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Simplify and speed-up math.hypot() and math.dist() (GH-102734)
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@rhettinger @Fidget-Spinner
rhettinger authored and Fidget-Spinner committed Mar 27, 2023
1 parent b537248 commit 824309e
Showing 1 changed file with 139 additions and 154 deletions.
293 changes: 139 additions & 154 deletions Modules/mathmodule.c
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Original file line number Diff line number Diff line change
Expand Up @@ -92,6 +92,113 @@ get_math_module_state(PyObject *module)
return (math_module_state *)state;
}

/*
Double and triple length extended precision algorithms from:
Accurate Sum and Dot Product
by Takeshi Ogita, Siegfried M. Rump, and Shin’Ichi Oishi
https://doi.org/10.1137/030601818
https://www.tuhh.de/ti3/paper/rump/OgRuOi05.pdf
*/

typedef struct{ double hi; double lo; } DoubleLength;

static DoubleLength
dl_fast_sum(double a, double b)
{
/* Algorithm 1.1. Compensated summation of two floating point numbers. */
assert(fabs(a) >= fabs(b));
double x = a + b;
double y = (a - x) + b;
return (DoubleLength) {x, y};
}

static DoubleLength
dl_sum(double a, double b)
{
/* Algorithm 3.1 Error-free transformation of the sum */
double x = a + b;
double z = x - a;
double y = (a - (x - z)) + (b - z);
return (DoubleLength) {x, y};
}

#ifndef UNRELIABLE_FMA

static DoubleLength
dl_mul(double x, double y)
{
/* Algorithm 3.5. Error-free transformation of a product */
double z = x * y;
double zz = fma(x, y, -z);
return (DoubleLength) {z, zz};
}

#else

/*
The default implementation of dl_mul() depends on the C math library
having an accurate fma() function as required by § 7.12.13.1 of the
C99 standard.
The UNRELIABLE_FMA option is provided as a slower but accurate
alternative for builds where the fma() function is found wanting.
The speed penalty may be modest (17% slower on an Apple M1 Max),
so don't hesitate to enable this build option.
The algorithms are from the T. J. Dekker paper:
A Floating-Point Technique for Extending the Available Precision
https://csclub.uwaterloo.ca/~pbarfuss/dekker1971.pdf
*/

static DoubleLength
dl_split(double x) {
// Dekker (5.5) and (5.6).
double t = x * 134217729.0; // Veltkamp constant = 2.0 ** 27 + 1
double hi = t - (t - x);
double lo = x - hi;
return (DoubleLength) {hi, lo};
}

static DoubleLength
dl_mul(double x, double y)
{
// Dekker (5.12) and mul12()
DoubleLength xx = dl_split(x);
DoubleLength yy = dl_split(y);
double p = xx.hi * yy.hi;
double q = xx.hi * yy.lo + xx.lo * yy.hi;
double z = p + q;
double zz = p - z + q + xx.lo * yy.lo;
return (DoubleLength) {z, zz};
}

#endif

typedef struct { double hi; double lo; double tiny; } TripleLength;

static const TripleLength tl_zero = {0.0, 0.0, 0.0};

static TripleLength
tl_fma(double x, double y, TripleLength total)
{
/* Algorithm 5.10 with SumKVert for K=3 */
DoubleLength pr = dl_mul(x, y);
DoubleLength sm = dl_sum(total.hi, pr.hi);
DoubleLength r1 = dl_sum(total.lo, pr.lo);
DoubleLength r2 = dl_sum(r1.hi, sm.lo);
return (TripleLength) {sm.hi, r2.hi, total.tiny + r1.lo + r2.lo};
}

static double
tl_to_d(TripleLength total)
{
DoubleLength last = dl_sum(total.lo, total.hi);
return total.tiny + last.lo + last.hi;
}


/*
sin(pi*x), giving accurate results for all finite x (especially x
integral or close to an integer). This is here for use in the
Expand Down Expand Up @@ -2301,6 +2408,7 @@ that are almost always correctly rounded, four techniques are used:
* lossless scaling using a power-of-two scaling factor
* accurate squaring using Veltkamp-Dekker splitting [1]
or an equivalent with an fma() call
* compensated summation using a variant of the Neumaier algorithm [2]
* differential correction of the square root [3]
Expand Down Expand Up @@ -2359,14 +2467,21 @@ algorithm, effectively doubling the number of accurate bits.
This technique is used in Dekker's SQRT2 algorithm and again in
Borges' ALGORITHM 4 and 5.
Without proof for all cases, hypot() cannot claim to be always
correctly rounded. However for n <= 1000, prior to the final addition
that rounds the overall result, the internal accuracy of "h" together
with its correction of "x / (2.0 * h)" is at least 100 bits. [6]
Also, hypot() was tested against a Decimal implementation with
prec=300. After 100 million trials, no incorrectly rounded examples
were found. In addition, perfect commutativity (all permutations are
exactly equal) was verified for 1 billion random inputs with n=5. [7]
The hypot() function is faithfully rounded (less than 1 ulp error)
and usually correctly rounded (within 1/2 ulp). The squaring
step is exact. The Neumaier summation computes as if in doubled
precision (106 bits) and has the advantage that its input squares
are non-negative so that the condition number of the sum is one.
The square root with a differential correction is likewise computed
as if in double precision.
For n <= 1000, prior to the final addition that rounds the overall
result, the internal accuracy of "h" together with its correction of
"x / (2.0 * h)" is at least 100 bits. [6] Also, hypot() was tested
against a Decimal implementation with prec=300. After 100 million
trials, no incorrectly rounded examples were found. In addition,
perfect commutativity (all permutations are exactly equal) was
verified for 1 billion random inputs with n=5. [7]
References:
Expand All @@ -2383,9 +2498,8 @@ exactly equal) was verified for 1 billion random inputs with n=5. [7]
static inline double
vector_norm(Py_ssize_t n, double *vec, double max, int found_nan)
{
const double T27 = 134217729.0; /* ldexp(1.0, 27) + 1.0) */
double x, scale, oldcsum, csum = 1.0, frac1 = 0.0, frac2 = 0.0, frac3 = 0.0;
double t, hi, lo, h;
double x, h, scale, oldcsum, csum = 1.0, frac1 = 0.0, frac2 = 0.0;
DoubleLength pr, sm;
int max_e;
Py_ssize_t i;

Expand All @@ -2410,54 +2524,21 @@ vector_norm(Py_ssize_t n, double *vec, double max, int found_nan)
x *= scale;
assert(fabs(x) < 1.0);

t = x * T27;
hi = t - (t - x);
lo = x - hi;
assert(hi + lo == x);

x = hi * hi;
assert(x <= 1.0);
assert(fabs(csum) >= fabs(x));
oldcsum = csum;
csum += x;
frac1 += (oldcsum - csum) + x;

x = 2.0 * hi * lo;
assert(fabs(csum) >= fabs(x));
oldcsum = csum;
csum += x;
frac2 += (oldcsum - csum) + x;

assert(csum + lo * lo == csum);
frac3 += lo * lo;
}
h = sqrt(csum - 1.0 + (frac1 + frac2 + frac3));

x = h;
t = x * T27;
hi = t - (t - x);
lo = x - hi;
assert (hi + lo == x);
pr = dl_mul(x, x);
assert(pr.hi <= 1.0);

x = -hi * hi;
assert(fabs(csum) >= fabs(x));
oldcsum = csum;
csum += x;
frac1 += (oldcsum - csum) + x;

x = -2.0 * hi * lo;
assert(fabs(csum) >= fabs(x));
oldcsum = csum;
csum += x;
frac2 += (oldcsum - csum) + x;

x = -lo * lo;
assert(fabs(csum) >= fabs(x));
oldcsum = csum;
csum += x;
frac3 += (oldcsum - csum) + x;

x = csum - 1.0 + (frac1 + frac2 + frac3);
sm = dl_fast_sum(csum, pr.hi);
csum = sm.hi;
frac1 += pr.lo;
frac2 += sm.lo;
}
h = sqrt(csum - 1.0 + (frac1 + frac2));
pr = dl_mul(-h, h);
sm = dl_fast_sum(csum, pr.hi);
csum = sm.hi;
frac1 += pr.lo;
frac2 += sm.lo;
x = csum - 1.0 + (frac1 + frac2);
return (h + x / (2.0 * h)) / scale;
}
/* When max_e < -1023, ldexp(1.0, -max_e) overflows.
Expand Down Expand Up @@ -2646,102 +2727,6 @@ long_add_would_overflow(long a, long b)
return (a > 0) ? (b > LONG_MAX - a) : (b < LONG_MIN - a);
}

/*
Double and triple length extended precision algorithms from:
Accurate Sum and Dot Product
by Takeshi Ogita, Siegfried M. Rump, and Shin’Ichi Oishi
https://doi.org/10.1137/030601818
https://www.tuhh.de/ti3/paper/rump/OgRuOi05.pdf
*/

typedef struct{ double hi; double lo; } DoubleLength;

static DoubleLength
dl_sum(double a, double b)
{
/* Algorithm 3.1 Error-free transformation of the sum */
double x = a + b;
double z = x - a;
double y = (a - (x - z)) + (b - z);
return (DoubleLength) {x, y};
}

#ifndef UNRELIABLE_FMA

static DoubleLength
dl_mul(double x, double y)
{
/* Algorithm 3.5. Error-free transformation of a product */
double z = x * y;
double zz = fma(x, y, -z);
return (DoubleLength) {z, zz};
}

#else

/*
The default implementation of dl_mul() depends on the C math library
having an accurate fma() function as required by § 7.12.13.1 of the
C99 standard.
The UNRELIABLE_FMA option is provided as a slower but accurate
alternative for builds where the fma() function is found wanting.
The speed penalty may be modest (17% slower on an Apple M1 Max),
so don't hesitate to enable this build option.
The algorithms are from the T. J. Dekker paper:
A Floating-Point Technique for Extending the Available Precision
https://csclub.uwaterloo.ca/~pbarfuss/dekker1971.pdf
*/

static DoubleLength
dl_split(double x) {
// Dekker (5.5) and (5.6).
double t = x * 134217729.0; // Veltkamp constant = 2.0 ** 27 + 1
double hi = t - (t - x);
double lo = x - hi;
return (DoubleLength) {hi, lo};
}

static DoubleLength
dl_mul(double x, double y)
{
// Dekker (5.12) and mul12()
DoubleLength xx = dl_split(x);
DoubleLength yy = dl_split(y);
double p = xx.hi * yy.hi;
double q = xx.hi * yy.lo + xx.lo * yy.hi;
double z = p + q;
double zz = p - z + q + xx.lo * yy.lo;
return (DoubleLength) {z, zz};
}

#endif

typedef struct { double hi; double lo; double tiny; } TripleLength;

static const TripleLength tl_zero = {0.0, 0.0, 0.0};

static TripleLength
tl_fma(double x, double y, TripleLength total)
{
/* Algorithm 5.10 with SumKVert for K=3 */
DoubleLength pr = dl_mul(x, y);
DoubleLength sm = dl_sum(total.hi, pr.hi);
DoubleLength r1 = dl_sum(total.lo, pr.lo);
DoubleLength r2 = dl_sum(r1.hi, sm.lo);
return (TripleLength) {sm.hi, r2.hi, total.tiny + r1.lo + r2.lo};
}

static double
tl_to_d(TripleLength total)
{
DoubleLength last = dl_sum(total.lo, total.hi);
return total.tiny + last.lo + last.hi;
}

/*[clinic input]
math.sumprod
Expand Down

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