Comment by post-it

Caches are automatically released by the OS when demand for memory increases.

Why he wouldn't say it about HDD space? You buy HDD to keep them empty?

And as for the money analogy, what's the idea there, that memory grows interest? Or that it's better to put your money in the bank and leave it there, as opposed to buy assets or stocks, and of course, pay for food, rent, and stuff you enjoy?

It’s counterintuitive but I learned this best by playing RTS games. If you don’t spend money your opponent can outdo you on the map by simply spending their money. But the principle extends, everything you have doing nothing (buildings units etc) is losing. The most efficient process is to have all your resources working for you at all times.

> about money in the bank?

Yes, generally. That's the entire idea behind the stock market.

I am not following, isn't this just a graph that shows that how fast operations happen is largely dependent on the odds that it is in cache at various levels (CPU/Ram/Disk)?

The memory operation itself is O(1), around 100 ns, where at a certain point we are doing full ram fetches each time because the odds of it being in CPU cache are low?

Typically O notation is an upper bound, and it holds well there.

That said, due to cache hits, the lower bound is much lower than that.

You see similar performance degradation if you iterate in a double sided array the in the wrong index first.

Memory access performance depends on the _maximum size of memory you need to address_. You can clearly see it in the graph of that article where L1, L2, L3 and RAM are no longer enough to fit the linked list. However while the working set fits in them the performance scales much better. So as long as you give priority to the working set, you can fill the rest of the biggest memory with whatever you want without affecting performance.

> Every byte used makes the whole thing slower.

This is an incorrect conclusion to make from the link you posted in the context of this discussion. That post is a very long-winded way of saying that the average speed of addressing N elements depends on N and the size of the caches, which isn't news to anyone. Key word: addressing.

RAM is always storing something, it’s just sometimes zeros or garbage. Nothing in how DRAM timings work is sensitive to what bits are encoded in each cell.

Huh? There is nothing called "empty memory". There is always something being stored in the memory, the important thing is whether you care about that specific bits or not.

And no, the articles you linked is about caching, not RAM access. Hardware-wise, it doesn't matter what you have in the cells, access latency is the same. There is gonna be some degradation with #read/write cycles, but that is besides the point.

why is it not O(1)? It has to service within a deadline time, so it is still constant.

The author of that post effectively re-defines "memory"/"RAM" as "data", and uses that to say "accessing data in the limit scales to N x sqrt(N) as N increases". Which, like, yeah? Duh, I can't fit 200PB of data into the physical RAM of my computer and the more data I have to access the slower it'll be to access any part of it without working harder at other abstraction layers to bring the time taken down. That's true. It's also unrelated to what people are talking about when they say "memory access is O(1)". When people say "memory access is O(1)" they are talking about cases where their data fits in memory (RAM).

Their experimental results would in fact be a flat line IF they could disable all the CPU caches, even though performance would be slow.

> I've been absolutely mindblown at how fast my 3 8GB M1 Macbook Airs I just bought for ~$350 brand new

Wait what? How did you manage that?

The old mental model doesn't fit how any OS manages RAM. Every OS plays all sorts of fun guessing games about caching, predicting what resources your program will actually need etc. The OS does a lot of work to ensure that everything just hums along as best as possible.

I mean at that point (13 GB memory used), the "Swap used" is at several hundred megabytes.

And if I more apps (or browser tabs), the "Swap used" keeps increasing, and the "memory pressure" graph switches color from green to yellow.

The color of that graph is the indicator I'm using to know that I should close my browser tabs :p

I'd rather my ram go to my page cache, not have bloated apps hoarding it.

Same. I've been programming in Go on an M1 for years and perf is spectacular.

Because 8GB is literally enough? There are multiple 8GB Macs in this house and they are fine. I wouldn't use them for development work but they're completely competent at the basics.

Perhaps because it's enough for a lot of things. I only came up against the 8GB limit when I ran a LLM locally using Ollama. It worked but wasn't workable.

8GB isn't ideal though and 16GB would've expanded its capacity to do more things. But soon as I want to do more things I shuffle over to my PC with it's dedicated GPU and 32GB o ram

I'm guessing Apple cuts capability to the lower end so as not to hurt sales of the higher end. Usage profile is often dependent on context. There are enough non-power users (when mobile) like me that 8GB isn't ideal but it's enough. And if it wasn't enough we could've paid more for the 16GB, but I personally decided it wasn't worth the ridiculous Apple ram price premium.

So these are my reasons for saying 8GB is enough. I'm also using an M1 MacBook Air, so the puniest of the lineup. Next laptop I'm considering is possibly a think pad with linux so I'm no macOS fanboi.

> I wonder, why is it physically painful for some Apple owners

This wasn't necessary. I was just pointing out that 8GB hardware is not the full story. It's also true with windows, as you correctly point out. If you're coming from a slow SSD, or even Linux (it's a relatively new feature to have on by default) you might be pleasantly surprised.

Also, I'm an Apple owner and I have no problem saying it's not enough for anyone on this website. I tried it for a few years as my "second screen" computer, and would bump against it all the time, with glorious screeching as the audio skipped. But, I'm also a developer/power user.

The majority of people aren't power users.and that's the target audience for this. Clearly.

8GB has been completely fine for every non power user I know. Again, the majority of people do everything within a browser, maybe play some music/video at the same time, maybe open an office type app. It's completely acceptable for that, and that should not surprise you, as someone who has an understanding of memory usage and paging, and high bandwidth SSDs, in the slightest.