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Revisiting The Quick Inverse Sq. Root – Is It Nonetheless Helpful?

Revisiting The Quick Inverse Sq. Root – Is It Nonetheless Helpful?

2023-04-20 16:05:17

April 20, 2023

In 2005, id Software program launched the supply code for his or her 1999 sport Quake III
below the GPL-2 license. Within the file code/game/q_math.c,
there’s a operate for calculating the reciprocal sq. root of a quantity
which at first look appears to make use of a really peculiar algorithm:

float Q_rsqrt( float quantity )
    lengthy i;
    float x2, y;
    const float threehalfs = 1.5F;

    x2 = quantity * 0.5F;
    y  = quantity;
    i  = * ( lengthy * ) &y;                       // evil floating level bit degree 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 may be eliminated

    return y;

Many articles have been written about this specific algorithm and it has its
personal nicely written Wikipedia page the place it’s known as the
quick inverse sq. root. The algorithm truly appeared on varied boards
earlier than the Q3 supply code was launched. Ryszard of Beyond3D did some
investigating in 2004-2005 and eventually tracked down
the unique writer of the algorithm to be Greg Walsh at Ardent Pc who
created it greater than a decade earlier.

How does it work?

So how does the tactic work, anyway? It’s carried out in two steps:

  1. acquire a tough approximation y for the reciprocal sq. root of our

    y  = quantity;
    i  = * ( lengthy * ) &y;
    i  = 0x5f3759df - ( i >> 1 );
    y  = * ( float * ) &i;
  2. enhance the approximation utilizing a single step of the Newton-Raphson (NR) methodology:
    const float threehalfs = 1.5F;
    x2 = quantity * 0.5F;
    y  = y * ( threehalfs - ( x2 * y * y ) );

First approximation

Essentially the most attention-grabbing half is the primary one. It makes use of a seemingly magic quantity
0x5f3759df and a few bit shifting and in some way finally ends up with the reciprocal
sq. root. The primary line shops the 32-bit floating-point quantity y as a
32-bit integer i by taking a pointer to y, changing it to a lengthy
pointer and dereferencing it. So y and i maintain two similar 32-bit vectors,
however one is interpreted as a floating-point quantity and the opposite is interpreted
as an integer quantity. Then, the integer quantity is shifted one step to the
proper, negated, and the fixed 0x5f3759df is added. Lastly, the ensuing
worth is interpreted as a floating quantity once more by dereferencing a float
pointer that factors to the integer i worth.

Right here, shifting, negation and addition is carried out within the integer area, how
do these operations have an effect on the quantity within the floating-point area? So as to
perceive how this may yield an approximation of the reciprocal sq. root we
should be accustomed to how floating level numbers are represented in reminiscence. A
floating-point quantity consists of an indication s{0,1} $s in {0,1}$, exponent eZ $ein mathbb{Z}$ and a fractional half 0f1 $0leq{f}. The worth of the
floating-point quantity is then

y=(1)s·(1+f)·2e. $y = (-1)^s cdot (1 + f) cdot 2^e. $

In our case, we will assume that our float is within the IEEE 754 binary32
format, the bits are then ordered as proven beneath.

Essentially the most vital bit is the signal bit S $S$, adopted by 8 bits (E $E$)
representing the exponent e $e$ and the remaining 23 bits (F $F$) representing
the fractional half f $f$. The quantity is unfavourable when S=1 $S=1$. The 8-bit
quantity E $E$ isn’t instantly used because the exponent, it has an offset or bias of
281=127 $2^8-1 = 127$. So E=0 $E=0$ signifies that the exponent is e=127 $e=-127$. F $F$ is
merely a fractional binary quantity with the decimal level earlier than the primary digit
such that f=F·223 $f=Fcdot2^{-23}$.

We are able to write a easy C program interp.c to print each the integer and
floating-point interpretations of a given quantity and likewise extract the completely different

#embody <stdlib.h>
#embody <stdio.h>
#embody <string.h>
#embody <stdint.h>

int primary(int argc, char *args[]) {
    /* parse quantity from args */
    uint32_t i;
    int ret;
    if (argc == 2) {
        ret = sscanf(args[1], "%u", &i);
    } else if (argc == 3 && strcmp(args[1], "-h") == 0) {
        ret = sscanf(args[2], "%x", &i);
    } else if (argc == 3 && strcmp(args[1], "-f") == 0) {
        float y;
        ret = sscanf(args[2], "%f", &y);
        i = *(uint32_t*)&y;
    } else {
        return EXIT_FAILURE;
    if (ret != 1) return EXIT_FAILURE;

    /* print representations */
    printf("hexadecimal: %xn", i);
    printf("unsigned int: %un", i);
    printf("signed int: %dn", i);
    printf("floating-point: %fn", *(float*)&i);

    /* print elements */
    int S = i >> 31;
    int E = (i >> 23) & ((1 << 8)-1);
    int e = E - 127;
    int F = i & ((1 << 23)-1);
    float f = (float)F / (1 << 23);
    printf("S: %dn", S);
    printf("E: %d (0x%x) <=> e: %dn", E, E, e);
    printf("F: %d (0x%x) <=> f: %fn", F, F, f);

    return EXIT_SUCCESS;

We are able to for instance take a look at the quantity 0x40b00000:

$ ./interp -h 40b00000
hexadecimal: 40b00000
unsigned int: 1085276160
signed int: 1085276160
floating-point: 5.500000
S: 0
E: 129 (0x81) <=> e: 2
F: 3145728 (0x300000) <=> f: 0.375000

We are able to additionally extract the components of a floating-point quantity:

$ ./interp -f -32.1
hexadecimal: c2006666
unsigned int: 3254806118
signed int: -1040161178
floating-point: -32.099998
S: 1
E: 132 (0x84) <=> e: 5
F: 26214 (0x6666) <=> f: 0.003125

Even now once we understand how the floating-point numbers are represented in reminiscence,
it’s not fully apparent how performing operations within the integer area
would have an effect on the floating-point area. At first we will attempt to merely iterate
over a variety of floating-point quantity and see what integer values we get:

#embody <stdio.h>

int primary() {
    float x;
    for (x = 0.1; x <= 8.0; x += 0.1) {
        printf("%ftpercentdn", x, *(int*)&x);

We are able to then plot the floating-point values on the x-axis and the integer values
on the y-axis with e.g. gnuplot to get a plot like this:

Effectively, this curve appears to be like fairly acquainted. We are able to look additional at a few of the knowledge
factors utilizing our earlier program:

$ ./interp -f 1.0
hexadecimal: 3f800000
unsigned int: 1065353216
signed int: 1065353216
floating-point: 1.000000
S: 0
E: 127 (0x7f) <=> e: 0
F: 0 (0x0) <=> f: 0.000000

$ ./interp -f 2.0
hexadecimal: 40000000
unsigned int: 1073741824
signed int: 1073741824
floating-point: 2.000000
S: 0
E: 128 (0x80) <=> e: 1
F: 0 (0x0) <=> f: 0.000000

$ ./interp -f 3.0
hexadecimal: 40400000
unsigned int: 1077936128
signed int: 1077936128
floating-point: 3.000000
S: 0
E: 128 (0x80) <=> e: 1
F: 4194304 (0x400000) <=> f: 0.500000

For 1.0 and a pair of.0 we get S=0 $S=0$, F=0 $F=0$ and a non-zero biased exponent E $E$.
If we take away the bias from this quantity (subtract by 127 << 23) after which shift
it to the far proper we find yourself with the exponent e $e$, in different phrases the bottom
2 logarithm of the floating-point quantity. Nevertheless, this solely works when S=0 $S=0$
and F=0 $F=0$, i.e. constructive integers. If S=1 $S=1$ we’ve got a unfavourable quantity for
which the logarithm is undefined. But when F0 $Fne{}0$ and we shift the exponent
to the far proper we’ll merely lose all of that knowledge. We are able to as a substitute convert
it to a floating-point worth and divide by 223 $2^{23}$, such that the fractional
half scales our ensuing worth linearly:

(float) (*(int*)&x - (127 << 23)) / (1 << 23)

Then we don’t precisely get the logarithm however we do get a linear approximation
for all non energy of two values. We are able to plot the approximation along with
the precise logarithmic operate:

Which means once we take a floating-point quantity and interpret it as an
integer quantity, we acquire an approximation of the logarithm of that quantity,
with some offset and scaling. And once we interpret an integer quantity as a
floating-point quantity, we get reverse, i.e. the exponential or antilogarithm
of our integer worth. This mainly signifies that once we carry out operations in
the integer area, it’s as if we carry out operations within the logarithmic
area. For instance, if we keep in mind our logarithmic identities, we all know that
if we take the logarithm of two numbers and add them collectively, we get the
logarithm of their product:

loga+logb=log(a·b). $log{a} + log{b} = log{(a cdot b)}. $

In different phrases, if we carry out addition within the integer area we get
multiplication within the floating-point area — roughly anyway. We are able to
do this with one other easy C program. One factor we have to take into account is how
our operation impacts the exponent bias. Once we add two numbers with biased
exponents we get double bias:

E1+E2=(e1+B)+(e2+B)=e1+e2+2B. $start{align} E_1 + E_2 &=& (e_1 + B) + (e_2 + B) &=& e_1 + e_2 + 2B. finish{align} $

We wish our bias to stay as B $B$ quite than 2B $2B$ so to be able to counter
this we merely subtract the consequence by B $B$. Our C program that performs
floating-point multiplication utilizing integer addition might then seem like this:

#embody <stdio.h>
#embody <stdlib.h>
#embody <stdint.h>

const uint32_t B = (127 << 23);

int primary(int argc, char *args[]) {
    /* parse components from args */
    float a, b;
    if (argc == 3) {
        int ret = sscanf(args[1], "%f", &a);
        ret += sscanf(args[2], "%f", &b);
        if (ret != 2) return EXIT_FAILURE;
    } else {
        return EXIT_FAILURE;

    /* carry out multiplication (integer addition) */
    uint32_t sum = *(uint32_t*)&a + *(uint32_t*)&b - B;
    float y = *(float*)&sum;

    /* evaluate with precise */
    float y_actual = a*b;
    float rel_err = (y - y_actual) / y_actual;

    printf("%f =? %f (%.2f%%)n", y, y_actual, 100*rel_err);

Let’s strive it out:

$ ./mul 3.14159 8.0
25.132721 =? 25.132721 (0.00%)

$ ./mul 3.14159 0.2389047
0.741016 =? 0.750541 (-1.27%)

$ ./mul -15.0 3.0
-44.000000 =? -45.000000 (-2.22%)

$ ./mul 6.0 3.0
16.000000 =? 18.000000 (-11.11%)

$ ./mul 0.0 10.0
0.000000 =? 0.000000 (inf%)

More often than not it’s not completely correct, it’s appropriate provided that one of many
components is an influence of two, and least correct when each components are proper
between two powers of two.

How in regards to the reciprocal sq. root? The reciprocal sq. root
1x $frac{1}{sqrt{x}}$ is equal to x1/2 $x^{-1/2}$ so we’ll want one other
logarithmic id:

plogx=logxp $plog{x} = log{x^p} $

Which means if we carry out multiplication within the integer area, we get
exponentiation within the floating-point area. Relying on our exponent p $p$ we
can acquire a number of completely different features, e.g:

p $p$ f(x) $f(x)$
2 x2 $x^2$
1/2 x $sqrt{x}$
-1 1x $frac{1}{x}$
-1/2 1x $frac{1}{sqrt{x}}$

So as to get a primary approximation of the reciprocal sq. root, we merely
have to multiply by -1/2 within the integer area and alter for the bias. The
bias will then be B/2 $-B/2$ and we wish the bias to be B $B$ so we merely want
so as to add 3B/2=0x5f400000 $3B/2 = texttt{0x5f400000}$. So, we’ll multiply by -1/2 by shifting
proper one step and negating, after which add the bias:

- (i << 1) + 0x5f400000;

That is now similar to the Q3 supply code besides that the fixed worth
differs barely. They used 0x5f3759df whereas we at the moment have 0x5f400000.
We are able to see whether it is potential to make enhancements by taking a look at our error. We
merely subtract our approximate worth for the reciprocal sq. root by the
precise worth and plot the consequence for a sure vary of numbers:

The graph repeats horizontally in each instructions (solely in numerous scale) so
we solely want to take a look at this half to grasp the error for all (regular)
floating-point numbers. We are able to see that the approximate worth is all the time
overestimating, by merely subtracting a continuing that’s round half the
most error we will make it symmetric and thus lower the common absolute
error. Trying on the graph, subtracting one thing like 0x7a120 would possibly work. Our
fixed would then be 0x5f385ee0 which is nearer to the fixed utilized in Q3.
Within the integer area, our error will merely heart the error across the x-axis
within the above diagram. Within the floating-point area, the error is affected
equally besides when our subtraction borrows from the exponent:

We might probably attempt to discover an precise optimum for some affordable
objective function however we’ll cease right here. Within the case of the unique Q3
fixed, it’s not actually clear the way it was chosen, maybe utilizing trial and

Enhancing the approximation

The second half is much less unconventional. When a primary approximation has been
obtained, one can enhance it through the use of a way often called Newton-Raphson (NR). If
you’re unfamiliar with it, Wikipedia has a great article on it. The
NR methodology is used to enhance an approximation for the foundation of an equation.
Since we wish the reciprocal sq. root we want an equation f(y) $f(y)$ that’s
zero when y $y$ is precisely the reciprocal sq. root of x $x$:

y=1x1y2=xf(y)=1y2x=0 $start{align} y = frac{1}{sqrt{x}} : Leftrightarrow : frac{1}{y^2} = x Rightarrow f(y) = frac{1}{y^2} – x = 0 finish{align} $

If we’ve got an approximate worth yn $y_n$ we will get a greater approximation
yn+1 $y_{n+1}$ by calculating the place the tangent of the operate’s graph at
y=yn $y=y_n$ (i.e. the spinoff) intersects f(y)=0 $f(y)=0$. That worth may be
expressed as

yn+1=ynf(yn)f(yn)=yn(32x2·yn2) $start{align} y_{n+1} &=& y_n – frac{f(y_n)}{f'(y_n)} &=& y_n left( frac{3}{2} – frac{x}{2} cdot {y_n}^2 proper) finish{align} $

which is the very same expression that’s used within the second a part of the Q3

How briskly is it?

Again in 2003 Chris Lomont wrote an article about his
investigations of the algorithm. His testing yielded that the algorithm was
4 instances quicker than utilizing the extra simple manner of merely utilizing
sqrt(x) from the usual library and taking its reciprocal.

In 2009, Elan Ruskin made a submit, Timing Square Root, the place he
primarily appeared on the sq. root operate but additionally in contrast the quick inverse
sq. root algorithm to different strategies. On his Intel Core 2, the quick inverse
sq. root was 4 instances slower than utilizing rsqrtss, or 30% slower than
rsqrtss with a single NR step.

Since then, there has come a number of new extensions to the x86 instruction set. I
have tried to sum up all sq. root directions at the moment accessible:

Set x $sqrt{x}$ 1x $frac{1}{sqrt{x}}$ Width
x87 (1980) fsqrt 32
3DNow! (1998) pfrsqrt 128
SSE (1999) sqrtps, sqrtss rsqrtps, rsqrtss 128
SSE2 (2000) sqrtpd, sqrtsd 128
AVX (2011) vsqrtps, vsqrtpd, vsqrtps_nr, vrsqrtps, vrsqrtps_nr 256
AVX-512 (2014) vrsqrt14pd, vrsqrt14ps, vrsqrt14sd, vrsqrt14ss 512

The fsqrt is kind of out of date by now. The 3DNow! extension has additionally been
deprecated and is not supported. All x86-64 processors help at the least
SSE and SSE2. Most processors help AVX and a few help AVX-512, however e.g.
GCC at the moment chooses to not emit any AVX directions by default.

The p and s is brief for “packed” and “scalar”. The packed directions are
vector SIMD directions whereas the scalar ones solely function on a single worth
at a time. With a register width of e.g. 256 bits, the packed instruction can
carry out 256/32=8 $256/32=8$ calculations in parallel. The s or d is brief for
“single” or “double” precision floating-point. Since we’re contemplating
approximations we will likely be utilizing single precision floating-point numbers. We might
then use both the ps or ss variants.

The quick inverse sq. root methodology had a fairly laborious time in opposition to the
rsqrtss instruction again in 2009 already. And since then, a number of extensions
with specialised SIMD directions has been applied in trendy x86
processors. Certainly, the quick inverse sq. root has no probability at the moment and its
time has handed?

Why don’t we give it a strive ourselves proper now, we will begin by operating some
assessments on my present machine which has a comparatively trendy processor: an AMD Zen
3 5950X from late 2020.

Preliminary testing

We are going to write a C program that tries to calculate the reciprocal sq. root
utilizing three completely different strategies:

  • precise: merely 1.0 / sqrtf(x), utilizing the sqrtf operate from the C
    customary library,
  • appr: first approximation from the Q3 supply as defined above,
  • appr_nr: the total Q3 methodology with one iteration of Newton-Raphson.

For every methodology we carry out the calculation for every worth in a randomized enter
array and time how lengthy it takes in complete. We are able to use the clock_gettime
operate from libc (for POSIX methods) to get the time earlier than and after we
carry out the calculations and calculate the distinction. We are going to then repeat this
many instances to lower the random variations. The C program appears to be like like this:

#embody <stdlib.h>
#embody <stdio.h>
#embody <stdint.h>
#embody <time.h>
#embody <math.h>

#outline N 4096
#outline T 1000
#outline E9 1000000000


enum strategies { EXACT, APPR, APPR_NR, M };
const char *METHODS[] = { "precise", "appr", "appr_nr" };

static inline float rsqrt_exact(float x) { return 1.0f / sqrtf(x); }
static inline float rsqrt_appr(float x) {
    uint32_t i = *(uint32_t*)&x;
    i = -(i >> 1) + 0x5f3759df;
    return *(float*)&i;
static inline float rsqrt_nr(float x, float y) { return y * (1.5f - x*0.5f*y*y); }

static inline float rsqrt_appr_nr(float x) {
    float y = rsqrt_appr(x);
    return rsqrt_nr(x, y);

int primary() {

    float y_sum[M] = {0};
    double t[M] = {0};

    for (int trial = 0; trial < T; trial++) {
        struct timespec begin, cease;
        float x[N], y[N];
        for (int i = 0; i < N; i++) { x[i] = rand(); }

        clock_gettime(CLOCK_REALTIME, &begin);
        for (int i = 0; i < N; i++) { y[i] = rsqrt_exact(x[i]); }
        clock_gettime(CLOCK_REALTIME, &cease);
        for (int i = 0; i < N; i++) { y_sum[EXACT] += y[i]; }
        t[EXACT] += ((cease.tv_sec-start.tv_sec)*E9 + cease.tv_nsec-start.tv_nsec);

        clock_gettime(CLOCK_REALTIME, &begin);
        for (int i = 0; i < N; i++) { y[i] = rsqrt_appr(x[i]); }
        clock_gettime(CLOCK_REALTIME, &cease);
        for (int i = 0; i < N; i++) { y_sum[APPR] += y[i]; }
        t[APPR] += ((cease.tv_sec-start.tv_sec)*E9 + cease.tv_nsec-start.tv_nsec);

        clock_gettime(CLOCK_REALTIME, &begin);
        for (int i = 0; i < N; i++) { y[i] = rsqrt_appr_nr(x[i]); }
        clock_gettime(CLOCK_REALTIME, &cease);
        for (int i = 0; i < N; i++) { y_sum[APPR_NR] += y[i]; }
        t[APPR_NR] += ((cease.tv_sec-start.tv_sec)*E9 + cease.tv_nsec-start.tv_nsec);

    for (int m = 0; m < M; m++) {
               t[m] * 1000.0f / N / T,
               (double) t[EXACT] / t[m],
               (y_sum[m] - y_sum[EXACT]) / y_sum[EXACT]);

    return 0;

On the finish of this system we print three issues for every methodology:

  • the common time to calculate a single operation in femtoseconds – the decrease
    the higher,
  • the ratio of the calculation time in comparison with the precise methodology – the upper
    the quicker,
  • the common error between the tactic and the precise methodology – simply to make
    positive the calculations are carried out appropriately.

So, what will we count on? There are devoted features for calculating the
reciprocal sq. root within the x86 instruction set that the compiler ought to be
in a position to emit. The throughput might then be higther than within the approximate methodology
the place we carry out a number of operations.

Let’s go forward and take a look at it, we’ll compile it utilizing GCC with none optimizations
at first, explicitly with -O0. Since we’re utilizing math.h for the precise
methodology we will even have to hyperlink the maths library utilizing -lm:

$ gcc -lm -O0 rsqrt.c
$ ./a.out
rsqrt	fs/op	ratio	err
precise	3330	1.00	0.0000
appr	2020	1.65	0.0193
appr_nr	6115	0.54	-0.0010

This appears affordable. The error is noticeable for the primary approximation however
diminished after one iteration of NR. The primary approximation is definitely quicker
than the precise methodology however when performed along with a step of NR it’s twice as
gradual. The NR methodology requires extra operations so this appears affordable.

Alright, however that is solely a debug construct, let’s strive including optimizations utilizing
the -O3 flag. It will allow all optimizations that don’t disregard any
requirements compliance.

$ gcc -lm -O3 rsqrt.c
$ ./a.out
rsqrt	fs/op	ratio	err
precise	1879	1.00	0.0000
appr	72	26.01	0.0193
appr_nr	178	10.54	-0.0010

Hmm, now the approximations are literally lots quicker than earlier than however the time
of the precise methodology has solely halved, making the approximation with NR greater than
ten instances quicker than the precise methodology. Maybe the compiler didn’t emit the
reciprocal sq. root features? Perhaps it should enhance if we use the -Ofast
flag as a substitute which is described by the gcc(1) man web page:

Disregard strict requirements compliance. -Ofast allows all -O3 optimizations.
It additionally allows optimizations that aren’t legitimate for all standard- compliant
packages. It activates -ffast-math, -fallow-store-data-races and the
Fortran-specific -fstack-arrays, except -fmax-stack-var-size is specified,
and -fno-protect-parens. It turns off -fsemantic-interposition.

Our precise methodology might not be as correct as earlier than, however it might be quicker.

$ gcc -lm -Ofast rsqrt.c
$ ./a.out
rsqrt	fs/op	ratio	err
precise	153	1.00	0.0000
appr	118	1.30	0.0137
appr_nr	179	0.85	-0.0009

And it’s certainly quicker. The primary approximation remains to be quicker, however with a
step of NR it’s slower than the precise methodology. The error has decreased barely
for the approximations as a result of we’re nonetheless evaluating in opposition to the “precise”
methodology which now yields completely different outcomes. Oddly sufficient, the primary
approximation has develop into half as quick. This appears to be a quirk of GCC, as
Clang doesn’t have this difficulty, in any other case it produces related outcomes:

$ clang -lm -O0 rsqrt.c
$ ./a.out
rsqrt	fs/op	ratio	err
precise	3715	1.00	0.0000
appr	1933	1.92	0.0193
appr_nr	6001	0.62	-0.0010

$ clang -lm -O3 rsqrt.c
$ ./a.out
rsqrt	fs/op	ratio	err
precise	1900	1.00	0.0000
appr	61	31.26	0.0193
appr_nr	143	13.24	-0.0010

$ clang -lm -Ofast rsqrt.c
$ ./a.out
rsqrt	fs/op	ratio	err
precise	148	1.00	0.0000
appr	62	2.40	0.0144
appr_nr	145	1.02	-0.0009


For each compilers, there’s fairly a big distinction between -O3 and
-Ofast. We are able to take a look at the disassembly to see what’s going on. We are going to want
to supply the -g flag to the compiler to be able to get debug symbols within the
binary that inform us which object code corresponds to which supply code.
Thereafter we will run objdump -d with the -S flag to see the disassembled
directions subsequent to the supply code:

$ gcc -lm -O3 -g rsqrt.c
$ objdump -d -S a.out
static inline float rsqrt_exact(float x) { return 1.0f / sqrtf(x); }
    118e:       66 0f ef db             pxor   %xmm3,%xmm3
    1192:       0f 2e d8                ucomiss %xmm0,%xmm3
    1195:       0f 87 e1 02 00 00       ja     147c <primary+0x3bc>
    119b:       f3 0f 51 c0             sqrtss %xmm0,%xmm0
    119f:       f3 0f 10 0d 99 0e 00    movss  0xe99(%rip),%xmm1    # 2040
    11a6:       00
    11ab:       f3 0f 5e c8             divss  %xmm0,%xmm1
    2040:       00 00 80 3f             1.0f

In case you’re unfamiliar, that is the AT&T syntax for x86-64 meeting. Notice
that the supply operand is all the time earlier than the vacation spot operand. The
parentheses point out an handle, for instance movss 0xecd(%rip),%xmm1 copies
the worth positioned 0xecd bytes forward of the handle within the rip register
(instruction pointer, a.okay.a. PC) to the xmm1 register. The xmmN registers
are 128 bits huge, or 4 phrases. Nevertheless, the ss directions are for scalar
single-precision values, so it should solely apply the operation on a single
floating-point worth within the least vital 32 bits.

Within the -O3 case we use the scalar sqrtss adopted by divss. There may be additionally
a evaluate ucomiss and a leap ja that can set errno to EDOM in case
the enter is lower than -0. We’re not utilizing errno in any respect so we will take away the
setting of errno by offering the -fno-math-errno flag:

See Also

$ gcc -lm -O3 -g -fno-math-errno rsqrt.c
$ ./a.out
rsqrt	fs/op	ratio	err
precise	479	1.00	0.0000
appr	116	4.13	0.0193
appr_nr	175	2.74	-0.0010
$ objdump -d -S a.out
static inline float rsqrt_exact(float x) { return 1.0f / sqrtf(x); }
    1170:       0f 51 0c 28             sqrtps (%rax,%rbp,1),%xmm1
    1174:       f3 0f 10 1d c4 0e 00    movss  0xec4(%rip),%xmm3    # 2040
    117b:       00
    117c:       48 83 c0 10             add    $0x10,%rax
    1180:       0f c6 db 00             shufps $0x0,%xmm3,%xmm3
    1184:       0f 28 c3                movaps %xmm3,%xmm0
    1187:       0f 5e c1                divps  %xmm1,%xmm0
    2040:       00 00 80 3f             1.0f

This prevents us from having to examine each enter worth individually and thus
permits us to make use of the packed variants of the directions, performing 4
operations at a time. This improved the efficiency lots. Nevertheless, we nonetheless
use sqrtps adopted by divps. We should additionally
allow -funsafe-math-optimizations and -ffinite-math-only in
order to make GCC emit rsqrtps as a substitute. We then get similar code
to once we used -Ofast:

$ gcc -lm -O3 -g -fno-math-errno -funsafe-math-optimizations -ffinite-math-only rsqrt.c
$ ./a.out
rsqrt	fs/op	ratio	err
precise	155	1.00	0.0000
appr	120	1.29	0.0137
appr_nr	182	0.85	-0.0009
$ objdump -d -S a.out
static inline float rsqrt_exact(float x) { return 1.0f / sqrtf(x); }
    1170:       0f 52 0c 28             rsqrtps (%rax,%rbp,1),%xmm1
    1174:       0f 28 04 28             movaps (%rax,%rbp,1),%xmm0
    1178:       48 83 c0 10             add    $0x10,%rax
    117c:       0f 59 c1                mulps  %xmm1,%xmm0
    117f:       0f 59 c1                mulps  %xmm1,%xmm0
    1182:       0f 59 0d c7 0e 00 00    mulps  0xec7(%rip),%xmm1    # 2050
    1189:       0f 58 05 b0 0e 00 00    addps  0xeb0(%rip),%xmm0    # 2040
    1190:       0f 59 c1                mulps  %xmm1,%xmm0
    2040:       00 00 40 c0             -3.0f
    2050:       00 00 00 bf             -0.5f

Now it makes use of rsqrtps, nevertheless it additionally has a number of multiplication directions as
nicely as an addition. Why are these wanted, isn’t the reciprocal sq. root all
we want? We are able to get a touch from trying on the disassembly of the appr_nr

static inline float rsqrt_nr(float x, float y) { return y * (1.5f - x*0.5f*y*y); }
    12f8:       f3 0f 10 1d 80 0d 00    movss  0xd80(%rip),%xmm3    # 2080
    12ff:       00
    1304:       0f 59 05 65 0d 00 00    mulps  0xd65(%rip),%xmm0    # 2070
    1310:       0f c6 db 00             shufps $0x0,%xmm3,%xmm3
    1318:       0f 28 d1                movaps %xmm1,%xmm2
    131b:       0f 59 d1                mulps  %xmm1,%xmm2
    131e:       0f 59 d0                mulps  %xmm0,%xmm2
    1321:       0f 28 c3                movaps %xmm3,%xmm0
    1324:       0f 5c c2                subps  %xmm2,%xmm0
    1327:       0f 59 c1                mulps  %xmm1,%xmm0
    2070:       00 00 00 3f             0.5f
    2080:       00 00 c0 3f             1.5f

The final half appears to be like fairly related, as a result of it’s truly doing the identical factor:
an iteration of Newton-Raphson. That is hinted within the man web page of

This feature allows use of the reciprocal estimate and reciprocal sq. root
estimate directions with extra Newton-Raphson steps to extend
precision as a substitute of doing a divide or sq. root and divide for
floating-point arguments.

The rsqrtps instruction solely ensures a relative error smaller than
1.5·212 $1.5cdot2^{-12}$, the NR iteration reduces it additional similar to within the Q3

If we don’t want this additional precision, can we get a speedup by skipping the NR
step? We are able to use built-in compiler intrinsics to be able to make the compiler
solely emit the rsqrtps instruction. The GCC guide has a
list of built-in features for the x86 instruction set.
There’s a __builtin_ia32_rsqrtps operate that can emit the rsqrtps

v4sf __builtin_ia32_rsqrtps (v4sf);

The guide additionally has a chapter about use these vector
directions with built-in features. We have to add a typedef for the v4sf
sort which incorporates 4 floating level numbers. We are going to then use an array of
N/4 $N/4$ of those vectors and easily present one vector at a time to the
built-in operate. N is a a number of of 4 so there aren’t any half full vectors.
We are able to merely forged our earlier float enter array to a vfs4 pointer. We
will add these components to our earlier program:

typedef float v4sf __attribute__ ((vector_size(16)));
v4sf rsqrt_intr(v4sf x) { return __builtin_ia32_rsqrtps(x); };

        v4sf *xv = (v4sf*)x, *yv = (v4sf*)y;
        clock_gettime(CLOCK_REALTIME, &begin);
        for (int i = 0; i < N/4; i++) { yv[i] = rsqrt_intr(xv[i]); }
        clock_gettime(CLOCK_REALTIME, &cease);
        for (int i = 0; i < N; i++) { y_sum[INTR] += y[i]; }
        t[INTR] += ((cease.tv_sec-start.tv_sec)*E9 + cease.tv_nsec-start.tv_nsec);

We are able to compile it to be able to run and disassemble it:

$ gcc -lm -O3 -g rsqrt_vec.c
$ ./a.out
rsqrt	fs/op	ratio	err
precise	1895	1.00	0.0000
appr	72	26.39	0.0193
appr_nr	175	10.81	-0.0010
rsqrtps	61	31.00	0.0000
$ objdump -d -S a.out
v4sf rsqrt_intr(v4sf x) { return __builtin_ia32_rsqrtps(x); };
    1238:       41 0f 52 04 04          rsqrtps (%r12,%rax,1),%xmm0

Now we’re all the way down to a single instruction and it’s barely quicker than earlier than.

There are additionally extensions that not all processors help that we will attempt to
use. We are able to inform the compiler to make use of any extensions which are accessible on our
processor utilizing -march=native. This may occasionally make the binary incompatible with
different processors, although.

$ gcc -lm -Ofast -g -march=native rsqrt_vec.c
$ ./a.out
rsqrt	fs/op	ratio	err
precise	78	1.00	0.0000
appr	40	1.96	0.0137
appr_nr	85	0.91	-0.0009
rsqrtps	62	1.25	0.0000

Now we’re all the way down to nearly nearly as good as the primary approximation. The intrinsic one
is just about simply as quick. The “precise” methodology obtained changed by a 256-bit
vrsqrtps and a step of NR:

static inline float rsqrt_exact(float x) { return 1.0f / sqrtf(x); }
    11d0:       c5 fc 52 0c 18          vrsqrtps (%rax,%rbx,1),%ymm1
    11d5:       c5 f4 59 04 18          vmulps (%rax,%rbx,1),%ymm1,%ymm0
    11da:       48 83 c0 20             add    $0x20,%rax
    11de:       c4 e2 75 a8 05 79 0e    vfmadd213ps 0xe79(%rip),%ymm1,%ymm0
    11e5:       00 00
    11e7:       c5 f4 59 0d 91 0e 00    vmulps 0xe91(%rip),%ymm1,%ymm1
    11ee:       00
    11ef:       c5 fc 59 c1             vmulps %ymm1,%ymm0,%ymm0

The __builtin_ia32_rsqrtps is now utilizing a single vrsqrtps and no NR step,
nonetheless, it nonetheless makes use of solely 128-bit registers.

Broad sweep

So, we did some testing on my machine and obtained some perception into what sort of
directions we will use to calculate the reciprocal sq. root and the way they
would possibly carry out. We are going to now attempt to run these benchmarks on a number of machines to
give us an concept how nicely our findings apply normally. These machines embody
all those that I occur to have handy SSH entry to. All ensuing knowledge
may be downloaded from here, it additionally contains outcomes for the
inverse and sq. root features, individually.

Beneath is an inventory of the x86 machines that had been examined together with their CPUs and
their launch date. All of the earlier assessments had been run on on the pc labeled
as “igelkott”.

Hostname CPU Household CPU Mannequin Yr Type issue
jackalope Core Intel Celeron 550 2007 i686 laptop computer
narwhal Piledriver AMD FX-6300 2012 x86_64 desktop
silverback Ivy Bridge Intel Xeon E5-1410 2014 x86_64 server
bovinae Kaby Lake Intel Core i5-8250U 2017 x86_64 laptop computer
igelkott Zen 3 AMD Ryzen 5950X 2020 x86_64 desktop
deck Zen 2 AMD APU 0405 2022 x86_64 cell

Beneath is a plot of the efficiency ratio in comparison with the precise methodology, i.e.
the time of every methodology divided by the point of the precise methodology. A better
ratio means increased efficiency, something beneath 1 is slower than precise and
something above is quicker. We use the -Ofast flag right here, as it’s the quickest
choice that can be utilized with out sacrificing portability.

The outcomes are fairly related throughout all the machines, the time of the
strategies are roughly ranked within the order rsqrtps <= appr < precise <=
appr_nr. Utilizing the appr_nr methodology is both slower or the identical because the
precise methodology, so it has no actual profit on this case.

The “jackalope” machine was not included within the above plot as a result of it had an
extraordinarily gradual precise methodology. Particularly when not utilizing -march=native because the
compiler then resorted to utilizing the vintage fsqrt instruction.

Beneath is a desk of the particular timings when utilizing -Ofast, numbers in
parenthesis makes use of -march=native. Every quantity is how lengthy a single operation
takes in femtoseconds.

Machine/Compiler precise appr appr_nr rsqrtps
jackalope-clang 53634 (5363) 1500 (2733) 4971 (3996) N/A
narwhal-gcc 419 (363) 443 (418) 601 (343) 396 (231)
narwhal-clang 389 (796) 340 (321) 445 (859) 349 (388)
silverback-gcc 422 (294) 179 (199) 543 (543) 178 (189)
bovinae-gcc 260 (127) 155 (81) 321 (119) 108 (105)
bovinae-clang 255 (132) 108 (78) 272 (112) 95 (96)
igelkott-gcc 141 (79) 111 (63) 168 (87) 58 (64)
igelkott-clang 152 (76) 63 (40) 149 (70) 61 (62)
deck-gcc 342 (160) 234 (114) 444 (172) 226 (120)
deck-clang 297 (166) 189 (123) 332 (140) 101 (126)

The sq. root operate yields barely completely different outcomes:

Oddly sufficient, the sqrtps built-in operate is slower than the precise methodology,
and the appr with out NR is now quicker as a substitute. The appr_nr methodology nonetheless
gives no benefit, it’s as a substitute constantly worse than precise.

Listed here are the unique timings for the sq. root operate as nicely, with
-Ofast. Once more, numbers in parentheses use -march=native:

Machine/Compiler precise appr appr_nr sqrtps
jackalope-clang 35197 (5743) 1494 (2738) 19191 (4308) N/A
narwhal-gcc 505 (399) 399 (427) 659 (559) 796 (785)
narwhal-clang 448 (823) 327 (319) 638 (847) 803 (780)
silverback-gcc 625 (297) 271 (190) 958 (728) 1163 (1135)
bovinae-gcc 301 (148) 155 (81) 408 (200) 225 (226)
bovinae-clang 315 (244) 92 (60) 399 (159) 317 (227)
igelkott-gcc 173 (95) 119 (38) 233 (124) 288 (296)
igelkott-clang 168 (143) 63 (48) 234 (104) 170 (283)
deck-gcc 419 (205) 215 (108) 519 (252) 575 (574)
deck-clang 325 (244) 153 (88) 372 (180) 315 (458)

Strive it your self

You may attempt to run the benchmarks in your machine and see should you get related
outcomes. There’s a shell script bench/ that can generate and run
benchmarks utilizing the bench/bench.c.m4 file. These information may be present in this
blog’s repo
. Merely run the script with no arguments and it’ll generate a
.tsv file with all outcomes:

$ cd bench
$ sh
$ grep rsqrt bench.tsv | kind -nk3 | head
rsqrt	appr	40	1.91	0.0139	clang-Ofast-march=native
rsqrt	rsqrtps	56	32.08	0.0000	clang-O3
rsqrt	appr	58	31.08	0.0193	clang-O3
rsqrt	rsqrtps	58	2.48	0.0000	clang-O3-fno-math-errno-funsafe-math-optimizations-ffinite-math-only
rsqrt	rsqrtps	59	2.45	0.0000	gcc-Ofast
rsqrt	rsqrtps	59	2.48	0.0000	clang-Ofast
rsqrt	rsqrtps	59	31.07	0.0000	gcc-O3
rsqrt	rsqrtps	59	7.83	0.0000	gcc-O3-fno-math-errno
rsqrt	appr	60	2.41	0.0144	clang-O3-fno-math-errno-funsafe-math-optimizations-ffinite-math-only
rsqrt	rsqrtps	60	8.09	0.0000	clang-O3-fno-math-errno

Closing ideas

To summarize, utilizing merely 1/sqrtf(x) on trendy x86 processors may be each
quicker and extra correct than the quick inverse sq. root methodology from Quake
III’s Q_rsqrt operate.

Nevertheless, a key takeaway is that it’s important to order the compiler to make it
quicker. When merely compiling utilizing -O3, the quick inverse sq. root methodology
is definitely significantly quicker than the naive implementation. Now we have to
enable the compiler to violate some strict specification necessities so as
to make it emit a quicker implementation (primarily to permit vectorization).

Equally may be stated for the atypical sq. root operate as nicely, simply utilizing
sqrtf(x) and altering compiler flags enable for a really quick implementation.

If very low accuracy may be tolerated, it’s potential to get a barely quicker
implementation by skipping the Newton-Raphson step from the quick inverse sq.
root methodology. Curiously, the compiler additionally performs an NR step after utilizing
approximate implementations of the inverse sq. root. This may also be made
barely quicker by skipping the NR step — by solely emitting the approximate
instruction with the assistance of compiler intrinsics.

On this submit, we targeted on x86, however how about different directions
units? The quick inverse sq. root methodology might maybe nonetheless be
helpful for processors with out devoted sq. root directions.

How are the {hardware} implementations of approximate sq. roots sometimes
applied? May an approximate {hardware} implementation probably use
one thing just like the primary approximation of the quick inverse sq. root

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