${\textstyle {\frac {1}{\sqrt {x}}}}$

${\displaystyle x}$

The algorithm accepts a 32-bit floating-point number as the input and stores a halved value for later use. Then, treating the bits representing the floating-point number as a 32-bit integer, a logical shift right by one bit is performed and the result subtracted from the number 0x5F3759DF, which is a floating-point representation of an approximation of ${\textstyle {\sqrt {2^{127}}}}$. This results in the first approximation of the inverse square root of the input. Treating the bits again as a floating-point number, it runs one iteration of Newton's method, yielding a more precise approximation.

William Kahan and K.C. Ng at Berkeley wrote an unpublished paper in May 1986 describing how to calculate the square root using bit-fiddling techniques followed by Newton iterations. In the late 1980s, Cleve Moler at Ardent Computer learned about this technique and passed it along to his coworker Greg Walsh. Greg Walsh devised the now-famous constant and fast inverse square root algorithm. Gary Tarolli was consulting for Kubota, the company funding Ardent at the time, and likely brought the algorithm to 3dfx Interactive circa 1994.

Jim Blinn demonstrated a simple approximation of the inverse square root in a 1997 column for IEEE Computer Graphics and Applications. Reverse engineering of other contemporary 3D video games uncovered a variation of the algorithm in Activision's 1997 Interstate '76.

Quake III Arena, a first-person shooter video game, was released in 1999 by id Software and used the algorithm. Brian Hook may have brought the algorithm from 3dfx to id Software. A discussion of the code appeared on the Chinese developer forum CSDN in 2000, and Usenet and the gamedev.net forum spread the code widely in 2002 and 2003. Speculation arose as to who wrote the algorithm and how the constant was derived; some guessed John Carmack. Quake III's full source code was released at QuakeCon 2005, but provided no answers. The authorship question was resolved in 2006 when Greg Walsh, the original author, contacted Beyond3D after their speculation gained popularity on Slashdot.

In 2007 the algorithm was implemented in some dedicated hardware vertex shaders using field-programmable gate arrays (FPGA).

The inverse square root of a floating point number is used in digital signal processing to normalize a vector, scaling it to length 1 to produce a unit vector. For example, computer graphics programs use inverse square roots to compute angles of incidence and reflection for lighting and shading. 3D graphics programs must perform millions of these calculations every second to simulate lighting. When the code was developed in the early 1990s, most floating point processing power lagged the speed of integer processing. This was troublesome for 3D graphics programs before the advent of specialized hardware to handle transform and lighting. Computation of square roots usually depends upon many division operations, which for floating point numbers are computationally expensive. The fast inverse square generates a good approximation with only one division step.

The length of the vector is determined by calculating its Euclidean norm: the square root of the sum of squares of the vector components. When each component of the vector is divided by that length, the new vector will be a unit vector pointing in the same direction. In a 3D graphics program, all vectors are in three-dimensional space, so ${\displaystyle {\boldsymbol {v}}}$ would be a vector ${\displaystyle (v_{1},v_{2},v_{3})}$.

${\displaystyle \|{\boldsymbol {v}}\|={\sqrt {v_{1}^{2}+v_{2}^{2}+v_{3}^{2}}}}$

is the Euclidean norm of the vector.

${\displaystyle {\boldsymbol {\hat {v}}}={\frac {\boldsymbol {v}}{\left\|{\boldsymbol {v}}\right\|}}}$

is the normalized (unit) vector. Substituting ${\textstyle \|{\boldsymbol {v}}\|}$, the equation can also be written as:

${\displaystyle {\boldsymbol {\hat {v}}}={\frac {\boldsymbol {v}}{\sqrt {v_{1}^{2}+v_{2}^{2}+v_{3}^{2}}}}}$

which relates the unit vector to the inverse square root of the distance components. The inverse square root can be used to compute ${\displaystyle {\boldsymbol {\hat {v}}}}$ because this equation is equivalent to

${\displaystyle {\boldsymbol {\hat {v}}}={\boldsymbol {v}}\,{\frac {1}{\sqrt {v_{1}^{2}+v_{2}^{2}+v_{3}^{2}}}}}$

where the fraction term is the inverse square root of ${\displaystyle v_{1}^{2}+v_{2}^{2}+v_{3}^{2}}$.

At the time, floating-point division was generally expensive compared to multiplication; the fast inverse square root algorithm bypassed the division step, giving it its performance advantage.

The following C code is the fast inverse square root implementation from Quake III Arena, stripped of C preprocessor directives, but including the exact original comment text:

float Q_rsqrt( float number )
{
	long i;
	float x2, y;
	const float threehalfs = 1.5F;

	x2 = number * 0.5F;
	y  = number;
	i  = * ( long * ) &y;						// evil floating point bit level hacking
	i  = 0x5f3759df - ( i >> 1 );               // what the fuck?
	y  = * ( float * ) &i;
	y  = y * ( threehalfs - ( x2 * y * y ) );   // 1st iteration
//	y  = y * ( threehalfs - ( x2 * y * y ) );   // 2nd iteration, this can be removed

	return y;
}

At the time, the general method to compute the inverse square root was to calculate an approximation for ${\textstyle {\frac {1}{\sqrt {x}}}}$, then revise that approximation via another method until it came within an acceptable error range of the actual result. Common software methods in the early 1990s drew approximations from a lookup table. The key of the fast inverse square root was to directly compute an approximation by utilizing the structure of floating-point numbers, proving faster than table lookups. The algorithm was approximately four times faster than computing the square root with another method and calculating the reciprocal via floating-point division. The algorithm was designed with the IEEE 754-1985 32-bit floating-point specification in mind, but investigation from Chris Lomont showed that it could be implemented in other floating-point specifications.

The advantages in speed offered by the fast inverse square root trick came from treating the 32-bit floating-point word as an integer, then subtracting it from a "magic" constant, 0x5F3759DF. This integer subtraction and bit shift results in a bit pattern which, when re-defined as a floating-point number, is a rough approximation for the inverse square root of the number. One iteration of Newton's method is performed to gain some accuracy, and the code is finished. The algorithm generates reasonably accurate results using a unique first approximation for Newton's method; however, it is much slower and less accurate than using the SSE instruction rsqrtss on x86 processors also released in 1999.

As an example, the number ${\displaystyle x=0.15625}$ can be used to calculate ${\textstyle {\frac {1}{\sqrt {x}}}\approx 2.52982}$. The first steps of the algorithm are illustrated below:

0011_1110_0010_0000_0000_0000_0000_0000  Bit pattern of both x and i
0001_1111_0001_0000_0000_0000_0000_0000  Shift right one position: (i >> 1)
0101_1111_0011_0111_0101_1001_1101_1111  The magic number 0x5F3759DF
0100_0000_0010_0111_0101_1001_1101_1111  The result of 0x5F3759DF - (i >> 1)

Interpreting as IEEE 32-bit representation:

0_01111100_01000000000000000000000  1.25 × 2
0_00111110_00100000000000000000000  1.125 × 2
0_10111110_01101110101100111011111  1.432430... × 2
0_10000000_01001110101100111011111  1.307430... × 2

Reinterpreting this last bit pattern as a floating point number gives the approximation ${\displaystyle y=2.61486}$, which has an error of about 3.4%. After one iteration of Newton's method, the final result is ${\displaystyle y=2.52549}$, an error of only 0.17%.

According to the C standard, reinterpreting a floating point value as an integer by casting then dereferencing the pointer to it is not valid (undefined behavior). Another way would be to place the floating point value in an anonymous union containing an additional 32-bit unsigned integer member, and accesses to that integer provides a bit level view of the contents of the floating point value.

# include <stdint.h> // uint32_t

float Q_rsqrt(float number)
{
  union {
    float    f;
    uint32_t i;
  } conv = { .f = number };
  conv.i  = 0x5f3759df - (conv.i >> 1);
  conv.f *= 1.5F - (number * 0.5F * conv.f * conv.f);
  return conv.f;
}

For C++, however, type punning through a union is also undefined behavior. In modern C++, the recommended method for implementing this function's casts is through C++20's std::bit_cast. This also allows the function to work in constexpr context:

import std;

constexpr std::float32_t Q_rsqrt(std::float32_t number) noexcept
{
  const auto y = std::bit_cast<std::float32_t>(
    0x5f3759df - (std::bit_cast<std::uint32_t>(number) >> 1));
  return y * (1.5f32 - (number * 0.5f32 * y * y));
}

The algorithm computes ${\textstyle {\frac {1}{\sqrt {x}}}}$ by performing the following steps:

1. Alias the argument ${\displaystyle x}$ to an integer as a way to compute an approximation of the binary logarithm ${\textstyle \log _{2}(x)}$
2. Use this approximation to compute an approximation of ${\textstyle \log _{2}\left({\frac {1}{\sqrt {x}}}\right)=-{\frac {1}{2}}\log _{2}(x)}$
3. Alias back to a float, as a way to compute an approximation of the base-2 exponential
4. Refine the approximation using a single iteration of Newton's method.

Since this algorithm relies heavily on the bit-level representation of single-precision floating-point numbers, a short overview of this representation is provided here. To encode a non-zero real number ${\displaystyle x}$ as a single precision float, the first step is to write ${\displaystyle x}$ as a normalized binary number:

${\displaystyle {\begin{aligned}x&=\pm 1.b_{1}b_{2}b_{3}\ldots \times 2^{e_{x}}\end{aligned}}}$

where the exponent ${\textstyle e_{x}}$ is an integer, and ${\textstyle 1.b_{1}b_{2}b_{3}\ldots }$ is the binary representation of the significand. Since the single bit before the point in the significand is always 1, it does not need be stored. The equation can be rewritten as:

${\displaystyle {\begin{aligned}x&=\pm 2^{e_{x}}(1+m_{x})\end{aligned}}}$

where ${\textstyle m_{x}}$ means ${\textstyle 0.b_{1}b_{2}b_{3}\ldots }$, so ${\textstyle m_{x}\in [0,1)}$. From this form, three unsigned integers are computed:

• ${\textstyle S_{x}}$, the "sign bit", is ${\displaystyle 0}$ if ${\displaystyle x}$ is positive and ${\displaystyle 1}$ negative or zero (1 bit)
• ${\textstyle E_{x}=e_{x}+B}$ is the "biased exponent", where ${\displaystyle B=127}$ is the "exponent bias" (8 bits)
• ${\textstyle M_{x}=m_{x}\times L}$, where ${\textstyle L=2^{23}}$ (23 bits)

These fields are then packed, left to right, into a 32-bit container.

As an example, consider again the number ${\displaystyle x=0.15625=0.00101_{2}}$. Normalizing ${\displaystyle x}$ yields:

${\textstyle x=+2^{-3}(1+0.25)}$

and thus, the three unsigned integer fields are:

• ${\displaystyle S=0}$
• ${\displaystyle E=-3+127=124=0111\ 1100_{2}}$
• ${\displaystyle M=0.25\times 2^{23}=2\ 097\ 152=0010\ 0000\ 0000\ 0000\ 0000\ 0000_{2}}$

these fields are packed as shown in the figure below:

If ${\textstyle {\frac {1}{\sqrt {x}}}}$ were to be calculated without a computer or a calculator, a table of logarithms would be useful, together with the identity ${\textstyle \log _{b}\left({\frac {1}{\sqrt {x}}}\right)=\log _{b}\left(x^{-{\frac {1}{2}}}\right)=-{\frac {1}{2}}\log _{b}(x)}$, which is valid for every base ${\displaystyle b}$. The fast inverse square root is based on this identity, and on the fact that aliasing a float32 to an integer gives a rough approximation of its logarithm. Here is how:

If ${\displaystyle x}$ is a positive normal number:

${\displaystyle x=2^{e_{x}}(1+m_{x})}$

then

${\displaystyle \log _{2}(x)=e_{x}+\log _{2}(1+m_{x})}$

and since ${\displaystyle m_{x}\in [0,1)}$, the logarithm on the right-hand side can be approximated by

${\displaystyle \log _{2}(1+m_{x})\approx m_{x}+\sigma }$

where ${\displaystyle \sigma }$ is a free parameter used to tune the approximation. For example, ${\displaystyle \sigma =0}$ yields exact results at both ends of the interval, while ${\textstyle \sigma ={\frac {1}{2}}-{\frac {1+\ln(\ln(2))}{2\ln(2)}}\approx 0.0430357}$ yields the optimal approximation (the best in the sense of the uniform norm of the error). However, this value is not used by the algorithm as it does not take subsequent steps into account.

Thus there is the approximation

${\displaystyle \log _{2}(x)\approx e_{x}+m_{x}+\sigma .}$

Interpreting the floating-point bit-pattern of ${\displaystyle x}$ as an integer ${\displaystyle I_{x}}$ yields

${\displaystyle {\begin{aligned}I_{x}&=E_{x}L+M_{x}\\&=L(e_{x}+B+m_{x})\\&=L(e_{x}+m_{x}+\sigma +B-\sigma )\\&\approx L\log _{2}(x)+L(B-\sigma ).\end{aligned}}}$

It then appears that ${\displaystyle I_{x}}$ is a scaled and shifted piecewise-linear approximation of ${\displaystyle \log _{2}(x)}$, as illustrated in the figure on the right. In other words, ${\displaystyle \log _{2}(x)}$ is approximated by

${\displaystyle \log _{2}(x)\approx {\frac {I_{x}}{L}}-(B-\sigma ).}$

The calculation of ${\textstyle y={\frac {1}{\sqrt {x}}}}$ is based on the identity

${\displaystyle \log _{2}(y)=-{\tfrac {1}{2}}\log _{2}(x)}$

Using the approximation of the logarithm above, applied to both ${\displaystyle x}$ and ${\displaystyle y}$, the above equation gives:

${\displaystyle {\frac {I_{y}}{L}}-(B-\sigma )\approx -{\frac {1}{2}}\left({\frac {I_{x}}{L}}-(B-\sigma )\right)}$

Thus, an approximation of ${\displaystyle I_{y}}$ is:

${\displaystyle I_{y}\approx {\tfrac {3}{2}}L(B-\sigma )-{\tfrac {1}{2}}I_{x}}$

which is written in the code as

i  = 0x5f3759df - ( i >> 1 );

The first term above is the magic number

${\displaystyle {\tfrac {3}{2}}L(B-\sigma )={\mathtt {0x5F3759DF}}}$

from which it can be inferred that ${\displaystyle \sigma \approx 0.0450466}$. The second term, ${\displaystyle {\frac {1}{2}}I_{x}}$, is calculated by shifting the bits of ${\displaystyle I_{x}}$ one position to the right.

conv.i = 0x5F1FFFF9 - ( conv.i >> 1 );
conv.f *= 0.703952253f * ( 2.38924456f - x * conv.f * conv.f );
return conv.f;