this post was submitted on 30 Nov 2023
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Well the issue at hand is that this is starting to get to the point that like the x86 arch, you cannot just move the NR_CPUS value upward and call it done. The kernel needs to keep some information on hand about the CPUs, it's usually about 8KB per CPU. That is usually allocated on the stack which is a bit of special memory that comes with some assurances like it being continuous and when things go out of scope they are automagically deallocated for you.
However, because of those special assurances, just simply increasing the size of the stack can create all kinds of issues. Namely TLB missing, which one of the things to make CPUs go faster is to move bits of RAM into some special RAM inside the CPU called cache (which there's different levels of cache and each level has different properties which is getting a bit too deep into details). The CPU attempts to make a guess as to the next bit of RAM that needs to move into cache before it's actually needed, this called prediction. Usually the CPU gets it right but sometimes it gets it wrong and the CPU must tell the actual core that it needs to wait while it goes and gets the correct bit of RAM, because the cores move way faster than the transfer of RAM to the cache, this is why the CPU needs to move the bits from RAM into cache before the core actually needs it.
So keeping the stack small pretty much ensures that you can fit the stack into one of the levels of cache on the CPU and allows the stack to be fast and have all that neat automagical stuff like deallocation when it goes out of scope. So you just cannot increase the NR_CPUS value because the stack will just get too large to nicely fit inside the cache, so it'll get broken up into "pages" with the current page in cache and the other one still in RAM and there will be swapping between the pages which can introduce TLB misses.
So the patch being submitted for particular configurations will set the CPUMASK_OFFSTACK flag. This moves that CPU information that's being maintained to be off of the stack. That is to be allocated with slab allocation. Slab allocation is a kernel allocation algorithm that's a bit different than if you did the usual C style
malloc
orcalloc
(which I will indicate that for any C programmers out there, you should usecalloc
first and if you have reasons usemalloc
. Butcalloc
should be your go to for security reasons but I don't want to paper over details here by just saying usecalloc
and never usemalloc
. There's a difference and that difference is important in some cases).Without deep diving into kernel slabs, slabs are a bit different in that they don't have some of those nice automagical things that come with the stack memory. So one must be a bit more careful with how they are used, but that's the nice thing about the slab allocator is that it's pretty smart about ensuring it's doing the right thing. This is for the 5.3 kernel, but I love the charts that give a overview of how the slab allocator works. It's pretty similar in 6.x kernels, but I don't have any nifty charts for that version, but if some does I will love you if you posted a link.
That said, it's a bit slower but a fair enough tradeoff until there's some change in ARM Cortex-X memory cache arrangement. Which going from memory I think Cortex-X4 has 32MB shared L3 cache, which if you have 8KB on the 8192 CPU max, you'll need 64MB just to hold the CPU bitmap in L3 which is slow compared to the other levels. And there's other stuff you're going to need in the cache at any given time so hogging it all is not ideal. Setting the limit for stack usage to 512 is good as that means the bitmap is just 4MB and you can schedule well ahead of time (the kernel has a prefetcher which things within the kernel can do all kinds of special stuff with it to indicate when a bit of RAM needs to be moved into cache, for us measly users we can only make a suggestion called a hint, to the prefetcher) when to move it all into cache or leave it in RAM. So it's a good balance for the moment.
But Server style ARM is making headway and so it makes sense to do a lot with it in the same way the kernel handles server style x86 and other server style archs like POWER and what not. But not mess with it too much for consumer style ARM, which hardly needs these massive bitmaps.
Since you seem to know a lot about this: I would think that at some point the purely physical size of a device is prohibitive of using shared cache, just because the distance from a cpu to the cache can't be too big. Do you know when this comes into play, if it does? Also, having written some multithreaded computational software, I've found that there's typically (for the stuff I do) a limit to how many cores I can efficiently make use of, before the overhead of opening and closing threads eats the advantage of sharing the work between cores. What kind of "everyday" server stuff is efficiently making use of ≈300 cores? It's clearly some set of tasks that can be done independently of one another, but do you know more specifically what kind of things people need this many cores on a server for?
Traditionally VMs would be the use case, but these days, at least in the Linux/cloud world, it's mainly containers. Containers, and the whole ecosystem that is built around them (such as Kubernetes/OpenShift etc) simply eat up those cores, as they're designed to scale horizontally and dynamically. See: https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale
Normally, you'd run a cluster of multiple servers to host such workloads, but imagine if all those resources were available on one physical hosts - it'd be a lot more effecient, since at the very least, you'd be avoiding all that network overhead and delays. Of course, you'd still have at least a two node cluster for HA, but the efficiency of a high-end node still rules.
Exactly! Imagine you have two services in a data center. If they have to communicate a lot with each other, then you would prefer them as close to each other as possible. Why? Well it's because of the difference between sending a request over a network vs. just sending it to another process on the same host. It's much more efficient in terms of latency and bandwidth. There are, of course, downsides and other other costs (like the fact that the cores that are handling the requests themselves are much less powerful), so you have to tailor your hardware allocation to your workloads. In general, if you're CPU-bound, you would want more powerful CPUs (necessitating fewer cores per host for power reasons), and if you're I/O bound, you want to reduce network latency as much as possible.
Now imagine you have thousands of services. The network I/O can get pretty extreme. Plus, occasionally, you have requirements like the fact that any data traveling from one host to another must be encrypted. So if you can keep as many services as possible on a single host, you reduce a lot of that overhead as well.
tl;dr: everything comes down to trade-offs and understanding the needs of your workloads, but in general, running 300 low power cores is probably indicative of an I/O-bound application and could hypothetically be much more efficient and cost-effective.