# Calculating approximate logarithms

###### PUBLISHED ON MAY 6, 2015 — MISC
Logarithms are surprisingly useful, but can be quite expensive to calculate. In this post we explore an interesting method for approximating the value of base 2 logarithms extremely quickly.

There are quite a few instances in which one may wish to place values on a logarithmic scale. One such case was encountered during an FPGA class at university where we were asked to display the amplitude response of an FM radio frequency band. It turns out that there are some neat tricks which make calculating approximate logs very efficient in this kind of scenario.

Here I will outline an algorithm for calculating approximate base 2 logs quickly, focusing on an implementation for microprocessors. Code examples are in Ruby for ease of reading.

### Coarse grain

Calculating the integer base 2 log of a number is as simple as finding the position of its most-significant ‘1’ bit. This is a happy result of how the binary system of representing numbers works, and it shouldn’t require too much thought for you to convince yourself that this is indeed the case.

So, to calculate a coarse log, all we need to do is find the position of the highest set bit!

You might already be thinking of a few ways to do this:

# String conversion technique
number = 342
binary = number.to_s(2)
coarse_log = binary.length - 1

# Bit shifting technique
number = 342
coarse_log = 0
while number != 0
coarse_log += 1
number >>= 1
end
coarse_log -= 1


Definitely avoid using the first technique, and you probably shouldn’t use the second either. Why? These days many processors have an assembly instruction which can achieve the desired result much more efficiently.

For example, on modern CPUs you can use the BSR (bit scan reverse) instruction and call it a day:

bsr RAX, number;


### Improving precision

In the previous section we demonstrated that a coarse log can taken using a single assembly instruction. This might be enough for some applications, but many others will require a little more precision.

To achieve more precision in our results we will use n bits following the most significant ‘1’ as a key to a lookup table. The value from the lookup table will be added to our coarse log to get a more precise result. The larger the value of n the more precise the result, but be aware that the size of the lookup table grows exponentially (it will have 2n entries).

n = 5
key = (number & ~(1 << coarse_log)) >> (coarse_log - n)
refined_log = coarse_log + lookup_table[key]


Fantastic! But what is lookup_table?

The lookup table should be a constant, hard-coded array of length 2n which contains the decimal parts of logs. We can pre-calculate this table by taking 2n linear steps between two adjacent powers of two (in this case 1 and 2), taking exact logs (using an existing math library) and recording the effect it has on the decimal portion fo the number.

lookup_table = (0...2**n).map do |key|
k = 1 + (key / 2.0**n)
Math.log(k) / Math.log(2)
end


### Tying it all together

N = 5

# Can be precomputed and hard-coded
LOOKUP_TABLE = (0...2**N).map do |key|
k = 1 + (key / 2.0**N)
Math.log(k) / Math.log(2)
end

def fast_log(number)
# No inline assembly for Ruby, use bitshifts to find highest set bit
tmp = number
coarse_log = 0
while tmp != 0
coarse_log += 1
tmp >>= 1
end
coarse_log -= 1

# Improve precision
key = (number & ~(1 << coarse_log)) >> (coarse_log - N)
refined_log = coarse_log + LOOKUP_TABLE[key]

return refined_log
end


### Other improvements

The techniques described here can easily be extended to work with fixed-point arithmetic, which is a big improvement on hardware which does not have native support for floating-point numbers. The lookup table can also be tweaked in such a way that the value returned represents a mid-point, giving better results on average (this will cause logs which normally return integers to be slightly off, which may matter depending on your use case).

Finally, I discovered after writing this post that there’s an article about fast approximate logarithms on eBay Tech Blog. If you are using a CPU where floating point arithmetic is cheap, you may want to consider using a mathematical function similar to that described in the article (instead of a lookup table) for greater accuracy at the cost of speed.