Perhaps they never really needed to fit 32GB into their intel macs either. Some days after the glowing reviews; and strange comments about magic memory utilization; we now see comments concerned about SSD wear due to swap file usage.
If the applications and data structures are more compact in memory on the arm processors; it should be easy to test; you just need an intel mac; and an M1 mac running the same app on the same document and look at how much memory it uses.
So on customer PC ram is maybe more used as caching mechanisms or eaten away by poorly designed memory leak/garbage collection.
And if your GPU is able do to real time rendering on data heavy load maybe you need less caching of intermediate results as well.
2.My current production server is a PostgreSQL database on a 16GB RAM VM running on Debian (my boss is stingy). This doesn't prevent me from managing a 300GB+ data cluster with pretty decent performances and perform actual data analysis.
3.If Chrome sometimes use +8GB for a godsake webrowser the only explanation is poor design, there is no excuse.
It's possible that apps have been completely overhauled for a baseline M1 experience. Extremely, extraordinarily unlikely that anything remotely of the sort has happened, though. And since M1-equipped Macs don't have any faster IO than what they replaced (disk, network, and RAM speeds are all more or less the same), there wouldn't be any reason for apps to have done anything substantially difference.
Third, Marcel Weiher explains Apple’s obsession about keeping memory consumption under control from his time at Apple as well as the benefits of reference counting:
>where Apple might have been “focused” on performance for the last 15 years or so, they have been completely anal about memory consumption. When I was there, we were fixing 32 byte memory leaks. Leaks that happened once. So not an ongoing consumption of 32 bytes again and again, but a one-time leak of 32 bytes.
>The benefit of sticking to RC is much-reduced memory consumption. It turns out that for a tracing GC to achieve performance comparable with manual allocation, it needs several times the memory (different studies find different overheads, but at least 4x is a conservative lower bound). While I haven’t seen a study comparing RC, my personal experience is that the overhead is much lower, much more predictable, and can usually be driven down with little additional effort if needed.
ARC is not specific to M1, BUT have been widely used in ObjC & Swift for years AND is thus heavily optimized on M1 that perform "retain and release" way faster (even when emulating x86)
Perfect illustration of Apple software+hardware long term strategy.
>This quote doesn’t really cover why M1 macs are more efficient with less ram than intel macs? You’ve got a memory budget, it’s likely broadly the same on both platforms
But both Intel Macs and ARM Macs use RC. Both chips are running the same software.
Browsers are written in C++ and javascript has full-blown GC.
I don't see how refcounting gives you advantage over manual memory management for most users.
I am going to speculate now, but maybe, just maybe, if some of the silicon that apple has used on the M1 is used for compression/decompression they could be transparently compressing all ram in hardware. Since this offloaded from the CPUs and allows a compressed stream of data from memory, they achieve greater ram bandwidth, less latency and less usage for a given amount of memory. If this is the case I hope that the memory has ECC and/or the compression has parity checking....
Are you aware of any x86 chips that utilize this method?
Cheers
Making it cheaper to create and destroy objects with hardware acceleration, and to do many small, low-cost reclaims without eating all your CPU would be a magical improvement to the JVM, because you could constrain memory use without blowing out CPU. From what's described in TFA it sounds like the same is true for modern MacOS programming.
The JVM already makes it extremely cheap to create and destroy objects: creation is always ~free (just a pointer increment), and then destruction is copying, so very sensitive to memory bandwidth but done in parallel. If most of your objects are dying young then deallocation is "free" (amortized over the cost of the remaining live objects). Given the reported bandwidth claims for the M1 if they ever make a server version of this puppy I'd expect to see way higher GC throughput on it too (maybe such a thing can be seen even on the 16GB laptop version).
The problem with Java on the desktop is twofold:
1. Versions that are mostly used don't give memory back to the OS even if it's been freed by the collector. That doesn't start happening by default until like Java 14 or 15 or so, I think. So your memory usage always looks horribly inflated.
2. If you start swapping it's death because the GC needs to crawl all over the heap.
There are comments here saying the M1 systems rely more heavily on swap than a conventional system would. In that case ARC is probably going to help. At least unless you use a modern pauseless GC where relocation is also done in parallel. Then pausing background threads whilst they swap things in doesn't really matter, as long as the app's current working set isn't swapped out to compensate.
But when swap hits 8-9 Gb, it’s effects start to get very noticeable.
Besides, a lot of memory usage is in web browsers, which must use garbage collection.
Looking at the reviews of M1 Macs, those systems are still responsive and making forward progress at a “memory pressure” that would make my x86 Mac struggle in a swap storm. It seems to come down to very fast access to RAM and storage, large on-die caches, and perhaps faster memory compression.
2 more points:
- All the evidence I've seen is gifs of people opening applications in the dock, which is... not impressive. I can do that already, apps barely allocate at all when they open to "log in to iCloud" or "Safari new tab". And don't we see that literally every time Apple launches Mac hardware? Sorry all tech reviewers everywhere, try measuring something.
- I think the actual wins come from the zillion things Apple has done in software. Like: memory compression, which come to think of it might be possible to do in hardware. Supposedly a lot of other work/tuning done on the dynamic pager, which is maybe enabled by higher bandwidth more than anything else.
Fun fact: you can stress test your pager and swap with `sudo memory_pressure`. Try `-l critical`. I'd like to see a benchmark comparing THAT under similar conditions with the previous generation.
I'm curious about FP/vector performance, but I'm pretty sure it's fine. I'm definitely eyeing a MBP myself! 20 hours of video playback? Crazy...
Because the M1 is similar to the chips used in iOS, hence the comparison is not inappropriate.
>The memory bandwidth on the new Macs is impressive. Benchmarks peg it at around 60GB/sec–about 3x faster than a 16” MBP. Since the M1 CPU only has 16GB of RAM, it can replace the entire contents of RAM 4 times every second. Think about that…
Or that's how I understand this, I don't actually own M1 Mac.
https://www.anandtech.com/show/16252/mac-mini-apple-m1-teste...
> Besides the additional cores on the part of the CPUs and GPU, one main performance factor of the M1 that differs from the A14 is the fact that’s it’s running on a 128-bit memory bus rather than the mobile 64-bit bus. Across 8x 16-bit memory channels and at LPDDR4X-4266-class memory, this means the M1 hits a peak of 68.25GB/s memory bandwidth.
The point of the memory bandwidth is so that it never has to swap to disk in the first place.
What? How does memory bandwidth obviate the need for disk swapping?
It is. We know they're using a unified memory architecture, they pointed it out in the presentation.
The reason GDDR isn't typically used for system RAM is it's higher latency & more power hungry. Like, the GDDR6 memory on a typical discreet card uses more power than the an entire M1-powered Mac Mini power hungry.
What "normal" laptop has that?
The latency appears real to me.
Separately from that x86's TSO-ish memory model also imposes a performance cost whether your algorithm needs those guarantees or not. Code sometimes relies on those guarantees without knowing it. Absent hardware support you would need to insert ARM atomics in translated code to preserve those guarantees which on most ARM CPUs would impose a lot of overhead. The M1 allows Rosetta to put the CPU into a memory ordering mode that preserves the expected memory model very efficiently (as well as using 4K page size for translated processes).
They are fast for atomics but still far, far slower than the equivalent non-atomic operation. An add operation takes around half a cycle (upper bound here - with how wide the firestorm core is an add operation is almost certainly less than half a cycle). At 1ghz a cycle is 1 nanosecond. The M1 runs at around 3ghz. So you're still talking the atomic operation being >10x slower than non-atomics.
Which should not be surprising at all. Apple didn't somehow invent literal magic here. They still need coherency across 8 cores, which means at a minimum L1 is bypassed for the atomic operation. The L2 latency is very impressive, contributing substantially to that atomic operation performance. But it's still coming at a very significant cost. It's very, very far from free. There's also no ARM vs. x86 difference here, since the atomic necessarily forces a specific memory ordering guarantee that's stricter than x86's default. Both ISAs are forced to do the same thing and pay the same costs.
How did you arrive at this number?
It's in the post. Half a cycle for an add or less, and cycles are every 1/3 nanosecond. So upper bound for an add would be around 1/6th a nanosecond. Likely less than that still yet, since the M1 is probably closer to an add in 1/8th a cycle not 1/2. Skylake by comparison is at around 1/4th a cycle for an add, and since M1's IPC is higher it's not going to be worse at basic ALU ops.
6 nanoseconds @ 3ghz is 18 cycles. That's on the slow end of the spectrum for a CPU instruction.
I'm not an EE expert and I haven't torn apart an M1, but Occams's Razor would suggest it's unlikely they made specialized hardware for NSObjects specifically. Other ARC systems on the same hardware would likely see similar benefits.
Kotlin/Native lets us do this comparison somewhat directly. The current and initial versions used reference counting for memory management. K/N binaries were far, far slower than the equivalent Kotlin programs running on the JVM and the developer had to deal with the hassle of RC (e.g. manually breaking cycles). They're now switching to GC.
The notion that GC is less memory efficient than RC is also a canard. In both schemes your objects have a mark word of overhead. What does happen though, is GC lets you delay the work to deallocate from memory until you really need it. A lot of people find this quite confusing. They run an app on a machine with plenty of free RAM, and observe that it uses way more memory than it "should" be using. So they assume the language or runtime is really inefficient, when in reality what's happened is that the runtime either didn't collect at all, or it collected but didn't bother giving the RAM back to the OS on the assumption it's going to need it again soon and hey, the OS doesn't seem to be under memory pressure.
These days on the JVM you can fix that by using the latest versions. The runtime will collect and release when the app is idle.
Specifically, as I understood it is that Apple software (written in objective C/Swift) uses a lot of retain/release (or Atomic Reference Counting) on top of manual memory, for memory management rather than other forms of garbage collection (such as those found in Java/C#), which gives Objective C programs a lower memory overhead (supposedly). This is why the iPhone ecosystem is able to run so much more snappier than the Android ecosystem.
That said, I don't see how that translates to lower memory usage than x86 programs. I think the supporting quotes he used for that point are completely orthogonal. I don't have an M1 mac, but I believe the same program running on both machines should use the same amount of memory.
I think you can reach a lot more than that. Presumably, on Intel they use something like LZO or LZ4, since it compresses/decompresses without too much CPU overhead. But if you have dedicated hardware for something like e.g. Brotli or zstd, one could reach much higher compression ratios.
Of course, this is assuming that memory can be compressed well, but I think this is true in many cases. E.g. when selecting one of the program/library files in the squash benchmarks:
https://quixdb.github.io/squash-benchmark/
you can observe higher compression ratios for e.g. Brotli/gzip/deflate than LZO/LZ4.
Edit: apparently, this isn't common knowledge.
GC vs RC is not a trivial comparison to make, but overall there are good reasons new systems hardly use RC (Objective-C dating back to the 90s isn't new). Where RC can help is where you have a massive performance cliff on page access, i.e. if you're swapped to disk. Then GC is terrible because it'll try and page huge sections of the heap at once where as RC is way more minimal in what it touches.
But in most other scenarios GC will win a straight up fight with an RC based system, especially when multi-threading gets involved. RC programs just spend huge amounts of time atomically incrementing and decrementing things, and rummaging through the heap structures, whereas the GC app is flying along in the L1 cache and allocations are just incrementing a pointer in a register. The work of cleaning up is meanwhile punted to those spare cores you probably aren't using anyway (on desktop/mobile). It's tough to beat that by hand with RC, again, unless you start hitting swap.
If M1 is faster at memory ops than x86 it's because they massively increased memory bandwidth. In fact I'd go as far as saying the CPU design is probably not responsible for most of the performance increase users are seeing. Memory bandwidth is the bottleneck for a lot of desktop tasks. If M1 core is say 10% faster than x86 but you have more of them and memory bandwidth is really 3x-4x larger, and the core can keep far more memory ops in flight simultaneously, that'll explain the difference all by itself.
On the other hand, that same paper shows that for every single one of their tested workloads, the generational GC outperforms manual memory management. Now obviously, you could do better with manual memory management if you took the time to understand the memory usage of your application to reduce fragmentation and to free entire arenas at a time, but for applications that don't have the developer resources to apply to that (the vast majority), the GC will win.
I'm not saying that better memory management is the reason Android wins these launch to interactivity benchmarks because the difference is so stark relative to the hardware performance that memory management isn't nearly enough to explain it, but it does contribute to it. (My own guess is that most of the performance difference comes from smarter process initialization from usage data. Apple is notoriously bad at using data for optimization.)
Apple has decades of proven experience producing and shipping massively over engineered systems. I believe em when they say these processors do ARC natively.
Hopefully I can clear up the discussion a little:
Q: Does reference counting 'use' less RAM than GC?
A: Yes (caveats etc. go here, but your question is a good explanation)
Q: Does the M1 in and of itself require less RAM than x86 processors?
A: No
Q: So why are people talking about the M1 and its RAM usage as if it's better than with x86?
A: It's really just around the faster reference counting. MacOS was already pretty efficient with RAM.
I'd like to propose tokamak-teapot's formula for hardware purchase:
Minimum RAM believed to be required = actual amount of RAM required * 2
N.B. I am aware that a sum that's greater than 16GB doesn't magically become less than 16GB, but it is somewhat surprising how well MacOS performs when it feels like RAM should be tight, so I'd suggest borrowing a Mac or making a Hackintosh to experience this if you're anxious about hitting the ceiling.
Then they pivoted into automating retain/release patterns from Cocoa and sold it, Apple style, as a victory of RC over tracing GC, while moving the GC related docs and C related workarounds into the documentation archive.
Operative word: tried. GC was an optional desktop-only component deprecated in Mountain Lion, which IIRC has not been accepted on MAS since 2015 was removed entirely from Sierra.
Without going into irrelevant weeds, "apple has always used refcounting everywhere" is a much closer approximation.
Which then in Apple style ("you are holding it wrong") turned it around in a huge marketing message, while hiding away the tracing GC efforts.
That's not exactly relevant to the subject at hand of what memory-management method software usually uses on macos.
> Which then in Apple style ("you are holding it wrong") turned it around in a huge marketing message, while hiding away the tracing GC efforts.
Hardly?
And people are looking to refcounting as a reason why apple software is relatively light on memory, which is completely fair and true and e.g. generally assumed as one of the reasons why ios devices fare well with significantly less ram than equivalent android devices. GCs have significant advantages, but memory overhead is absolutely one of the drawbacks.
https://github.com/ixy-languages/ixy-languages
And the fact that M1 has special instructions dedicated to optimize RC,
https://blog.metaobject.com/2020/11/m1-memory-and-performanc...
Memory overhead in languages with tracing GC (RC is a GC algorithm) only happens in languages like Java without support for value types.
If the language supports value types, e.g. D, and there is still memory overhead versus RC, then fire the developers or they better learn to use the language features available on their plate.
> https://github.com/ixy-languages/ixy-languages
This shows latency, not memory consumption, as far as I can tell.
> If the language supports value types, e.g. D, and there is still memory overhead versus RC, then fire the developers or they better learn to use the language features available on their plate.
Memory overhead of certain types of garbage collectors (notably generational ones) is well-known and it's specified relative to the size of the heap that they manage. Using value types is of course a valid point, regarding how you should use the language, but it doesn't change the overhead of the GC, it just keeps the heap it manages smaller. If the overhead was counted against the total memory use of a program, then we wouldn't be talking about the overhead of the garbage collector, but more about how much the garbage collector is actually used. Note that I'm not arguing against tracing GCs, only trying to keep it factual.
(It explains why iOS does better with less ram than android, but the quote is specifically claiming this as a reason for 8GB ram to be acceptable)