Comment by tialaramex

> The RAM trade off is excellent for normal sizes but if you scale enormously the trade off eventually reverses

I don't think you've watched the talk. The minimal RAM-per-core is quite high, and often sits there unused even though it could be used to reduce the usage of the more expensive CPU. You pay for RAM that you could use to reduce CPU utilisation and then don't use it. What you want to aim for is a RAM/CPU usage that matches the RAM/CPU ratio on the machine, as that's what you pay for. Doubling the CPU often doubles your cost, but doubling RAM costs much less than that (5-15%).

If two implementations of an algorithm use different amounts of memory (assuming they're reasonable implementations), then the one using less memory has to use more CPU (e.g. it could be compressing the memory or freeing and reusing it more frequently). Using more CPU to save on memory that you've already paid for is just wasteful.

Another way to think about it is consider the extreme case (although it works for any interim value) where a program, say a short-running one, uses 100% of the CPU. While that program runs, no other program can use the machine, anyway, so if you don't use up to 100% of the machine's RAM to reduce the program's duration, then you're wasting it.

As the talk says, it's hard to find less than 1GB per core, so if a program uses computational resources that correspond to a full core yet uses less than 1GB, it's wasteful in the sense that it's spending more of a more expensive resource to save on a less expensive one. The same applies if it uses 50% of a core and less than 500MB of RAM.

Of course, if you're looking at kernels or drivers or VMs or some sorts of agents - things that are effectively pure overhead (rather than direct business value) - then their economics could be different.

> Second thing though: Unpredictability. GC means you can't be sure when reclamation happens.

What you say may have been true with older generations of GCs (or even something like Go's GC, which is basically Java's old CMS, recently removed after two newer GC generations). OpenJDK's current GCs, like ZGC, do zero work in stop-the-world pauses. Their work is more evenly spread out and predictable, and even their latency is more predictable than what you'd get with something like Rust's reference-counting GC. C#'s GC isn't that stellar either, but most important server-side software uses Java, anyway.

The one area where manual memory management still beats the efficiency of a modern tracing GC (although maybe not for long) is when there's a very regular memory usage pattern through the use of arenas, which is another reason why I find Zig particularly interesting - it's most powerful where modern GCs are weakest.

By design or happy accident, Zig is very focused on where the problems are: the biggest security issue for low-level languages is out-of-bounds access, and Zig focuses on that; the biggest shortcoming of modern tracing GCs is arena-like memory usage, and Zig focuses on that. When it comes to the importance of UAF, compilation times, and language complexity, I think the jury is still out, and Rust and Zig obviously make very different tradeoffs here. Zig's bottom-line impact, like that of Rust, may still be too low for widespread adoption, but at least I find it more interesting.

> As I understand it this one is why Microsoft are rewriting Office backend stuff in Rust after writing it originally in C#

The rate at which MS is doing that is nowhere near where it would be if there were some significant economic value. You can compare that to the rate of adoption of other programming languages or even techniques like unit testing or code review. With any new product, you can expect some noise and experimentation, but the adoption of products that offer a big economic value is usually very, very fast, even in programming.