ryu

This needs a 2011 on it, which will explain why it is missing the very recent Ryū algorithm, which I believe is the fastest algorithm at this point: https://dl.acm.org/doi/10.1145/3192366.3192369 (PLDI 2018 paper) https://github.com/ulfjack/ryu (C source code)

Actually, there's even more recent research into doing this really efficiently, using an algorithm called Ryū. [e] https://github.com/ulfjack/ryu [-4] https://www.youtube.com/watch?v=kw-U6smcLzk [1.73] https://www.youtube.com/watch?v=4P_kbF0EbZM

Performance in the opposite direction is also useful; the Ryū algorithm[0] is a fast float-to-string conversion that I've used recently to great effect[1]. Namely, to convert path information from glyphs into vector graphics for a minimal math-oriented Java-based TeX implementation[2]. The result is code that converts 1,000 simple formulas as strings into individual SVG documents in under 600 milliseconds on newish hardware. [0]: https://github.com/ulfjack/ryu/blob/master/README.md [1]: https://news.ycombinator.com/item?id=17633182 [2]: https://github.com/DaveJarvis/JMathTeX/blob/master/README.md

Ryu algorithm by Ulf Adams (https://github.com/ulfjack/ryu) known to fastest in much cases, especially for short string representations. However, I would like to draw attention to my own speed-competing but more compact GRISU-based implementation. Briefly, about this implementation: 1. it is a GRUSU-based, but not exactly the GRISU2; 2. for now produces only a raw ASCII representation, e.g. -22250738585072014e-324 without dot and '\0' at the end; 3. compared to Ryu, it significantly less code size and spends less clock cycles per digit, but is slightly inferior in a whole because generates a longer ASCII representation. Now I would like to get feedback, assess how much this is in demand and collect suggestions for further improvements. For instance, I think that it is reasonable to implement conversion with a specified precision (i.e., wi