I just got one. I’m blown away by the speed as well. Chrome runs insanely fast! Alas, it’s not developer ready yet. Brew is a mess. Docker doesn’t work. PyCharm is WIP although can use x86 version. I was skeptical of the hype but this little laptop has made me realize how slow everything else is. Unfortunately, while the hardware has accelerated far beyond expectations, the software - specifically MacOS BigSur is a m…
> I want macOS that has a UI of Windows 95. That would be totally insane on Apple Silicon. So much this. I used to run Windows 7 in classic mode and really liked the low footprint, no nonsense appearance. Windows 10 has no such mode. I wonder if we will see Linux support
Apple Silicon M1: Black Magic Fuckery
121–130 of 1001 posts
Re: Apple Silicon M1: Black Magic Fuckery
#122This is fascinating: > Retain and release are tiny actions that almost all software, on all Apple platforms, does all the time. ….. The Apple Silicon system architecture is designed to make these operations as fast as possible. It’s not so much that Intel’s x86 architecture is a bad fit for Apple’s software frameworks, as that Apple Silicon is designed to be a bespoke fit for it …. retaining and releasing NSObjects i…
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, the speed at which your retains/releases happen isn’t going to be the issue. it’s not like intel macs use GC where m1 uses RC. (It explains why iOS does better with less ram than android, but the quote is specifically claiming this as a reason for 8G…
Re: Apple Silicon M1: Black Magic Fuckery
#123This is fascinating: > Retain and release are tiny actions that almost all software, on all Apple platforms, does all the time. ….. The Apple Silicon system architecture is designed to make these operations as fast as possible. It’s not so much that Intel’s x86 architecture is a bad fit for Apple’s software frameworks, as that Apple Silicon is designed to be a bespoke fit for it …. retaining and releasing NSObjects i…
How does this work? Isn't reference counting a lot of +1 and -1?
So low latency of the cache to system RAM can help here, at least for cases where the Rc is shared between threads. But also if the thread is not shared between threads but the thread is moved to a different CPU. Still it's probably not the main reason.
Given how atomic (might) be implemented on ARM and that the cach and memory is on the same chip my main guess is that they did some optimizations in the coherency protocol/implementation (which keeps the memory between caches and the system memory/RAM coherent). I believe there is a bit of potential to optimize for RC, i.e. to make that usage pattern of atomics fast. Lastly they probably take special care that the atomic related instructions used by Rc are implemented as efficient as possible (mostly fetch_add/fetch_sub).
Re: Apple Silicon M1: Black Magic Fuckery
#124You know the thing I worry about next: how are apps going to inevitably bloat in inefficiency and claw back the improvements in CPU?
In Electron 14, we have found an astounding new paradigm for writing apps that results in astounding developer time savings! Now instead of running your Javascript app inside a copy of Chrome, we have a custom hybrid of Javascript and Lisp, which is internally transpiled onto a Brainfuck interpreter running in Conway's Life! Electron 15: The Life machine has been re-implemented as a series of GPU instructions, which…
Re: Apple Silicon M1: Black Magic Fuckery
#125I know very little, so perhaps someone could enlighten me. But I am curious how Apple Silicon will be for machine learning. When Apple releases a MacBook Pro with 64GB of unified memory (assuming they will) — won’t that be amazing for machine learning? I am under the impression that GPU memory is a huge factor in performance. Also, is there any way that the neural engine can accelerate training — or is it just for ex…
I wouldn't expect it to be particularly competitive in training large models. It's an integrated GPU with 8 cores, and the "neural engine" has an additional 16 cores. The kinds of discrete GPUs (mostly Nvidia) that people use for deep learning have more like 5000+ cores. I think Apple is aiming more at either training small models, or running pre-trained models. For example Photoshop is starting to integrate neural f…
Re: Apple Silicon M1: Black Magic Fuckery
#126Earlier quoted context omitted.
On the other hand, Apple is fashion. They have always advertised and placed themselves as such. If you want functionality, you need a non-Mac running Linux. I'm just hoping we have better silicon options soon, for those of us who don't want customization-unfriendly, upgrade-unfriendly Apple hardware.
While you're getting rightfully downvoted (although I won't join in), it's worth underlining how much "they have always advertised and placed themselves as such" is utter nonsense. Apple has always advertised functionality and performance of their products, even when the case was (charitably) pretty shaky. They advertised their PowerPC Macs that way, up until the point they couldn't, then advertised their Intel Macs…
"I'm a Mac." "And I'm a PC." was _absolutely_ designed to appeal to the fashionable, "hey, you're going to be far cooler than this stuffy nerd" than just "functionality and performance".
So, in among everything else, they absolutely do care about looks. I'll agree that they don't "[not care] about anything but looks", but it's a part of the brand.
Re: Apple Silicon M1: Black Magic Fuckery
#127You know the thing I worry about next: how are apps going to inevitably bloat in inefficiency and claw back the improvements in CPU?
In Electron 14, we have found an astounding new paradigm for writing apps that results in astounding developer time savings! Now instead of running your Javascript app inside a copy of Chrome, we have a custom hybrid of Javascript and Lisp, which is internally transpiled onto a Brainfuck interpreter running in Conway's Life! Electron 15: The Life machine has been re-implemented as a series of GPU instructions, which…
Re: Apple Silicon M1: Black Magic Fuckery
#128Earlier quoted context omitted.
On the other hand, Apple is fashion. They have always advertised and placed themselves as such. If you want functionality, you need a non-Mac running Linux. I'm just hoping we have better silicon options soon, for those of us who don't want customization-unfriendly, upgrade-unfriendly Apple hardware.
While you're getting rightfully downvoted (although I won't join in), it's worth underlining how much "they have always advertised and placed themselves as such" is utter nonsense. Apple has always advertised functionality and performance of their products, even when the case was (charitably) pretty shaky. They advertised their PowerPC Macs that way, up until the point they couldn't, then advertised their Intel Macs…
Re: Apple Silicon M1: Black Magic Fuckery
#129Earlier quoted context omitted.
I don't think retain/release perf has anything to do with memory consumption, but I have seen a bunch of reviews claiming that 8GB is perfectly fine. This is fascinating to me, because: (a) every 8GB Mac I've used in the past has been unusably slow (b) since upgrading my 32GB Hackintosh to Big Sur, my usual 40GB working set is only about 20GB. (c) My 2015 16GB MBPr with Big Sur is also using about half as much physic…
Every 8gb mac I’ve used before was fine, and that was with running Xcode, Firefox, photoshop, mail, terminal, and other programs.
The only situation I ever ran into where it was a problem was in trying to run multiple VM's at once.
Otherwise it's just a non-issue. Programs often reserve a lot more memory than they actually use (zero hit in performance) so memory stats are misleading, and the OS is really good at swapping memory not touched in a while to the SSD without you noticing.
Yes, sometimes it takes a couple seconds to switch to a tab I haven't touched in Chrome in days because it's got to swap it back in from the SSD. Who cares?
Re: Apple Silicon M1: Black Magic Fuckery
#130I just got one. I’m blown away by the speed as well. Chrome runs insanely fast! Alas, it’s not developer ready yet. Brew is a mess. Docker doesn’t work. PyCharm is WIP although can use x86 version. I was skeptical of the hype but this little laptop has made me realize how slow everything else is. Unfortunately, while the hardware has accelerated far beyond expectations, the software - specifically MacOS BigSur is a m…
Hey, what if we just... kept it that way?
Kiss all that speed goodbye once software developers get their hands on this. And then the low end current-gen models will be even slower when running "the code that runs ok on the M1 hardware, I guess, ship it."