Curation curve introduction

In this document we derive the approximate integer square root function used by Scorum for the curation curve

MSB function

Let function msb(x) be defined as follows for x >= 1:

• (Definition 1a) msb(x) is the index of the most-significant 1 bit in the binary representation of x

The following definitions are equivalent to Definition (1a):

• (Definition 1b) msb(x) is the length of the binary representation of x as a string of bits, minus one
• (Definition 1c) msb(x) is the greatest integer such that 2 ^ msb(x) <= x
• (Definition 1d) msb(x) = floor(log_2(x))

Many CPU's (including Intel CPU's since the Intel 386) can compute the msb() function very quickly on machine-word-size integers with a special instruction directly implemented in hardware. In C++, the Boost library provides reasonably compiler-independent, hardware-independent access to this functionality with boost::multiprecision::detail::find_msb(x).

Approximate logarithms

According to Definition 1d, msb(x) is already a (somewhat crude) approximate base-2 logarithm. The bits below the most-significant bit provide the fractional part of the linear interpolation. The fractional part is called the mantissa and the integer part is called the exponent; effectively we have re-invented the floating-point representation.

Here are some Python functions to convert to/from these approximate logarithms:

def to_log(x, wordsize=32, ebits=5):
    if x <= 1:
        return x
    # mantissa_bits, mantissa_mask are independent of x
    mantissa_bits = wordsize - ebits
    mantissa_mask = (1 << mantissa_bits)-1
 
    msb = x.bit_length() - 1
    mantissa_shift = mantissa_bits - msb
    y = (msb << mantissa_bits) | ((x << mantissa_shift) & mantissa_mask)
    return y
 
def from_log(y, wordsize=32, ebits=5):
    if y <= 1:
        return y
    # mantissa_bits, leading_1, mantissa_mask are independent of x
    mantissa_bits = wordsize - ebits
    leading_1 = 1 << mantissa_bits
    mantissa_mask = leading_1 - 1
 
    msb = y >> mantissa_bits
    mantissa_shift = mantissa_bits - msb
    y = (leading_1 | (y & mantissa_mask)) >> mantissa_shift
    return y

Approximate square roots

To construct an approximate square root algorithm, start from the identity log(sqrt(x)) = log(x) / 2. We can easily obtain sqrt(x) ~ from_log(to_log(x) >> 1). We can proceed by manual inlining the inner function call:

def approx_sqrt_v0(x, wordsize=32, ebits=5):
    if x <= 1:
        return x
    # mantissa_bits, leading_1, mantissa_mask are independent of x
    mantissa_bits = wordsize - ebits
    leading_1 = 1 << mantissa_bits
    mantissa_mask = leading_1 - 1
 
    msb_x = x.bit_length() - 1
    mantissa_shift_x = mantissa_bits - msb_x
    to_log_x = (msb_x << mantissa_bits) | ((x << mantissa_shift_x) & mantissa_mask)
 
    z = to_log_x >> 1
 
    msb_z = z >> mantissa_bits
    mantissa_shift_z = mantissa_bits - msb_z
    result = (leading_1 | (z & mantissa_mask)) >> mantissa_shift_z
    return result

Optimized approximate square roots

First, consider the following simplifications:

• The exponent part of z, denoted here msb_z, is simply msb_x >> 1
• The MSB of the mantissa part of z is the low bit of msb_x
• The lower bits of the mantissa part of z are simply the bits of the mantissa part of x shifted once

The above simplifications enable a more fundamental improvement: We can compute the mantissa and exponent of z directly from x and msb_x. Therefore, packing the intermediate result into to_log_x and then immediately unpacking it, effectively becomes a no-op and can be omitted. This makes the wordsize and ebits variables fall out. Making choices for these parameters and allocating extra space at the top of the word for exponent bits becomes completely unnecessary!

One subtlety is that the two shift operators result in a net shift of mantissa bits. The are shifted left by mantissa_bits - msb_x and then shifted right by mantissa_bits - msb_z. The net shift is therefore a right-shift of msb_x - msb_z.

The final code looks like this:

def approx_sqrt_v1(x):
    if x <= 1:
        return x
    # mantissa_bits, leading_1, mantissa_mask are independent of x
    msb_x = x.bit_length() - 1
    msb_z = msb_x >> 1
    msb_x_bit = 1 << msb_x
    msb_z_bit = 1 << msb_z
    mantissa_mask = msb_x_bit-1
 
    mantissa_x = x & mantissa_mask
    if (msb_x & 1) != 0:
        mantissa_z_hi = msb_z_bit
    else:
        mantissa_z_hi = 0
    mantissa_z_lo = mantissa_x >> (msb_x - msb_z)
    mantissa_z = (mantissa_z_hi | mantissa_z_lo) >> 1
    result = msb_z_bit | mantissa_z
    return result