32/64-Bit 80x86 Assembly Language Architecture

Do not expect the resulting values from different calculations to be identical. For example, 2.0 x 9.0 is about 18.0, and 180.0 10.0 is about18.0, but the two 18.0 values are not guaranteed to be identical.

2.0 9.0 180.0 10.0

Let us examine a range of values 10 ⁿ and compare a displacement of 0.001 versus 0.0000001.

Okay, one more table for more clarity.

What this means is that smaller numbers such as those that are normalized and have a numerical range from 1.0 to 1.0 allow for higher precision values, but those with larger values are inaccurate and thus not very precise. For example, the distance between 1.001 and 1.002,1.002 and 1.003, etc. is about 0x20c4 (8,388). This means that about 8,387 numbers exist between those two samples. A number with a higher digit count such as 1000.001 or 1000.002 support about 0x11(17), so only about 16 numbers exist between those two numbers. And a number around 1000000 identifies 1000000.001 and 1000000.002 as the same number. This makes for comparisons of floating-point numbers with nearly the same value very tricky. This is one of the reasons why floating-point numbers are not used for currency as they tend to lose pennies. Binary-coded decimal (BCD) and fixed-point (integer) are used instead.

So when working with normalized numbers {1.0 1.0}, a comparison algorithm with a precision slop factor (accuracy) of around0.0000001 should be utilized. When working with estimated results, a much smaller value should be used. The following function returns a Boolean true : false value to indicate that the two values are close enough to be considered the same value. Normally you would not compare two floating-point values except to see if one is greater than the other for purposes of clipping. You almost never use a comparison of the same value as shown here. It is only used in this book for purposes of comparing the results of C code to assembly code to see if you are getting results from your algorithms in the range of what you expected.

Listing 8-1: vmp_IsFEqual() Compares two single-precision floating-point values and determines if they are equivalent based upon the precision factor or if one is less than or greater than the other.

bool vmp_IsFEqual(float fA, float fB, float fPrec) { // The same so very big similar numbers or very small // accurate numbers. if (fA == fB) return true; // Try with a little precision slop! return (((fAfPrec)<=fB) && (fB<=(fA+fPrec))); }

Making the call for single-precision floating-point numbers is easy:

#define SINGLE_PRECISION 0.0000001f if (!vmp_IsFEqual(f, f2, SINGLE_PRECISION))

For a fast algorithm that uses estimation for division or square roots, then merely reduces the precision to something less accurate:

#define FAST_PRECISION 0.001f

This book will discuss these fast estimate algorithms in later chapters. For vector comparisons, this book uses the following code: When dealing with quad vectors (vmp3DQVector) an alternative function is called:

Listing 8-2: Compare two {XYZ} vectors using a specified precision factor.

bool vmp_IsVEqual(const vmp3DVector * const pvA, const vmp3DVector * const pvB, float fPrec) { ASSERT_PTR4(pvA); // See explanation of assert macros ASSERT_PTR4(pvB); // later in this chapter! if (!vmp_IsFEqual(pvA>x, pvB>x, fPrec) !vmp_IsFEqual(pvA>y, pvB>y, fPrec) !vmp_IsFEqual(pvA>z, pvB>z, fPrec)) { return false; } return true; }

Listing 8-3: Compare two {XYZW} vectors using a specified precision factor.

bool vmp_IsQVEqual(const vmp3DQVector *const pvA, const vmp3DQVector *const pvB, float fPrec)

and a fourth element {.w} is tested :

!vmp_IsFEqual(pvA>w, pvB>w, fPrec)

FTST FPU Test If Zero

Mnemonic

P

PII

K6

3D!

3Mx+

SSE

SSE2

A64

SSE3

E64T

FTST