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Comment by hedora | Hacker News Reader

In rust, I usually just make sure stuff is not Box<>, and try to reuse buffers. That generally gets the memory allocator completely out of the way (except for async).

The remaining allocator performance problems are mostly due to it zeroing allocated memory unless I use unsafe. Is java able to stackify most new Object calls and elide default initialization of object members these days?

I’m surprised to hear there is no demand for compiler enforced/facilitated thread safety in Java. That was a major pain point in all the Java code bases I’ve worked with in the past, and is a headline safety feature for rust (which goes even further and enforces aliasing rules) and JS. Could you be seeing selection bias in your user base?

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> Is java able to stackify most new Object calls and elide default initialization of object members these days?

Escape analysis in OpenJDK will stack allocate values where it can show it is safe to do so. Project Valhalla is also reducing the memory footprint of objects.

As for thread safety, that is more of a language concern than a runtime one. Amongst JVM languages Scala is leading here AFAIK. Its "capture checking"[1] provides thread safety (e.g. [2]) and actually covers escape analysis as well. On Scala Native (the native code backend for Scala) capture checking can be used for safe stack allocation and safe arena allocation.

[1]: https://docs.scala-lang.org/scala3/reference/experimental/cc... [2]: https://softwaremill.com/understanding-capture-checking-in-s...

> In rust, I usually just make sure stuff is not Box<>, and try to reuse buffers. That generally gets the memory allocator completely out of the way (except for async).

You say "just", but this is easy when programs are small. The problem is that this gets harder and harder and harder as programs grow large (the whole point of the JVM's design was to address the performance issues that plague large C++ programs). E.g. someone who works at one of the world's largest tech companies just told me that they have problems with Rust programs spending 30% of their CPU on memory management even when they're as small as a couple hundreds of thousands of LOC.

> Is java able to stackify most new Object calls and elide default initialization of object members these days?

No, the general idea is to just make memory management efficient (although some objects are "stackified" and the compiler will elide zeroing when non-defaults are passed to a constructor). Now, I say "just", but this used to come at the cost of GC pauses and larger footprint. Now it only comes at the cost of a larger footprint.

But there is a definite choice here when it comes to performance. Low level languages give you control that means performance is attained through manual effort. Java takes away control to improve effort-per-performance. Roughly speaking, these tradeoffs mean that when programs are small and the extra effort is manageable, low-level languages are hard to beat, but when programs are large, it is Java that is hard to beat.

> I’m surprised to hear there is no demand for compiler enforced/facilitated thread safety in Java. That was a major pain point in all the Java code bases I’ve worked with in the past, and is a headline safety feature for rust (which goes even further and enforces aliasing rules) and JS.

This used to be a bigger problem when locks were the main mechanism for sharing data among threads. Now, with the wide selection of concurrent data structures, such problems don't occur as much. I'm not saying they don't occur at all, just not frequently enough to become a major priority.

Also, safe Rust's data-race freedom comes at the cost of requiring unsafe for benign races, which are not uncommon in concurrent algorithms (i.e. it excludes even "good" races). This may be fine in languages whose view on performance is "with enough effort you can get good performance", but, as I said, Java is about making more "naive" programs fast with little effort.

It is fair that there are many ways to be slow in any number of programming languages. I'm surprised to hear "Rust programs spending 30% of their CPU on memory management even when they're as small as a couple hundreds of thousands of LOC", although I can visualize some unique workloads where that's unavoidable irrespective of language & runtime.

You say unavoidable, but moving collectors are designed to reduce CPU at high allocation rates by increasing the heap size. Generational moving collectors have a pathological case - a high allocation rate of long-lived objects - but that's quite hard to get yourself into by accident. Their main downside (besides the inherent increased footprint) used to be unpredictable long pauses, which could have a very high impact on tail latencies, and that's just gone with ZGC.

Generational moving collectors are a very powerful memory management optimisation, but because they necessarily require some "interesting" FFI layer between the ordinary heap and any passing of pointers between the program and the hardware/OS - the very thing low-level languages are designed not to have - this is a powerful, general optimisation (not perfect, but extremely useful in a wide class of programs) that is not available to low-level programming languages. And it's not the only one, BTW. JIT compilers are also designed for "global" average-case optimisations at the cost of precise low-level control over the worst case, which is another thing that low-level languages trade away.

In the most simplistic way, I would say that the precise control that low-level languages are all about helps their performance when programs are small (and can be manually optimised globally) and hurts their performance as programs get large. They have to give up on some optimisations that come at the cost of ceding low-level control, and that includes moving collectors.

When I say "unavoidable" I mean exactly things like your "a high allocation rate of long-lived objects (as one example), where the work that has to be done requires a significant number of CPU and memory cycles spent on memory management (manual or automatic).

When I say tradeoffs", I mean exactly things like "inherent increased footprint", or my earlier "wasting RAM" point.

At this point, I'm not sure that we're disagreeing about these details, but rather what we make of them... I view them as just years of continued tuning of tradeoffs (heap size vs CPU cycles vs memory cycles), while you them as major breakthrough that makes garbage collection much more desirable. Is that fair?

A high allocation rate of long-lived objects is not easy to do. Object "death" rate has to equal the allocation rate, so a high allocation rate of long-lived objects means that somehow you get to allocate, say, 1 GB/s of data that is kept for a long time and is discarded at a rate of 1 GB/s.

> When I say tradeoffs", I mean exactly things like "inherent increased footprint", or my earlier "wasting RAM" point.

Well, that is a real tradeoff, but there's a reason why it's a very attractive one for a huge class of applications. There are two ways of looking at this, which amount to the same thing:

1. Because both RAM and CPU are needed for computation, what matters isn't each of their utilisation values separately but only the more constraining or impactful of the two.

2. Because CPU is needed to use RAM, every CPU cycle you spend effectively takes away some other program's ability to use RAM.

This means that for any amount of CPU utilisation, there is some amount of RAM that is effectively free (i.e. has no additional impact), and the more CPU a program consumes, the more RAM it can consume without it making additional impact. It's easiest to see in the extreme case of a program using 100% CPU: no other program can make progress, and so it doesn't matter how much of the available RAM your program is using - it effectively captures all of it whether it uses it or not. But this scales to any amount of CPU utilisation (not quite linearly). What wastes RAM is not using that "free RAM" to reduce the dominant resource, CPU. And what further determines the RAM/CPU "exchange rate" is the RAM/CPU ratio offered by the hardware, which is more RAM-heavy than some appreciate (it is very hard to find a metal or virtual deployment with less than 1 GB of RAM per core these days - taking into account partial cores in virtual machines - outside of embedded devices).

This means that if you have a memory management algorithm that uses more RAM to help reduce CPU as CPU utilisation rises, that's usually a good thing. And moving collectors work exactly like that. The heap overhead in a generational moving collector is only a function of the allocation rate, and a high allocation rate also means high CPU usage.

My colleague, the main developer of ZGC these days, gave a keynote about this very subject at ISMM: https://youtu.be/mLNFVNXbw7I

> I view them as just years of continued tuning of tradeoffs (heap size vs CPU cycles vs memory cycles), while you them as major breakthrough that makes garbage collection much more desirable. Is that fair?

I say that for many years, the main practical, most "felt" tradeoff of moving collectors has been their STW pauses. With pauses eliminated, there is a qualitative change in the attractiveness of moving collectors, making them more appropriate than other memory management techniques for an even broader class of applications than before. Previously, applications that were very sensitive to tail latencies didn't want moving collectors; now, the tail latency is no longer an issue (unless your application's tail latency tolerance is such that a realtime OS is needed). In other words, I'm saying that moving collectors' most impactful tradeoff is now gone.

> A high allocation rate of long-lived objects is not easy to do.

This is exactly what async programs do on the hot path. Consider a 1M request per second process holding 64K of buffers per request. That’s 64GB of allocations per second. Now, assume the requests hit a remote database with 10ms latency. That’s 640MB of live heap in steady state, which ends up in the “long lived” part of most garbage collectors.

Using RAM to save CPU is exactly the wrong tradeoff when such a system becomes CPU bound.

It’s almost always the case that it is CPU bound due to an incoming request spike or elevated retry rates on the backend. Those tend to pile up, creating a 64GB/sec leak.

The alternative is that the system is CPU bound because the heap is large. This is also very common. Unless each collection takes less work as the heap increases in size, backing off the GC rate to free CPU instantly drives the system into metastable failure, where the GC becomes more expensive because the GC is expensive.

Instead of reasoning about this all the time, it’s much easier (for me, granted, I am not a typical java developer) to just jam the CPU intensive work on a low priority event queue so that it uses 100% CPU but never blocks low latency stuff, or things about to retire requests. (Or, stick it in a dedicated but small thread pool if I can’t touch the async event loops).

This ends up being easier to deal with than java, since everything is thread safe, allocations are predictable, and there are CPU escape hatches I can use.

C++ lets me use smart pointers that have exactly the semantics I want, and that are memory safe but racy in practice. Rust makes them actually memory and thread safe, but sometimes adds useless copies, initializations and thread synchronization (or requires unsafe).

> That’s 640MB of live heap in steady state, which ends up in the “long lived” part of most garbage collectors.

It won't, because that is exactly the thing good moving GCs detect and size the young-gen accordingly.

> Using RAM to save CPU is exactly the wrong tradeoff when such a system becomes CPU bound.

Did you mean to write something else, because it's pretty obvious that it's the right tradeoff? If something is CPU-bound, you want to reduce the CPU usage.

> Unless each collection takes less work as the heap increases in size, backing off the GC rate to free CPU instantly drives the system into metastable failure, where the GC becomes more expensive because the GC is expensive.

The whole point of moving collectors is that each collection takes the same amount of work, but you need to do it less frequently as the heap rises. So yes, as they heap grows, moving GCs are supposed to work less. The heap grows as a function of the allocation rate while the CPU devoted to memory management remains the same. That's precisely the optimisation that moving collectors bring.

> Instead of reasoning about this all the time

The whole point is that the GC is what "reasons" about this for you.

> it’s much easier (for me, granted, I am not a typical java developer) to just jam the CPU intensive work on a low priority event queue so that it uses 100% CPU but never blocks low latency stuff, or things about to retire requests.

That's orthogonal. You can do that at least as easily in Java.

> This ends up being easier to deal with than java, since everything is thread safe, allocations are predictable, and there are CPU escape hatches I can use.

Thread safety is orthogonal, and now with ZGC, memory management in Java is more predictable than malloc/free allocators.

> C++ lets me use smart pointers that have exactly the semantics I want, and that are memory safe but racy in practice. Rust makes them actually memory and thread safe, but sometimes adds useless copies, initializations and thread synchronization (or requires unsafe).

Yes, and it's also less efficient and less predictable in the memory management work as programs grow larger.

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