Given two positive integers *x* and *y*, the greatest common divisor (GCD) *z* is the largest number that divides both *x* and *y*. For example, given 64 and 32, the greatest common divisor is 32.

There is a fast technique to compute the GCD called the binary GCD algorithm or Stein’s algorithm. According to Wikipedia, it is 60% faster than more common ways to compute the GCD.

I have honestly never written a program where computing the GCD was the bottleneck. However, Pigeon wrote a blog post where the binary GCD fared very poorly compared to a simple implementation of Euler’s algorithm with remainders:

unsigned gcd_recursive(unsigned a, unsigned b) { if (b) return gcd_recursive(b, a % b); else return a; }

Though Pigeon is a great hacker, I wanted to verify for myself. It seems important to know whether an algorithm that has its own wikipedia page is worth it. Unfortunately, the code on Wikipedia’s page implementing the binary GCD algorithm is either inefficient or slightly wrong. Here is a version using a GCC intrinsic function (`__builtin_ctz`) to find the *number of trailing zeros*:

unsigned int gcd(unsigned int u, unsigned int v) { int shift; if (u == 0) return v; if (v == 0) return u; shift = __builtin_ctz(u | v); u >>= __builtin_ctz(u); do { unsigned m; v >>= __builtin_ctz(v); v -= u; m = (int)v >> 31; u += v & m; v = (v + m) ^ m; } while (v != 0); return u << shift; }

My result? Using integers in [0,2000), the simple version Pigeon proposed does 25 millions GCDs per second, whereas my binary GCD does 39 millions GCDs per second, a difference of 55% on an Intel core i7 desktop. Why do my results disagree with Pigeon? His version of the binary GCD did not make use of the intrinsic `__builtin_ctz` and used an equivalent loop instead. When I implemented something similarly inefficient, I also got a slower result (17 millions GCDs per second) which corroborates Pigeon’s finding.

My benchmarking code is available.

On a 64-bit machine, you probably can adapt this technique using the `__builtin_ctzll` intrinsic.

**Update**: You can read more about sophisticated GCD algorithms in the gmplib manual.

**Conclusion**: The Binary GCD is indeed a faster way to compute the GCD for 32-bit integers, but only if you use the right instructions (e.g., `__builtin_ctz`). And someone ought to update the corresponding Wikipedia page.

What I don’t get is that you have a speedup only on numbers of the form m—2^k and n—2^j, and the speed-up is proportional to min(j,k). How do you explain doubling the speed if asymptotically few pairs of numbers are of that form?

euclyd, not euler.

Thanks for the post!

Yea, I heart Euler, but this is Euclid, all day.

Another excellent example of shaving off constants!

They’re not quadratic, they’re O(lg min(a,b)).

see:

http://en.wikipedia.org/wiki/Euclidean_algorithm#Algorithmic_efficiency

“And someone ought to update the corresponding Wikipedia page.”

Why don’t you do it yourself?

By the way, the numbers you used for testing are relatively small. More complicated algorithms are often slower for small numbers and don’t show their efficiency until the numbers are bigger. Without using anything bigger than uint32 you could test numbers of size ~1’000’000’000.

If you care about asymptotics, then both of these are quadratic. For a subquadratic algorithm, you need something like a half-gcd based algorithm.

@Pigeon

It is not necessary for the numbers to be divisible by two for them to benefit from the binary GCD.

Take 3 and 5. After the first pass in the loop you get 3 and 2. The 2 gets back to 1 due to the ctz shift.

The nice thing with the binary GCD is that it does not use any expensive operation (ctz is quite cheap on recent Intel processors) whereas the basic GCD relies on integer division.

They are quadratic when considering operations with integers that are larger than an unsigned int/long.

For example, see: https://gmplib.org/manual/Greatest-Common-Divisor-Algorithms.html and https://gmplib.org/manual/Binary-GCD.html

Hi Daniel, I can trim another 12% off your gcd() above by removing the two redundant shifts by “shift” of “u” and “v” that occur before the loop.

@Ralph

Well done. I have updated my blog post and credited you for the gains.

I wonder if you could save a cmpl by reusing the u > v comparison for the loop break as well. That is:

if( u == v)

break;

else if (u > v)

…

This will shorten the last iteration and probably speed up the speculative execution.

@KWillets

With clang, your version is faster. With GCC, the version in the blog post is faster. The difference is within 10%.

If I played with compiler flags, there might be other differences as well.

In any case, your version is on github if you want to benchmark it.

@Persson

I have added a test in my code with large numbers but it makes no difference. Of course, these are word-size integers… results would differ with big integers.

Hi again Daniel, I can save a further 7.5% on my earlier suggestion by altering the loop to

do {

unsigned m;

v >>= __builtin_ctz(v);

m = (v ^ u) & -(v < u);

u ^= m;

v ^= m;

v -= u;

} while (v);

I have re-run tests with a version using the built-ins. The speed-ups are there indeed: 40% on larger numbers.

http://hbfs.wordpress.com/2013/12/10/the-speed-of-gcd/

(and @Mike I think the state of the art for fast division is O(n^log_2(3)), which is still more than linear, but subquadratic.)

For my tweak the assembler output from gcc has the comparison, then a branch to the top of the loop, then the same comparison :(. The second comparison isn’t reachable by any other path either.

Maybe some syntactic shuffling would trigger the optimization; I may give it a few tries later.

This is faster on my version of gcc:

{

int shift, uz, vz;

uz = __builtin_ctz(u);

if ( u == 0) return v;

vz = __builtin_ctz(v);

if ( v == 0) return u;

shift = uz > vz ? vz : uz;

u >>= uz;

do {

v >>= vz;

if (u > v) {

unsigned int t = v;

v = u;

u = t;

}

v = v – u;

vz = __builtin_ctz(v);

} while( v != 0 );

return u << shift;

}

Results:

gcd between numbers in [1 and 2000]

26.4901 17.6991 32.7869 25.974 24.3902 31.746 36.6972

I was actually trying to get it to utilize the fact that ctz sets the == 0 flag when its argument is 0, so a following test against 0 should not need an extra instruction. However the compiler didn't notice. Instead it set up some interesting instruction interleaving so that the v != 0 test is actually u == v before the subtraction; I believe this is to enable ILP.

Also, using an inline xchg instruction for the swap doubles the speed:

gcd between numbers in [1 and 2000]

26.1438 16.3934 33.6134 25.974 25.4777 30.5344 72.7273

gcd between numbers in [1000000001 and 1000002000]

26.1438 16 33.8983 25.974 25.3165 29.6296 72.7273

@KWillets

Thanks. I have added your code to the benchmark.

Do you have the code for the version with the xchg instruction?

@Ralph

I added your version to the benchmark.

Here’s the asm for the swap; I just replaced the part inside the brackets with xswap(u,v):

#define xswap(a,b) __asm__ (\

“xchg %0, %1\n”\

: : “r”(a), “r” (b));

Unfortunately I don’t understand if this is correctly defined (I copied it from some poorly-documented examples), but the assembler output looks good.

@KWillets

I have checked into github a version with your inline assembly (slightly tweaked to be more standard). It is not faster.

When I ran your code “as is” I got failed tests.

https://github.com/lemire/Code-used-on-Daniel-Lemire-s-blog/blob/master/2013/12/26/gcd.cpp

Looking at Steven’s asm listings, I realized that my compiler was significantly behind, so I downloaded 3G of Apple “updates” last night. These results are now from clang-500.2.79.

I started playing around with various ways of getting abs(v-u) (especially when unsigned) and also realized that bsfl(x) == bsfl(-x), so this works for the inner loop on gcdwikipedia5fast:

do {

v >>= vz;

unsigned int diff =v;

diff -= u;

vz = __builtin_ctz(diff);

if( diff == 0 ) break;

if ( v < u ) {

u = v;

v = 0 – diff;

} else

v = diff;

} while( 1 );

If diff is signed 32-bit it's slightly faster, abs(diff) can be used, and the v < u test can be switched to diff < 0 for a slight gain. But it becomes a 31-bit algorithm. I haven't tried signed 64-bit yet.

Using bsfl(diff) instead of v seems to speed it up significantly; it's probably ILP again since it doesn't have to wait for v to finalize.

Hold on, I just tried signed 64-bit and got a huge boost:

do {

v >>= vz;

long long int diff = v ;

diff -= u;

vz = __builtin_ctz(diff);

if( diff == 0 ) break;

if ( diff < 0 )

u = v;

v = abs(diff);

} while( 1 );

@KWillets

I added these two alternatives to the benchmark.

I find that results vary a lot depending on the compiler and processor. It is hard to identify a clear winner… except that they are all faster than the Euclidean algorithm with remainder.

I checked the new revision and the 64-bit version (7) should use abs() and a few other edits.

Should I be submitting edits to github?

Thanks for the post!

It’s cool and it’ faster 3x than mod in my golang implement

It’s possible to slightly improve Ralph Corderoy’s branch-free code above (Dec. 28 comment) by using a difference delta rather than an xor delta.

If you don’t mind limiting the input range to INT_MAX, the sign of (int)(v-u) can be used to control the swap:

`v -= u;`

mask = (int)v >> 31;

u += v & mask; /* u + (v - u) = v */

v = (v + mask) ^ mask; /* Conditional negate ~(v - 1) = -v */

If you want to accept inputs up to UINT_MAX, it’s still possible to combine the subtract and mask formation with a bit of asm magic (x86 AT&T syntax) to get access to the carry flag:

`asm("sub %2,%1; sbb %0,%0" : "=r" (mask), "+r" (v) : "g" (u));`

Depending on the CPU, it may be worth spending an instruction to clear the mask to avoid a false dependency on its previous value. Add

, “0” (0)

to the end of the list of input parameters. (For those not familiar with GCC asm syntax, the 0 in quotes means that this input operand should be in the same register as output operand 0, the mask. The 0 in parens is the operand value. GCC will generate an

`xorl`

instruction to zero the mask.)Your proposal was added to the benchmark. Here are the numbers on my laptop:

I’ve just noticed your benchmarks don’t change the number range between runs — the offset is used only in the tests. With the benchmarks corrected, the mod approach shows faster on the larger value range.

Also noting the label on the results is misleading. The benchmark appears to be calculating ops per ms, not the timings directly, so larger is better.

Results on my TGL-H laptop after corrections:

gcd between numbers in [1 and 2000]

Running tests... Ok!

Kops/ms (larger values are better).

basicgcd 50

gcdwikipedia2 20.3046

gcdwikipedia2fast 44.4444

gcd_recursive 50

gcd_iterative_mod 48.1928

gcdFranke 46.5116

gcdwikipedia3fast 45.4545

gcdwikipedia4fast 61.5385

gcdwikipedia5fast 44.9438

gcdwikipedia2fastswap 43.0108

gcdwikipedia7fast 48.1928

gcdwikipedia7fast32 76.9231

gcdwikipedia8Spelvin 64.5161

`gcd between numbers in [1000000001 and 1000002000]`

Running tests... Ok!

Kops/ms (larger values are better).

basicgcd 37.7358

gcdwikipedia2 7.15564

gcdwikipedia2fast 16.4609

gcd_recursive 37.037

gcd_iterative_mod 37.7358

gcdFranke 16.5289

gcdwikipedia3fast 16.3934

gcdwikipedia4fast 22.3464

gcdwikipedia5fast 16.4609

gcdwikipedia2fastswap 16.3265

gcdwikipedia7fast 18.3486

gcdwikipedia7fast32 31.746

gcdwikipedia8Spelvin 23.3918

I’ve submitted a PR with the fixes

Thanks. Here are my results after merging your fix.