P1144 [[trivially_relocatable]] is one of those optimizations (like move semantics and copy elision) where you can make a benchmark show any improvement you want, because the whole point of the optimization is to eliminate arbitrarily complicated user code. So for example at C++Now 2018 I showed a 3x speedup on vector<unique_ptr<int>>::reserve ; but I could just as well have shown an Nx speedup on vector<deque<T>>::reserve , where the value of N depends purely on your choice of T — could be 2x, could be 20x, could be 200x. (libstdc++’s deque<T> is the pathological case: it’s trivially relocatable but not nothrow-move-constructible. That’s the key to understanding Marc Glisse’s recent libstdc++ patches.)

So when we talk about “benchmarks for [[trivially_relocatable]] ,” what we really mean is “how much does it improve the performance of real software,” not contrived benchmarks.

For a long time, I’ve had “build LLVM/Clang both ways and compare its performance” on my to-do list. But even that wouldn’t be much of a fair comparison, because LLVM/Clang already has a lot of smart people who’ve spent years making sure that LLVM/Clang is fast. They generally don’t use std::vector ; they don’t use std::string ; they don’t even use std::sort . So I actually wouldn’t expect Clang to get faster simply by using a P1144-enabled compiler and standard library. (Remember, the compiler changes don’t do anything by themselves; you need a library that can take advantage of the new information P1144 provides.)

But last night I did serendipitously run some completely unscientific benchmarks on my Homeworlds AI. Its bottleneck is in the move-generation code, where it keeps a giant std::unordered_set<std::string> representing (the serialized forms of) all the game states it’s explored so far.

We can compare the performance of some game-tree searches using unordered_set , using set , and using P1222 flat_set since I just implemented that.

Notice that “many dynamic insertions, no searches” is the absolute stupidest case for flat_set . You’d never use a flat_set like this in the real world.

I ran my existing game-tree search benchmarks as they’ve existed since February 2016; I did not modify them for this test. My compile lines looked like this:

make clean CXXFLAGS='-std=c++17 -O3 -DNDEBUG -D_LIBCPP_TRIVIALLY_RELOCATABLE=' \ CXX=../llvm/build/bin/clang++ \ make -j8 annotate ./run-benchmarks.sh make clean CXXFLAGS='-std=c++17 -O3 -DNDEBUG -DALLMOVES_USE_FLATSET=1' \ CXX=../llvm/build/bin/clang++ \ make -j8 annotate ./run-benchmarks.sh // etc.

run-benchmarks.sh is a shell script containing these three lines:

time for j in `seq 40`; do for i in `seq 40`; do echo ai_move; done | ./annotate --seed $j Sam Dave >/dev/null; done time for j in `seq 48`; do ./annotate --auto < benchmarks/perf-27635-moves.txt > /dev/null; done time for j in `seq 30`; do ./annotate --auto < benchmarks/perf-33332-moves.txt > /dev/null; done

Here are the results. First the results using -D_LIBCPP_TRIVIALLY_RELOCATABLE= , which effectively disables all P1144-related optimizations. Lower numbers are faster:

Data structure Bench 1 Bench 2 Bench 3 unordered_set 25.9 28.1 34.6 set 29.1 34.1 41.8 flat_set 37.6 51.9 64.0

And now without -D_LIBCPP_TRIVIALLY_RELOCATABLE= , so that P1144-related optimizations are enabled. Lower numbers are faster.

Data structure Bench 1 Bench 2 Bench 3 unordered_set 26.9 28.8 34.6 set 29.6 35.1 40.1 flat_set 35.5 48.8 59.9

We expect to see improvement anywhere that we use a swap -based algorithm (e.g. if we sort things); and anywhere we resize vectors of things (so, we expect the biggest improvement in the case that’s bottlenecked on flat_set ). We see basically what we expect, plus a good deal of noise in this very unscientific, population-size-of-1 experiment. (For example, the +1 second on Benchmark 1 for unordered_set is simply noise as far as I know. Running the same benchmark a second time produced 25.3.)

The flat_set numbers were so bad that I went and wrote a not-quite-philosophically-defensible optimization in vector::insert , where if we’re inserting just a single element in the middle of the vector, and trivial relocation is available, then we use memmove rather than move-assign-in-a-loop to do the bulk of the data-shoveling. (If we’re inserting more than one element, then I was too lazy to figure out what the math in libc++’s __move_range was doing, so I just punted on that part.)

With the extra vector::insert optimization, flat_set ’s performance (on this stupidly unrealistic insertion workload) increases quite a bit. Again, remember there’s tons of noise in here.

Data structure Bench 1 Bench 2 Bench 3 unordered_set 24.3 27.7 33.3 set 29.1 33.1 39.8 flat_set 33.2 41.1 50.3