Unfortunately performance claims appear to be bogus.

1. ospan, performance claims seem to be based on, doesn't do any bound checks, so you can easily get buffer overflow.

2. fast_io generates a whopping 50kB of static data just to format an integer.

So if these benchmark results are correct (I was not able to verify because the author hasn't provided the benchmark source):

> format_int 7867424 ns 7866027 ns 89 items_per_second=127.129M/s

> fast_io_ospan_res 6871917 ns 6870708 ns 102 items_per_second=145.545M/s

fast_io gives 15% perf improvement by replacing a safe format_int API from https://github.com/fmtlib/fmt with a similar but unsafe one + 50kB of extra data. Adding safety will likely bring perf down which the last line seems to confirm:

> fast_io_concat 7967591 ns 7966162 ns 88 items_per_second=125.531M/s

This shows that fast_io is slightly slower than the equivalent {fmt} code. Again this is from the fast_io's benchmark results that I hasn't been able to reproduce.

50kB may not seem like much but for comparison, after a recent binary size optimization, the whole {fmt} library is around 57kB when compiled with `-Os -flto`: http://www.zverovich.net/2020/05/21/reducing-library-size.ht...

The floating-point benchmark results are even less meaningful. They appear to be based on a benchmark that I wrote to test the worst case Grisu (https://www.cs.tufts.edu/~nr/cs257/archive/florian-loitsch/p...) performance on unrealistic random data with maximum digit count. fast_io compares it to Ryu (https://dl.acm.org/doi/pdf/10.1145/3192366.3192369) where maximum digit count is actually the best case and the performance degrades as the number of digits goes down. A meaningful thing to do would be to use Milo Yip's benchmark instead: https://github.com/miloyip/dtoa-benchmark