rustymath

rustymath gives you fast versions of the elementary maths functions, such as exp, log, sin and pow, for 16-, 32- and 64-bit floats. You can call them from Rust, where the compiler builds them into your own loops, or from Python, where they work on whole NumPy arrays. Most also take complex numbers. It also has special functions such as gamma and erf, probability distributions, APL’s tolerant comparison, number theory and polynomials.

rustymath gives up the last bits of accuracy for speed. Every function is within 4 units in the last place (ulps) of the correctly rounded result. At 32 bits, most functions run two to six times as fast as the standard library’s versions.

Install

Install the Python package from PyPI. For Rust, see Using rustymath from Rust.

pip install rustymath

Using rustymath from Python

Each function is a NumPy ufunc. It takes arrays of any shape, broadcasts them as NumPy does, and gives a result of the same width. Complex arrays work wherever the function has complex values, and integer arrays wherever NumPy’s version takes them. add, multiply, fmax and fmin have reduce and accumulate methods that run rustymath’s vectorized sums, products, maximums and minimums. Import rustymath beside NumPy:

import numpy as np, rustymath as rm

Where NumPy has a function, rustymath uses the same name and arguments. To switch, you change only the import. Here exp keeps the 32-bit width of its argument:

x = np.linspace(0, 2, 5, dtype=np.float32)
rm.exp(x)

array([1.       , 1.6487211, 2.718282 , 4.4816885, 7.389056 ],
      dtype=float32)

To check the accuracy yourself, compare with NumPy. np.spacing gives the size of one ulp at each value. Dividing the difference by it gives the distance between the two results in ulps:

xs = np.linspace(-10, 10, 100_001)
y = np.exp(xs)
(np.abs(rm.exp(xs) - y) / np.spacing(y)).max()

np.float64(2.0)

Functions that NumPy lacks keep their own names. root(n, y) gives the nth root of y:

rm.root(3, np.array([8., 27., 1000.]))

array([ 2.,  3., 10.])

The Rust names follow Rust’s standard library instead. Python’s log, power and arcsin are Rust’s ln, pow and asin.

Tolerant comparison

APL compares floats within a relative tolerance. Two floats are equal when their difference is at most the tolerance times the larger magnitude. With the usual tolerance, 0.1 + 0.2 equals 0.3. rm.equal compares this way. Its tolerance ct defaults to rm.CT, 1e-14. Pass ct=0 to compare exactly:

rm.equal(0.1 + 0.2, 0.3), rm.equal(0.1 + 0.2, 0.3, ct=0)

(np.True_, np.False_)

floor, ceil, remainder, gcd and lcm take ct too. A value within tolerance of a whole number counts as that whole number:

rm.floor(np.array([2.9999999999999996, 2.5]))

array([3., 2.])

remainder takes its arguments in NumPy’s order. rm.remainder(x, y) is the Rust residue(y, x).

unique, index and isin search within the same tolerance. unique and isin take the arguments of numpy.unique and numpy.isin. index(x, y) gives where in x each item of y first matches, or the size of x where nothing does:

rm.unique([0.3, 1.0, 0.1 + 0.2]), rm.index([0.3, 1.0], 0.1 + 0.2)

(array([0.3, 1. ]), np.int64(0))

Using rustymath from Rust

Add rustymath to your Cargo.toml:

[dependencies]
rustymath = "0.1"

On x86-64, build for a recent CPU, with -C target-cpu=x86-64-v3 or -C target-cpu=native. You can set it once in .cargo/config.toml:

[target.'cfg(target_arch = "x86_64")']
rustflags = ["-C", "target-cpu=x86-64-v3"]

Rust’s default x86-64 target has no fused multiply-add and no vector instruction for floor or trunc. Without the setting, rustymath multiplies and adds separately. Some of its loops also run one item at a time.

Each function comes in two forms. The free function, such as exp, is generic over f32, f64 and half::f16. The compiler inlines it into the loop that calls it and vectorizes that loop:

use half::f16;
use rustymath::exp;

let e64 = exp(1.0f64); // 2.718281828459045
let e32 = exp(1.0f32); // 2.718282
let e16 = exp(f16::from_f32(1.0)); // 2.719

The function object, such as Exp or Pow, has a scalar form one and a slice form many. Both run code that rustymath compiles itself. Your build settings don’t change their speed. Each argument of many is one item, which every result pairs with, or a slice with one item for each result. many appends the results to a vector and returns whether every item succeeded. A NaN result from arguments that aren’t NaN counts as a failure. Objects also take complex items wherever the function has complex values, and some take integers, such as Gcd at i64.

use rustymath::{Arg, Binary, Pow};

let mut squares = Vec::new();
Pow.many(Arg::Many(&[1.5, 2.0, 3.0]), Arg::One(2.0), 3, &mut squares); // squares: [2.25, 4.0, 9.0]

Use the slice forms of Sin, Cos and Tan for arrays. They reduce each item by multiples of π with a few multiply-adds, then give the rare items beyond 2^23 at f64, or 2^20 at f32, an exact reduction in integer arithmetic. The free sin takes a branch for those items. That branch stops a loop that calls it from vectorizing.

Tolerant comparison

The tolerant module has APL’s tolerant comparison: x equals y when |x - y| is at most the tolerance times the larger of |x| and |y|. Its objects, such as Equal, Less, Floor, Residue and Gcd, each hold a Tolerance. With Tolerance::EXACT, a slice form runs a loop of plain IEEE comparisons, chosen once per call.

use rustymath::{tolerant::{Equal, Tolerance}, Arg, Binary};

let mut same = Vec::new();
Equal(Tolerance::DEFAULT).many(Arg::Many(&[0.3, 0.1 + 0.2, 0.31]), Arg::One(0.3), 3, &mut same); // same: [true, true, false]

Other modules

• number_theory: primality, factorisation, next and previous primes, Euler’s totient, and the enumeration and counting of primes, on a u64, a u128 or a BigUint. Each computes in the narrowest of those that holds the value. A function that can run for a long time also has a _with version, whose extra argument is a check that can stop it.
• polynomial: evaluation over a slice by Horner’s rule, roots from the eigenvalues of the companion matrix, building from roots, derivatives and least-squares fitting.
• reduce: sums, products, maximums and minimums over a slice, and their running versions. Float sums and products vectorize by combining the items in any order.
• distribution: sixteen distributions. The objects Pdf, Cdf and Quantile hold a distribution and evaluate it over a slice. Each distribution also samples from a seeded generator.
• random: rolls and deals that fill a slice from one generator.

The special functions gamma, lgamma, erf, erfc, their inverses, the incomplete gamma and beta functions and Lambert W have free functions and objects at the crate root, as the elementary functions do.

Accuracy and speed

The table gives nanoseconds per item for a loop over 65,536 inputs on an Apple M5 Max, in October 2026, beside the standard library’s version of each function. A row named after an object times its slice form. Pow.one and Root.one time the scalar forms. To print the table on your own machine, run RUSTYMATH_TIMES=1 cargo test --test speed -- --nocapture.

An f16 computes in f32 and rounds once. Special values, such as infinities, NaN and signed zeros, give IEEE’s results. The tests compare each function with a correctly rounded reference at every f16 input, at samples of f32 and f64 inputs, and at published hard cases.

Where the target has a fused multiply-add instruction, as on AArch64 and on x86-64 with FMA, the functions use it. Elsewhere, as in wasm, they multiply and add separately, because a call to a software fma would stop the loop vectorizing. FUSED says which the target uses. The modules fused and unfused hold every function again, with the choice made. A loop compiled with FMA for itself, such as an AVX2 version chosen when your program runs, calls fused. The 4-ulp bound holds either way.

At f64, pow and root compute ln |x| and its product with y as double words, through Arm Optimized Routines’ tables. The result stays within 4 ulps even where y ln |x| nears the overflow threshold. A power or root whose exact result is a float, such as 15^4 or ∛27, comes out exactly. Their scalar forms take one branch for special values, as Arm’s scalar pow does. Their slice forms compute every item the fast way, then redo only the rare items with special values. At f32 and f16, pow and root compute in f64 and round once. There the free pow and root have no branch. A loop that calls them vectorizes.

Licence

Apache-2.0. Some algorithms and constants come from SLEEF and from Arm Optimized Routines. THIRD-PARTY.md has their notices.