params.
Plenty of `inner static classes`. Where's the up-to-date comparison showing that `static` makes a performance difference for those tiny programs?
Plenty of TreeNode allocations.
----
> The problem with …
The problem with saying "in any substantial project that performance drops off" is when we don't show any evidence.
https://benchmarksgame-team.pages.debian.net/benchmarksgame/...
On the real world, you won't get things as optimized in higher level languages, because optimized code looks completely unidiomatic. A 3x speedup from Java is a pretty normal claim.
It's Haskell-like features only help on large and complex programs.
They also talked about it pretty frequently.
Maybe ggp's claim that D just needs a heavy hitter backing it might be misplaced.
Right now, I think eBay is one of the big companies that uses D.
Edit: Sibling comment beat me to it.
Ofc, Apple is not MS, so Swift developer experience and docs kind of suck (if you're not in the "iOS bubble" where it's nice and friendly), and multiplatform dev experience especially sucks even worse...
And "easier to pick up and code in" might not necessarily be an advantage for a systems language - better start slowly putting the puzzle together in your head, and start banging up code others need to see only after you've internalized the language and its advanced features and when (not) to use them! It helps everyone in the long run. This is one reason why I'd bet on Rust!
Well, for fairness, D is quite a bit older than Swift. (It's nearly as much older than Swift as it is younger than C++!) But what do you think pushes Swift out of the "C++ with hindsight" basket?
So I doubt they would sponsor D.
I don't think MS has any interest in improving C++ (look at their compiler). But that's not because of competing activities.
Visual C++ is the best commercial implementation of C++ compilers, better not be caught using xlc, aCC, TI, IAR, icc and plenty of other commercial offerings.
If C++ has span, span_view, modules, co-routines, core guidelines, lifetime profile static analyser, is it in large part to work started and heavily contributed by Microsoft on ISO, and their collaboration with Google.
As for competing with C++, it is quite clear, specially when comparing the actual software development landscape with the 90's, that C++ has lost the app development war.
Nowadays across all major consumer OSes it has been relegated to the drivers and low level OS services layer like visual compositor, graphics engine, GPGPU binding libraries.
Ironically, from those mainstream OSes, Microsoft is the only one that still cares to provide two UI frameworks directly callable from C++.
Which most Windows devs end up ignoring in favour of the .NET bindings, as Kenny Kerr mentions in one of his talks/blog posts.
Back to the D issue, Azure IoT makes use of C# and Rust, and there is Verona at MSR as well, so as much I would like to see them spend some effort on D, I don't see it happening.
Rust have by far the better ecosystem compared to those 3
My refute it simply: Rusts web development story isn’t out of the box clean like Crystal Lang’s which ships with an HTML language out of the box. So it could be categorized as a poor choice in comparison to Crystal
Did you mean HTTP server? If so, there are at least 3 good ones in Rust that are only a `cargo add` away. If you've already taken the trouble to set up a toolchain for a new language, surely a single line isn't asking too much.
— Maintenance Complexity "keep in mind that this is a subjective metric based on the author's experience!"
Would it be fair to accept an optimization on a language and refuse it on another because it is not idiomatic ?
Help make one:
— contribute programs that you consider to be "idiomatic"
https://salsa.debian.org/benchmarksgame-team/benchmarksgame/...
— use those measurement and web scripts to publish your own "common use cases" "idiomatic code" benchmarks game
----
>> compare language speed for common use cases vs trying to squeeze every last piece of performance
— please be clear about why you wish to compare the speed of programs written as if speed did not matter
----
??? What do you think is not "idiomatic" about programs such as:
https://benchmarksgame-team.pages.debian.net/benchmarksgame/...
Most code will be considerably slower due to a lot of factors.
Java in particular is a very pointer-heavy language, made up of pointers to pointers to pointers everywhere, which is really bad for our modern systems that often are much more memory latency than CPU constrained.
A factor of 2-4x to languages like C++ or Rust for most code seems plausible (and even low) unless the limiting factor is external, like network or file system IO.
It's true that pointer chasing really hurts in some sorts of program and benchmark. For sure. No argument. That's why Project Valhalla exists.
But it's also my view that modern C++ programming gets away with a lot of slow behaviours that people don't really investigate or talk about because they're smeared over the program and thus don't show up in profilers, whereas actually the JVM fixes them everywhere.
C++ programs tend to rely much more heavily on copying large structures around than pointer-heavy programs. This isn't always or even mostly because "value types are fast". It's usually because C++ doesn't have good memory management so resource management and memory layout gets conflated, e.g. std::vector<BigObject>. You can't measure this because the overheads are spread out over the entire program and inlined everywhere, so don't really show up in profiling. For the same reasons C++ programs rely heavily on over-specialised generics where the specialisation isn't actually a perf win but rather a side effect of the desire for automatic resource management, which leads to notorious problems with code bloat and (especially) compile time bloat.
Another source of normally obscured C++ performance issues is the heap. We know malloc is very slow because people so frequently roll their own allocators that the STL supports this behaviour out of the box. But malloc/new is also completely endemic all over C++ codebases. Custom allocators are rare and restricted to very hot paths in very well optimised programs. On the JVM allocation is always so fast it's nearly free, and if you're not actually saturating every core on the machine 100% of the time, allocation effectively is free because all the work is pushed to the spare cores doing GC.
Yet another source of problems is cases where the C++ programmer doesn't or can't actually ensure all data is laid out in memory together because the needed layouts are dynamically changing. In this case a moving GC like in the JVM can yield big cache hit rate wins because the GC will move objects that refer to each other together, even if they were allocated far apart in time. This effect is measurable in modern JVMs where the GC can be disabled:
https://shipilev.net/jvm/anatomy-quarks/11-moving-gc-localit...
And finally some styles of C++ program involve a lot of virtual methods that aren't always used, because e.g. there is a base class that has multiple implementations but in any given run of the program only one base class is used (unit tests vs prod, selected by command line flag etc). JVM can devirtualise these calls and make them free, but C++ compilers usually don't.
On the other hand all these things can be obscured by the fact that C++ these days tends only to be used in codebases where performance is considered important, so C++ devs write performance tuned code by default (or what they think is tuned at least). Whereas higher level languages get used for every kind of program, including the common kind where performance isn't that big of a deal.
Without global knowledge of memory lifetimes, maintainers make local decisions to copy rather than share.
Allocation in a C++ program is going to be about the same speed as in a Java program. Modern mallocs are doing basically the same thing on the hot-path: bumping the index on a local slab allocator.
But now I sound like a Geico (insurance) commercial. Sorry about that.
Tight, low level code in Java and Go is roughly as fast as average C code. The Go compiler is know to be less good at optimizing code than e.g. GCC, but this in many cases creates little practical difference, while the Java JIT compilers have become excellent to a point where they often beat GCC, especially as they can use run time profiling for code optimization. So they can optimize the code for the actual task at hand.
Where the languages differ in "speed" is their runtime environment. Java and Go are languages with garbage collection, which of course means that some amount of CPU is required to perform GC. But as the modern garbage collectors run in parallel with the program, this CPU effort often enough is no bottleneck. On the other side, manual memory management has different performance trade-offs, which in many cases can make it quite slow on its own.