# Javascript primes

I recently decided to revisit the prime detection code I wrote for the Gaussian prime spiral page. The original method I used was the Miller-Rabin technique as described in SICP, but it was recursive and could cause a stack overflow quite easily. It also suffered from a problem with how Javascript represents integers.

The Miller-Rabin method makes use of exponentials, which can be broken down into a series of repeated squares. In the worst case if you're wanting to find out if \(n\) is a prime, you'll perform \((n−1)^2\), which can be an issue with large numbers.

Javascript integers max out at \(2^{53}\), which means the largest number you can square and get the correct answer is less than \(2^{27}\). Which isn't a terribly large number (134,217,728).

An alternative, and rather naive, method is to just divide \(n\) by every number from \(2\) up to \(n−1\). This is pretty inefficient. You can improve it a bit by only checking the prime numbers up to \(\sqrt{n}\). This can be achieved quite simply using streams:

function isPrime(n) { function p(n) { return { v: n, n: function() { for (n+=2 ; !isPrime(n) ; n+=2); return p(n); } } } var pr=p(3); do { if (pr.v * pr.v > n) { return true; } if (n%pr.v===0) { return false; } } while (pr=pr.n()); }

This is pretty cool, you create a stream of prime numbers (starting from 3) and check against each one. The definition of the prime stream relies on the isPrime function too. The smart bit is that to check whether a number is prime the stream will always have been generated to a high enough number to divide by. For example, if you want to check \(5\), then the stream already has 3 in it, and \(3^2 > 5\), which means it passes.

This code benefits a lot from memoization. My final code ended up looking like:

var isPrime = _.memoize(function(n) { function p(n) { return { v: n, n: function() { for (n+=2 ; !isPrime(n) ; n+=2); return p(n); } } } n=Math.abs(n); if (n===2) { return true; } if (n < 2 || n%2 === 0) { return false; } var pr=p(3); do { if (pr.v * pr.v > n) { return true; } if (n%pr.v===0) { return false; } } while (pr=pr.n()); });

Ideally the stream should be memoized too.

Now, this should be pretty quick for low numbers, certainly faster than the Miller-Rabin method, but for larger numbers it should be slower. Using Chrome I found the cross-over point to be around five thousand, but there wasn’t really much in it. Oddly, when I tried it in Firefox the stream-based method ran around four times faster while the Miller-Rabin method ran at about the same speed as in Chrome. As far as I can tell this means that in Firefox the stream-based method is faster for any number less than \(2^{26}\), which is as high as the Miller-Rabin method can safely go anyway.

I don’t know why Firefox is so much faster than Chrome.