Much of the difference in optimizability is that C/C++ allows you to do fairly extreme copy elision. This is important because memory bandwidth tends to be a major performance bottleneck.
C/C++ code can trivially reach into some remote bit of state and mutate it directly. The compiler rarely objects, no matter how ugly the access method or pattern. It is up to the programmer to make sure the invariants hold and the compiler can’t always see through it. These elided copies add up, though it usually has a maintainability and safety cost.
It is related to why functional languages tend to be mediocre for supercomputing in practice despite having nice properties for massive parallelism in theory.
Are there any common resources for this kind of access methods and patterns that specifically aim to bridge the soft-/hardware gap? The best one I can come up with is Performance Analysis and Tuning on Modern CPUs[0] by Denis Bakhvalov.
This is kind of hacker news post and comments I like; extreme languages on extreme performance with extreme features, not types being added to a language ten gizzilion times slower than a tortoise and the post making anti-type flat-earthers getting freaked out if they have to vaccinate next.
This seems to raise the question: is libdeflate fast? My overall impression of libdeflate is that it produces a similar output size to zstd but takes 40 times longer to do it. I realize these are different algorithms of course, but they aren't that different, both being in the Huffman line of the LZ family. So why is libdeflate considered a performance benchmark?
It’s probably just because a zstd implementation in Lean would be more difficult. The whole project depends on being able to have a correctness proof for the implementation, and implementing that for the DEFLATE algorithm is simpler.
There are no well known programs made with Roc. It's been usable for writing real software since July or so for those of us willing to deal with compiler crashes and regular syntax/feature changes. To my knowledge there are six people producing significant amounts of software in Roc. I'm using it for a speech recognition/transcription app and the desktop environment and apps on my eink tablet, both on my own UI framework.
Well, it has not even reached 0.1.0, so the language is still in early development phase. But it has interesting properties, so I would recommend experimenting with it.
C/C++ code can trivially reach into some remote bit of state and mutate it directly. The compiler rarely objects, no matter how ugly the access method or pattern. It is up to the programmer to make sure the invariants hold and the compiler can’t always see through it. These elided copies add up, though it usually has a maintainability and safety cost.
It is related to why functional languages tend to be mediocre for supercomputing in practice despite having nice properties for massive parallelism in theory.
Are there any common resources for this kind of access methods and patterns that specifically aim to bridge the soft-/hardware gap? The best one I can come up with is Performance Analysis and Tuning on Modern CPUs[0] by Denis Bakhvalov.
[0]: https://github.com/dendibakh/perf-book