My summary is that it's about one or two allocations per nanosecond on CF Bolz's machine, an AMD Ryzen 7 PRO 7840U, presumably on one core, and it's about 11 instructions per allocation.
This is about 2–4× faster than my pointer-bumping arena allocator for C, kmregion†, which is a similar number of instructions on the (inlined) fast path. But possibly that's because I was testing on slower hardware. I was also testing with 16-byte initialized objects, but without a GC. It's about 10× the speed of malloc/free.
I don't know that I'd recommend using kmregion, since it's never been used for anything serious, but it should at least serve as a proof of concept.
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.
Thank you very much! I vaguely remember that it did that, and the failure to keep the pointer in registers might explain why PyPy's version is twice as fast (?).
It is a bump allocator. I don't know why it's so much faster than mine, but my hypothesis was that CF Bolz was testing on a faster machine. The speedup over malloc/free is because bumping a pointer is much faster than calling a subroutine.
The reason why their allocator is faster than Boehm isn't because of conservative stack scanning.
You can move objects while using conservative stack scanning. This is a common approach. JavaScriptCore used to use it.
You can have super fast allocation in a non-moving collector, but that involves an algorithm that is vastly different from the Boehm one. I think the fastest non-moving collectors have similar allocation fast paths to the fastest moving collectors. JavaScriptCore has a fast non-moving allocator called bump'n'pop. In Fil-C, I use a different approach that I call SIMD turbosweep. There's also the Immix approach. And there are many others.
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.
Sorry, I refuse to accept a trivial loop of generating up to 2 objects at a time as ever being a relevant benchmark. I didn’t say throughput wasn’t relevant. I said this test isn’t convincing.
> coming to the conclusion that GC was somewhat faster than malloc/free
Many people have made that conclusion. Certainly not true for real-time cases where tail latency is critical.
For malloc/free specific workload is much more important than for a bump allocator; malloc potentially has branching on size, different cache access/utilization patterns, dependency on distance/pattern between mallocs & frees, whereas a bump allocator is just bumping a single pointer, practically without any branching, and very simple linear cache utilization.
My summary is that it's about one or two allocations per nanosecond on CF Bolz's machine, an AMD Ryzen 7 PRO 7840U, presumably on one core, and it's about 11 instructions per allocation.
This is about 2–4× faster than my pointer-bumping arena allocator for C, kmregion†, which is a similar number of instructions on the (inlined) fast path. But possibly that's because I was testing on slower hardware. I was also testing with 16-byte initialized objects, but without a GC. It's about 10× the speed of malloc/free.
I don't know that I'd recommend using kmregion, since it's never been used for anything serious, but it should at least serve as a proof of concept.
______
† http://canonical.org/~kragen/sw/dev3/kmregion.h http://canonical.org/~kragen/sw/dev3/kmregion.c http://canonical.org/~kragen/sw/dev3/kmregion_example.c
I simulated yours vs the UPB arena fast path:
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.
Thank you very much! I vaguely remember that it did that, and the failure to keep the pointer in registers might explain why PyPy's version is twice as fast (?).
I don't know much about language internals or allocation but am learning. why this could be significantly faster than a bump/arena allocator?
And is the speed up over malloc/free due to large block allocation as opposed to individual malloc?
It is a bump allocator. I don't know why it's so much faster than mine, but my hypothesis was that CF Bolz was testing on a faster machine. The speedup over malloc/free is because bumping a pointer is much faster than calling a subroutine.
The reason why their allocator is faster than Boehm isn't because of conservative stack scanning.
You can move objects while using conservative stack scanning. This is a common approach. JavaScriptCore used to use it.
You can have super fast allocation in a non-moving collector, but that involves an algorithm that is vastly different from the Boehm one. I think the fastest non-moving collectors have similar allocation fast paths to the fastest moving collectors. JavaScriptCore has a fast non-moving allocator called bump'n'pop. In Fil-C, I use a different approach that I call SIMD turbosweep. There's also the Immix approach. And there are many others.
> Every allocation takes 110116790943 / 10000000000 ≈ 11 instructions and 21074240395 / 10000000000 ≈ 2.1 cycles
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.
A few years ago I ran some benchmarks on an old but vaguely reasonable work load. I came up with a p95 or just 25nanoseconds but p99.9 on the order of tens of microseconds. https://www.forrestthewoods.com/blog/benchmarking-malloc-wit...
Of course “2% of time in GC” is doing a lot of heavy lifting here. But I’d really need to see a real work load for me to start to believe.
You should believe it, because it's true.
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.
Sorry, I refuse to accept a trivial loop of generating up to 2 objects at a time as ever being a relevant benchmark. I didn’t say throughput wasn’t relevant. I said this test isn’t convincing.
> coming to the conclusion that GC was somewhat faster than malloc/free
Many people have made that conclusion. Certainly not true for real-time cases where tail latency is critical.
Modern GC certainly better than old ones.
For malloc/free specific workload is much more important than for a bump allocator; malloc potentially has branching on size, different cache access/utilization patterns, dependency on distance/pattern between mallocs & frees, whereas a bump allocator is just bumping a single pointer, practically without any branching, and very simple linear cache utilization.