# Binary GCD algorithm Visualisation of using the binary GCD algorithm to find the greatest common divisor (GCD) of 36 and 24. Thus, the GCD is 22 × 3 = 12.

The binary GCD algorithm, also known as Stein's algorithm or the binary Euclidean algorithm, is an algorithm that computes the greatest common divisor of two nonnegative integers. Stein's algorithm uses simpler arithmetic operations than the conventional Euclidean algorithm; it replaces division with arithmetic shifts, comparisons, and subtraction.

Although the algorithm in its contemporary form was first published by the Israeli physicist and programmer Josef Stein in 1967, it may have been known by the 2nd century BCE, in ancient China.

## Algorithm

The algorithm reduces the problem of finding the GCD of two nonnegative numbers v and u by repeatedly applying these identities:

• gcd(0, v) = v, because everything divides zero, and v is the largest number that divides v. Similarly, gcd(u, 0) = u.
• gcd(2u2v) = 2·gcd(u, v)
• gcd(2uv) = gcd(uv), if v is odd (2 is not a common divisor). Similarly, gcd(u2v) = gcd(uv) if u is odd.
• gcd(uv) = gcd(|u − v|, min(u, v)), if u and v are both odd.

## Implementation

While the above description of the algorithm is mathematically-correct, performant software implementations typically differ from it in a few notable ways:

• eschewing trial division by 2 in favour of a single bitshift and the count trailing zeros primitive; this is functionally equivalent to repeatedly applying identity 3, but much faster;
• expressing the algorithm iteratively rather than recursively: the resulting implementation can be laid out to avoid repeated work, invoking identity 2 at the start and maintaining as invariant that both numbers are odd upon entering the loop, which only needs to implement identities 3 and 4;
• making the loop's body branch-free except for its exit condition (v == 0): in the example below, the exchange of u and v (ensuring u ≤ v) compiles down to conditional moves; hard-to-predict branches can have a large, negative impact on performance.

Following is an implementation of the algorithm in Rust exemplifying those differences, adapted from uutils:

```pub fn gcd(mut u: u64, mut v: u64) -> u64 {
use std::cmp::min;
use std::mem::swap;

// Base cases: gcd(n, 0) = gcd(0, n) = n
if u == 0 {
return v;
} else if v == 0 {
return u;
}

// Using identities 2 and 3:
// gcd(2ⁱ u, 2ʲ v) = 2ᵏ gcd(u, v) with u, v odd and k = min(i, j)
// 2ᵏ is the greatest power of two that divides both u and v
let i = u.trailing_zeros();  u >>= i;
let j = v.trailing_zeros();  v >>= j;
let k = min(i, j);

loop {
/// u and v are odd at the start of the loop
debug_assert!(u % 2 == 1, "u = {} is even", u);
debug_assert!(v % 2 == 1, "v = {} is even", v);

// Swap if necessary so u <= v
if u > v {
swap(&mut u, &mut v);
}
/// u and v are still both odd after (potentially) swapping

// Using identity 4 (gcd(u, v) = gcd(|v-u|, min(u, v))
v -= u;
/// v is now even, but u is unchanged (and odd)

// Identity 1: gcd(u, 0) = u
// The shift by k is necessary to add back the 2ᵏ factor that was removed before the loop
if v == 0 {
return u << k;
}

// Identity 3: gcd(u, 2ʲ v) = gcd(u, v) (u is known to be odd)
v >>= v.trailing_zeros();
/// v is now odd again
}
}
```

## Efficiency

The algorithm requires O(n) steps, where n is the number of bits in the larger of the two numbers, as every 2 steps reduce at least one of the operands by at least a factor of 2. Each step involves only a few arithmetic operations (O(1) with a small constant); when working with word-sized numbers, each arithmetic operation translates to a single machine operation, so the number of machine operations is on the order of log₂(max(u, v)), i.e. n.

However, the asymptotic complexity of this algorithm is O(n2), as those arithmetic operations (subtract and shift) each take linear time for arbitrarily-sized numbers (one machine operation per word of the representation). This is the same as for the Euclidean algorithm, though a more precise analysis by Akhavi and Vallée proved that binary GCD uses about 60% fewer bit operations.

## Extensions

The binary GCD algorithm can be extended in several ways, either to output additional information, deal with arbitrarily-large integers more efficiently, or to compute GCDs in domains other than the integers.

The extended binary GCD algorithm, analogous to the extended Euclidean algorithm, fits in the first kind of extension, as it provides the Bézout coefficients in addition to the GCD, i.e. integers a and b such that a·u + b·v = gcd(u, v).

In the case of large integers, the best asymptotic complexity is O(log n M(n)), with M(n) the cost of n-bit multiplication; this is near-linear, and vastly smaller than the O(n2) of the binary GCD algorithm, though concrete implementations only outperform older algorithms for numbers larger than about 64 kilobits (i.e. greater than 8×1019265). This is achieved by extending the binary GCD algorithm using ideas from the Schönhage–Strassen algorithm for fast integer multiplication.

The binary GCD algorithm has also been extended to domains other than natural numbers, such as Gaussian integers, Eisenstein integers, quadratic rings, integer rings of number fields, and arbitrary unique factorisation domains.[citation needed]

## Historical description

An algorithm for computing the GCD of two numbers was known in ancient China, under the Han dynasty, as a method to reduce fractions:

If possible halve it; otherwise, take the denominator and the numerator, subtract the lesser from the greater, and do that alternately to make them the same. Reduce by the same number.

— Fangtian – Land surveying, The Nine Chapters on the Mathematical Art

The phrase "if possible halve it" is ambiguous,

• if this applies when either of the numbers become even, the algorithm is the binary GCD algorithm;
• if this only applies when both numbers are even, the algorithm is similar to the Euclidean algorithm.