And is the speed up over malloc/free due to large block allocation as opposed to individual malloc?

https://godbolt.org/z/1oTrv1Y58

Messing with it a bit, it seems like yours has a slightly shorter dependency chain due to loading the two members separately, where UPB loads them as a pair (as it needs both in order to determine how much size is available). Also seems to have less register pressure. I think that's because yours bumps down. UPB's supports in place forward extension, so it needs to bump up.

If you added branch hints to signal to the compiler that your slow path is not often hit, you might see some improvement (although if you have PGO it should already do this). These paths could also be good candidates for the `preserve_most` calling convention.

However, there is an unfortunate compiler behavior here for both implementations - it doesn't track whether the slow path (which is not inlined, and clobbers the pointers) was actually hit, so it reloads on the hot path, for both approaches. Unfortunately this means that a sequence of allocations will store and load the arena pointers repeatedly, when ideally they'd keep the current position in a register on the hot path and refill that register after clobbering in the cold path.

You were measuring malloc, so of course you came up with numbers that were 20 times worse than PyPy's nursery allocator. That's because malloc is 20 times slower, whatever common sense may say.

Also, you're talking about tail latency, while CF Bolz was measuring throughput. Contrary to your assertion, throughput is indeed a meaningful metric, though, especially for interactive UIs such as videogames, tail latency is often more important. For applications like compilers and SMT solvers, on the other hand, throughput matters more.

You're right that there are some other costs. A generational or incremental GC more or less requires write barriers, which slow down the parts of your code that mutate existing objects instead of allocating new ones. And a generational collector can move existing objects to a different address, which complicates FFIs that might try to retain pointers to them outside the GC's purview.

There are a lot of papers running captured allocation traces like you did against different memory allocators, some of them including GCed allocators. Ben Zorn did a lot of work like this in the 90s, coming to the conclusion that GC was somewhat faster than malloc/free. Both GCs and mallocs have improved substantially since then, but it would be easy to imagine that the results would be similar. Maybe someone has done it.

But to a significant extent we write performance-critical code according to the performance characteristics of our platforms. If mutation is fast but allocation is slow, you tend to write code that does less allocation and more mutation than you would if mutation was slow and allocation was fast, all else being equal. So you'd expect code written for platforms with fast allocation to allocate more, which makes faster allocation a bigger advantage for that code. Benchmarking captured allocation traces won't capture that effect.

Tail latency has always been one of the worst things about GCs. I hear amazing things about new real-time GC algorithms, including remarkable reports from Azul, but I don't know if they're true or what their price is.

> I don’t believe this in even the slightest. That is not a meaningful metric for literally any actual workload in the universe. It defies common sense.

I just want to make the point here that making bump allocators fast is useful because it's an essential part of modern allocators and will be called A LOT in case of CPython.

That benchmark is useful to make sure the codegen/JIT is performing like it should, but it is still just measuring bump pointer allocation in the most ideal situation. 2 cycles/alloc is entirely normal reasonable considering modern uarchs, the critical path is two one-cycle uops.

Now, I agree with your point that this is far from meaningful for the p95/p99 of actual workloads. That is indeed the metric that users or customers actually care about. Modern GCs, like Go's, have moved away from using throughput as a metric. It's "easy" to make bump allocators fast, the hard problem is everything else around it: you still need to allocate your arenas/pools and manage the object lifetime. In the case of CPython that's refcounting, deallocs, GCs, work that modern CPUs really suck at (bad locality, bad branching).

I measured ~19.5% of the wallclock time of pyperformance benchmarks being related to memory management (ISPASS25 poster). That's not taking into account the extra boxing and struct slots being created for bookkeeping purposes, which adds more second-order effect related to cache pressure. It's insanely hard to accurately measure memory management overheads.