FastMath test2

Percentage Accurate: 99.7% → 100.0%

Time: 1.8s

Alternatives: 3

Speedup: 2.3×

Specification

Math

\[\left(d1 \cdot 10 + d1 \cdot d2\right) + d1 \cdot 20 \]

FPCore

(FPCore (d1 d2) :precision binary64 (+ (+ (* d1 10.0) (* d1 d2)) (* d1 20.0)))

C

double code(double d1, double d2) {
	return ((d1 * 10.0) + (d1 * d2)) + (d1 * 20.0);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(d1, d2)
use fmin_fmax_functions
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    code = ((d1 * 10.0d0) + (d1 * d2)) + (d1 * 20.0d0)
end function

Java

public static double code(double d1, double d2) {
	return ((d1 * 10.0) + (d1 * d2)) + (d1 * 20.0);
}

Python

def code(d1, d2):
	return ((d1 * 10.0) + (d1 * d2)) + (d1 * 20.0)

Julia

function code(d1, d2)
	return Float64(Float64(Float64(d1 * 10.0) + Float64(d1 * d2)) + Float64(d1 * 20.0))
end

MATLAB

function tmp = code(d1, d2)
	tmp = ((d1 * 10.0) + (d1 * d2)) + (d1 * 20.0);
end

Wolfram

code[d1_, d2_] := N[(N[(N[(d1 * 10.0), $MachinePrecision] + N[(d1 * d2), $MachinePrecision]), $MachinePrecision] + N[(d1 * 20.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.7% accurate, 1.0× speedup

Math

\[\left(d1 \cdot 10 + d1 \cdot d2\right) + d1 \cdot 20 \]

FPCore

(FPCore (d1 d2) :precision binary64 (+ (+ (* d1 10.0) (* d1 d2)) (* d1 20.0)))

C

double code(double d1, double d2) {
	return ((d1 * 10.0) + (d1 * d2)) + (d1 * 20.0);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(d1, d2)
use fmin_fmax_functions
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    code = ((d1 * 10.0d0) + (d1 * d2)) + (d1 * 20.0d0)
end function

Java

public static double code(double d1, double d2) {
	return ((d1 * 10.0) + (d1 * d2)) + (d1 * 20.0);
}

Python

def code(d1, d2):
	return ((d1 * 10.0) + (d1 * d2)) + (d1 * 20.0)

Julia

function code(d1, d2)
	return Float64(Float64(Float64(d1 * 10.0) + Float64(d1 * d2)) + Float64(d1 * 20.0))
end

MATLAB

function tmp = code(d1, d2)
	tmp = ((d1 * 10.0) + (d1 * d2)) + (d1 * 20.0);
end

Wolfram

code[d1_, d2_] := N[(N[(N[(d1 * 10.0), $MachinePrecision] + N[(d1 * d2), $MachinePrecision]), $MachinePrecision] + N[(d1 * 20.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 2.3× speedup

Math

\[\left(d2 - -30\right) \cdot d1 \]

FPCore

(FPCore (d1 d2) :precision binary64 (* (- d2 -30.0) d1))

C

double code(double d1, double d2) {
	return (d2 - -30.0) * d1;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(d1, d2)
use fmin_fmax_functions
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    code = (d2 - (-30.0d0)) * d1
end function

Java

public static double code(double d1, double d2) {
	return (d2 - -30.0) * d1;
}

Python

def code(d1, d2):
	return (d2 - -30.0) * d1

Julia

function code(d1, d2)
	return Float64(Float64(d2 - -30.0) * d1)
end

MATLAB

function tmp = code(d1, d2)
	tmp = (d2 - -30.0) * d1;
end

Wolfram

code[d1_, d2_] := N[(N[(d2 - -30.0), $MachinePrecision] * d1), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(d1 \cdot 10 + d1 \cdot d2\right) + d1 \cdot 20 \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto \color{blue}{\left(d1 \cdot 10 + d1 \cdot d2\right) + d1 \cdot 20} \]

   2. lift-+.f64 N/A

      \[\leadsto \color{blue}{\left(d1 \cdot 10 + d1 \cdot d2\right)} + d1 \cdot 20 \]

   3. lift-*.f64 N/A

      \[\leadsto \left(\color{blue}{d1 \cdot 10} + d1 \cdot d2\right) + d1 \cdot 20 \]

   4. lift-*.f64 N/A

      \[\leadsto \left(d1 \cdot 10 + \color{blue}{d1 \cdot d2}\right) + d1 \cdot 20 \]

   5. distribute-lft-out N/A

      \[\leadsto \color{blue}{d1 \cdot \left(10 + d2\right)} + d1 \cdot 20 \]

   6. lift-*.f64 N/A

      \[\leadsto d1 \cdot \left(10 + d2\right) + \color{blue}{d1 \cdot 20} \]

   7. distribute-lft-out N/A

      \[\leadsto \color{blue}{d1 \cdot \left(\left(10 + d2\right) + 20\right)} \]

   8. *-commutative N/A

      \[\leadsto \color{blue}{\left(\left(10 + d2\right) + 20\right) \cdot d1} \]

   9. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\left(10 + d2\right) + 20\right) \cdot d1} \]

   10. add-flip N/A

       \[\leadsto \color{blue}{\left(\left(10 + d2\right) - \left(\mathsf{neg}\left(20\right)\right)\right)} \cdot d1 \]

   11. +-commutative N/A

       \[\leadsto \left(\color{blue}{\left(d2 + 10\right)} - \left(\mathsf{neg}\left(20\right)\right)\right) \cdot d1 \]

   12. associate--l+ N/A

       \[\leadsto \color{blue}{\left(d2 + \left(10 - \left(\mathsf{neg}\left(20\right)\right)\right)\right)} \cdot d1 \]

   13. add-flip N/A

       \[\leadsto \color{blue}{\left(d2 - \left(\mathsf{neg}\left(\left(10 - \left(\mathsf{neg}\left(20\right)\right)\right)\right)\right)\right)} \cdot d1 \]

   14. lower--.f64 N/A

       \[\leadsto \color{blue}{\left(d2 - \left(\mathsf{neg}\left(\left(10 - \left(\mathsf{neg}\left(20\right)\right)\right)\right)\right)\right)} \cdot d1 \]

   15. metadata-eval N/A

       \[\leadsto \left(d2 - \left(\mathsf{neg}\left(\left(10 - \color{blue}{-20}\right)\right)\right)\right) \cdot d1 \]

   16. metadata-eval N/A

       \[\leadsto \left(d2 - \left(\mathsf{neg}\left(\color{blue}{30}\right)\right)\right) \cdot d1 \]

   17. metadata-eval 100.0%

       \[\leadsto \left(d2 - \color{blue}{-30}\right) \cdot d1 \]

3. Applied rewrites 100.0%

   \[\leadsto \color{blue}{\left(d2 - -30\right) \cdot d1} \]
4. Add Preprocessing

Alternative 2: 97.4% accurate, 1.3× speedup

Math

\[\begin{array}{l} \mathbf{if}\;d2 \leq -21000:\\ \;\;\;\;d2 \cdot d1\\ \mathbf{elif}\;d2 \leq 980000000:\\ \;\;\;\;30 \cdot d1\\ \mathbf{else}:\\ \;\;\;\;d2 \cdot d1\\ \end{array} \]

FPCore

(FPCore (d1 d2)
 :precision binary64
 (if (<= d2 -21000.0)
   (* d2 d1)
   (if (<= d2 980000000.0) (* 30.0 d1) (* d2 d1))))

C

double code(double d1, double d2) {
	double tmp;
	if (d2 <= -21000.0) {
		tmp = d2 * d1;
	} else if (d2 <= 980000000.0) {
		tmp = 30.0 * d1;
	} else {
		tmp = d2 * d1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(d1, d2)
use fmin_fmax_functions
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    real(8) :: tmp
    if (d2 <= (-21000.0d0)) then
        tmp = d2 * d1
    else if (d2 <= 980000000.0d0) then
        tmp = 30.0d0 * d1
    else
        tmp = d2 * d1
    end if
    code = tmp
end function

Java

public static double code(double d1, double d2) {
	double tmp;
	if (d2 <= -21000.0) {
		tmp = d2 * d1;
	} else if (d2 <= 980000000.0) {
		tmp = 30.0 * d1;
	} else {
		tmp = d2 * d1;
	}
	return tmp;
}

Python

def code(d1, d2):
	tmp = 0
	if d2 <= -21000.0:
		tmp = d2 * d1
	elif d2 <= 980000000.0:
		tmp = 30.0 * d1
	else:
		tmp = d2 * d1
	return tmp

Julia

function code(d1, d2)
	tmp = 0.0
	if (d2 <= -21000.0)
		tmp = Float64(d2 * d1);
	elseif (d2 <= 980000000.0)
		tmp = Float64(30.0 * d1);
	else
		tmp = Float64(d2 * d1);
	end
	return tmp
end

MATLAB

function tmp_2 = code(d1, d2)
	tmp = 0.0;
	if (d2 <= -21000.0)
		tmp = d2 * d1;
	elseif (d2 <= 980000000.0)
		tmp = 30.0 * d1;
	else
		tmp = d2 * d1;
	end
	tmp_2 = tmp;
end

Wolfram

code[d1_, d2_] := If[LessEqual[d2, -21000.0], N[(d2 * d1), $MachinePrecision], If[LessEqual[d2, 980000000.0], N[(30.0 * d1), $MachinePrecision], N[(d2 * d1), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if d2 < -21000 or 9.8e8 < d2

   1. Initial program 99.7%

      \[\left(d1 \cdot 10 + d1 \cdot d2\right) + d1 \cdot 20 \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(d1 \cdot 10 + d1 \cdot d2\right) + d1 \cdot 20} \]

      2. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(d1 \cdot 10 + d1 \cdot d2\right)} + d1 \cdot 20 \]

      3. lift-*.f64 N/A

         \[\leadsto \left(\color{blue}{d1 \cdot 10} + d1 \cdot d2\right) + d1 \cdot 20 \]

      4. lift-*.f64 N/A

         \[\leadsto \left(d1 \cdot 10 + \color{blue}{d1 \cdot d2}\right) + d1 \cdot 20 \]

      5. distribute-lft-out N/A

         \[\leadsto \color{blue}{d1 \cdot \left(10 + d2\right)} + d1 \cdot 20 \]

      6. lift-*.f64 N/A

         \[\leadsto d1 \cdot \left(10 + d2\right) + \color{blue}{d1 \cdot 20} \]

      7. distribute-lft-out N/A

         \[\leadsto \color{blue}{d1 \cdot \left(\left(10 + d2\right) + 20\right)} \]

      8. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(10 + d2\right) + 20\right) \cdot d1} \]

      9. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(10 + d2\right) + 20\right) \cdot d1} \]

      10. add-flip N/A

          \[\leadsto \color{blue}{\left(\left(10 + d2\right) - \left(\mathsf{neg}\left(20\right)\right)\right)} \cdot d1 \]

      11. +-commutative N/A

          \[\leadsto \left(\color{blue}{\left(d2 + 10\right)} - \left(\mathsf{neg}\left(20\right)\right)\right) \cdot d1 \]

      12. associate--l+ N/A

          \[\leadsto \color{blue}{\left(d2 + \left(10 - \left(\mathsf{neg}\left(20\right)\right)\right)\right)} \cdot d1 \]

      13. add-flip N/A

          \[\leadsto \color{blue}{\left(d2 - \left(\mathsf{neg}\left(\left(10 - \left(\mathsf{neg}\left(20\right)\right)\right)\right)\right)\right)} \cdot d1 \]

      14. lower--.f64 N/A

          \[\leadsto \color{blue}{\left(d2 - \left(\mathsf{neg}\left(\left(10 - \left(\mathsf{neg}\left(20\right)\right)\right)\right)\right)\right)} \cdot d1 \]

      15. metadata-eval N/A

          \[\leadsto \left(d2 - \left(\mathsf{neg}\left(\left(10 - \color{blue}{-20}\right)\right)\right)\right) \cdot d1 \]

      16. metadata-eval N/A

          \[\leadsto \left(d2 - \left(\mathsf{neg}\left(\color{blue}{30}\right)\right)\right) \cdot d1 \]

      17. metadata-eval 100.0%

          \[\leadsto \left(d2 - \color{blue}{-30}\right) \cdot d1 \]

   3. Applied rewrites 100.0%

      \[\leadsto \color{blue}{\left(d2 - -30\right) \cdot d1} \]

   4. Taylor expanded in d2 around 0

      \[\leadsto \color{blue}{30} \cdot d1 \]
   5. Step-by-step derivation

   6. Applied rewrites 50.8%

      \[\leadsto \color{blue}{30} \cdot d1 \]

   7. Taylor expanded in d2 around inf

      \[\leadsto \color{blue}{d2} \cdot d1 \]
   8. Step-by-step derivation

   9. Applied rewrites 51.0%

      \[\leadsto \color{blue}{d2} \cdot d1 \]

   if -21000 < d2 < 9.8e8

   1. Initial program 99.7%

      \[\left(d1 \cdot 10 + d1 \cdot d2\right) + d1 \cdot 20 \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(d1 \cdot 10 + d1 \cdot d2\right) + d1 \cdot 20} \]

      2. lift-+.f64 N/A

         \[\leadsto \color{blue}{\left(d1 \cdot 10 + d1 \cdot d2\right)} + d1 \cdot 20 \]

      3. lift-*.f64 N/A

         \[\leadsto \left(\color{blue}{d1 \cdot 10} + d1 \cdot d2\right) + d1 \cdot 20 \]

      4. lift-*.f64 N/A

         \[\leadsto \left(d1 \cdot 10 + \color{blue}{d1 \cdot d2}\right) + d1 \cdot 20 \]

      5. distribute-lft-out N/A

         \[\leadsto \color{blue}{d1 \cdot \left(10 + d2\right)} + d1 \cdot 20 \]

      6. lift-*.f64 N/A

         \[\leadsto d1 \cdot \left(10 + d2\right) + \color{blue}{d1 \cdot 20} \]

      7. distribute-lft-out N/A

         \[\leadsto \color{blue}{d1 \cdot \left(\left(10 + d2\right) + 20\right)} \]

      8. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(10 + d2\right) + 20\right) \cdot d1} \]

      9. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(10 + d2\right) + 20\right) \cdot d1} \]

      10. add-flip N/A

          \[\leadsto \color{blue}{\left(\left(10 + d2\right) - \left(\mathsf{neg}\left(20\right)\right)\right)} \cdot d1 \]

      11. +-commutative N/A

          \[\leadsto \left(\color{blue}{\left(d2 + 10\right)} - \left(\mathsf{neg}\left(20\right)\right)\right) \cdot d1 \]

      12. associate--l+ N/A

          \[\leadsto \color{blue}{\left(d2 + \left(10 - \left(\mathsf{neg}\left(20\right)\right)\right)\right)} \cdot d1 \]

      13. add-flip N/A

          \[\leadsto \color{blue}{\left(d2 - \left(\mathsf{neg}\left(\left(10 - \left(\mathsf{neg}\left(20\right)\right)\right)\right)\right)\right)} \cdot d1 \]

      14. lower--.f64 N/A

          \[\leadsto \color{blue}{\left(d2 - \left(\mathsf{neg}\left(\left(10 - \left(\mathsf{neg}\left(20\right)\right)\right)\right)\right)\right)} \cdot d1 \]

      15. metadata-eval N/A

          \[\leadsto \left(d2 - \left(\mathsf{neg}\left(\left(10 - \color{blue}{-20}\right)\right)\right)\right) \cdot d1 \]

      16. metadata-eval N/A

          \[\leadsto \left(d2 - \left(\mathsf{neg}\left(\color{blue}{30}\right)\right)\right) \cdot d1 \]

      17. metadata-eval 100.0%

          \[\leadsto \left(d2 - \color{blue}{-30}\right) \cdot d1 \]

   3. Applied rewrites 100.0%

      \[\leadsto \color{blue}{\left(d2 - -30\right) \cdot d1} \]

   4. Taylor expanded in d2 around 0

      \[\leadsto \color{blue}{30} \cdot d1 \]
   5. Step-by-step derivation

   6. Applied rewrites 50.8%

      \[\leadsto \color{blue}{30} \cdot d1 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 50.8% accurate, 3.9× speedup

Math

\[30 \cdot d1 \]

FPCore

(FPCore (d1 d2) :precision binary64 (* 30.0 d1))

C

double code(double d1, double d2) {
	return 30.0 * d1;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(d1, d2)
use fmin_fmax_functions
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    code = 30.0d0 * d1
end function

Java

public static double code(double d1, double d2) {
	return 30.0 * d1;
}

Python

def code(d1, d2):
	return 30.0 * d1

Julia

function code(d1, d2)
	return Float64(30.0 * d1)
end

MATLAB

function tmp = code(d1, d2)
	tmp = 30.0 * d1;
end

Wolfram

code[d1_, d2_] := N[(30.0 * d1), $MachinePrecision]

Derivation

1. Initial program 99.7%

   \[\left(d1 \cdot 10 + d1 \cdot d2\right) + d1 \cdot 20 \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto \color{blue}{\left(d1 \cdot 10 + d1 \cdot d2\right) + d1 \cdot 20} \]

   2. lift-+.f64 N/A

      \[\leadsto \color{blue}{\left(d1 \cdot 10 + d1 \cdot d2\right)} + d1 \cdot 20 \]

   3. lift-*.f64 N/A

      \[\leadsto \left(\color{blue}{d1 \cdot 10} + d1 \cdot d2\right) + d1 \cdot 20 \]

   4. lift-*.f64 N/A

      \[\leadsto \left(d1 \cdot 10 + \color{blue}{d1 \cdot d2}\right) + d1 \cdot 20 \]

   5. distribute-lft-out N/A

      \[\leadsto \color{blue}{d1 \cdot \left(10 + d2\right)} + d1 \cdot 20 \]

   6. lift-*.f64 N/A

      \[\leadsto d1 \cdot \left(10 + d2\right) + \color{blue}{d1 \cdot 20} \]

   7. distribute-lft-out N/A

      \[\leadsto \color{blue}{d1 \cdot \left(\left(10 + d2\right) + 20\right)} \]

   8. *-commutative N/A

      \[\leadsto \color{blue}{\left(\left(10 + d2\right) + 20\right) \cdot d1} \]

   9. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\left(10 + d2\right) + 20\right) \cdot d1} \]

   10. add-flip N/A

       \[\leadsto \color{blue}{\left(\left(10 + d2\right) - \left(\mathsf{neg}\left(20\right)\right)\right)} \cdot d1 \]

   11. +-commutative N/A

       \[\leadsto \left(\color{blue}{\left(d2 + 10\right)} - \left(\mathsf{neg}\left(20\right)\right)\right) \cdot d1 \]

   12. associate--l+ N/A

       \[\leadsto \color{blue}{\left(d2 + \left(10 - \left(\mathsf{neg}\left(20\right)\right)\right)\right)} \cdot d1 \]

   13. add-flip N/A

       \[\leadsto \color{blue}{\left(d2 - \left(\mathsf{neg}\left(\left(10 - \left(\mathsf{neg}\left(20\right)\right)\right)\right)\right)\right)} \cdot d1 \]

   14. lower--.f64 N/A

       \[\leadsto \color{blue}{\left(d2 - \left(\mathsf{neg}\left(\left(10 - \left(\mathsf{neg}\left(20\right)\right)\right)\right)\right)\right)} \cdot d1 \]

   15. metadata-eval N/A

       \[\leadsto \left(d2 - \left(\mathsf{neg}\left(\left(10 - \color{blue}{-20}\right)\right)\right)\right) \cdot d1 \]

   16. metadata-eval N/A

       \[\leadsto \left(d2 - \left(\mathsf{neg}\left(\color{blue}{30}\right)\right)\right) \cdot d1 \]

   17. metadata-eval 100.0%

       \[\leadsto \left(d2 - \color{blue}{-30}\right) \cdot d1 \]

3. Applied rewrites 100.0%

   \[\leadsto \color{blue}{\left(d2 - -30\right) \cdot d1} \]

4. Taylor expanded in d2 around 0

   \[\leadsto \color{blue}{30} \cdot d1 \]
5. Step-by-step derivation

6. Applied rewrites 50.8%

   \[\leadsto \color{blue}{30} \cdot d1 \]
7. Add Preprocessing

Developer Target 1: 100.0% accurate, 2.3× speedup

Math

\[d1 \cdot \left(30 + d2\right) \]

FPCore

(FPCore (d1 d2) :precision binary64 (* d1 (+ 30.0 d2)))

C

double code(double d1, double d2) {
	return d1 * (30.0 + d2);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(d1, d2)
use fmin_fmax_functions
    real(8), intent (in) :: d1
    real(8), intent (in) :: d2
    code = d1 * (30.0d0 + d2)
end function

Java

public static double code(double d1, double d2) {
	return d1 * (30.0 + d2);
}

Python

def code(d1, d2):
	return d1 * (30.0 + d2)

Julia

function code(d1, d2)
	return Float64(d1 * Float64(30.0 + d2))
end

MATLAB

function tmp = code(d1, d2)
	tmp = d1 * (30.0 + d2);
end

Wolfram

code[d1_, d2_] := N[(d1 * N[(30.0 + d2), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2025183 
(FPCore (d1 d2)
  :name "FastMath test2"
  :precision binary64

  :alt
  (! :herbie-platform c (* d1 (+ 30 d2)))

  (+ (+ (* d1 10.0) (* d1 d2)) (* d1 20.0)))