Count number of 1's in binary representation

Question

Efficient way to count number of 1s in the binary representation of a number in O(1) if you have enough memory to play with. This is an interview question I found on an onli

Answer 1

I came here having a great belief that I know beautiful solution for this problem. Code in C:

    short numberOfOnes(unsigned int d) {
        short count = 0;

        for (; (d != 0); d &= (d - 1))
            ++count;

        return count;
    }

But after I've taken a little research on this topic (read other answers:)) I found 5 more efficient algorithms. Love SO!

There is even a CPU instruction designed specifically for this task: popcnt. (mentioned in this answer)

Description and benchmarking of many algorithms you can find here.

Answer 2

The below method can count the number of 1s in negative numbers as well.

private static int countBits(int number)    {
    int result = 0;
    while(number != 0)  {
        result += number & 1;
        number = number >>> 1;
    }
    return result;
}

However, a number like -1 is represented in binary as 11111111111111111111111111111111 and so will require a lot of shifting. If you don't want to do so many shifts for small negative numbers, another way could be as follows:

private static int countBits(int number)    {
    boolean negFlag = false;
    if(number < 0)  { 
        negFlag = true;
        number = ~number;
    }

    int result = 0;
    while(number != 0)  {
        result += number & 1;
        number = number >> 1;
    }
    return negFlag? (32-result): result;
}

Answer 3

That will be the shortest answer in my SO life: lookup table.

Apparently, I need to explain a bit: "if you have enough memory to play with" means, we've got all the memory we need (nevermind technical possibility). Now, you don't need to store lookup table for more than a byte or two. While it'll technically be Ω(log(n)) rather than O(1), just reading a number you need is Ω(log(n)), so if that's a problem, then the answer is, impossible—which is even shorter.

Which of two answers they expect from you on an interview, no one knows.

There's yet another trick: while engineers can take a number and talk about Ω(log(n)), where n is the number, computer scientists will say that actually we're to measure running time as a function of a length of an input, so what engineers call Ω(log(n)) is actually Ω(k), where k is the number of bytes. Still, as I said before, just reading a number is Ω(k), so there's no way we can do better than that.