When I published Grisu (Google double-conversion), it was multiple times faster than the existing algorithms. I knew that there was still room for improvement, but I was at most expecting a factor 2 or so. Six times faster is really impressive.
https://vitaut.net/posts/2025/smallest-dtoa/
And there’s one detail I found confusing. Suppose I go through the steps to find the rounding interval and determine that k=-3, so there is at most one integer multiple of 10^-3 in the interval (and at least one multiple of 10^-4). For the sake of argument, let’s say that -3 worked: m·10^-3 is in the interval.
Then, if m is not a multiple of 10, I believe that m·10^-3 is the right answer. But what if m is a multiple of 10? Then the result will be exactly equal, numerically, to the correct answer, but it will have trailing zeros. So maybe I get 7.460 instead of 7.46 (I made up this number and I have no idea whether any double exists gives this output.) Even though that 6 is definitely necessary (there is no numerically different value with decimal exponent greater than -3 that rounds correctly), I still want my formatter library to give me the shortest decimal representation of the result.
Is this impossible for some reason? Is there logic hiding in the write function to simplify the answer? Am I missing something?
Unlike formatting, correct parsing involves high precision arithmetic.
Example: the IEEE 754 double closest to the exact value "0.1" is 7205759403792794*2^-56, which has an exact value of A (see below). The next higher IEEE 754 double has an exact value of C (see below). Exactly halfway between these values is B=(A+C)/2.
A=0.1000000000000000055511151231257827021181583404541015625
B=0.100000000000000012490009027033011079765856266021728515625
C=0.10000000000000001942890293094023945741355419158935546875
0.1000000000000000124900090270330110797658562660217285156249
0.1000000000000000124900090270330110797658562660217285156251
Parsing a decimal ASCII string to a decimal value already optimizes well, because you can scale each digit by it's power of 10 in parallel and just add up the result.
https://old.reddit.com/r/rust/comments/omelz4/making_rust_fl...
Is it, though? It's genuinely hard for me to tell.
There's both serialization and deserialization of data sets with, e.g., JSON including floating point numbers, implying formatting and parsing, respectively.
Source code (including unit tests etc.) with hard-coded floating point values is compiled, linted, automatically formatted again and again, implying lots of parsing.
Code I usually work with ingests a lot of floating point numbers, but whatever is calculated is seldom displayed as formatted strings and more often gets plotted on graphs.
I wonder how Teju Jaguá compares. I don't see it in the C++ benchmark repo you linked and whose graph you included.
I have contributed an implementation in Rust :) https://crates.io/crates/teju it includes benchmarks which compare it vs Ryu and vs Rust's stdlib, and the readme shows a graph with some test cases. It's quite easy to run if you're interested!
> A more interesting improvement comes from a talk by Cassio Neri Fast Conversion From Floating Point Numbers. In Schubfach, we look at four candidate numbers. The first two, of which at most one is in the rounding interval, correspond to a larger decimal exponent. The other two, of which at least one is in the rounding interval, correspond to the smaller exponent. Cassio’s insight is that we can directly construct a single candidate from the upper bound in the first case.
Another nice thing about your post is mentioning the "shell" of the algorithm, that is, actually translating the decimal significand and exponent into a string (as opposed to the "core", turning f * 2^e into f' * 10^e'). A decent chunk of the overall time is spent there, so it's worth optimising it as well.
The bottleneck are the 3 conditionals: - positive or negative - positive or negative exponent, x > 10.0 - correction for 1.xxxxx * 2^Y => fract(log10(2^Y)) 1.xxxxxxxx > 10.0