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---
title: Michael Abrash's Graphics Programming Black Book, Special Edition
author: Michael Abrash
date: '1997-07-01'
isbn: '1576101746'
publisher: The Coriolis Group
category: 'Web and Software Development: Game Development,Web and Software Development:
Graphics and Multimedia Development'
chapter: '10'
pages: 190-203
---
## Chapter 10\
Patient Coding, Faster Code {#Heading1}
### How Working Quickly Can Bring Execution to a Crawl {#Heading2}
My grandfather does *The New York Times* crossword puzzle every Sunday.
In ink. With nary a blemish.
The relevance of which will become apparent in a trice.
What my grandfather is, is a pattern matcher *par excellence*. You're a
pattern matcher, too. So am I. We can't help it; it comes with the
territory. Try focusing on text and not reading it. Can't do it. Can you
hear the voice of someone you know and not recognize it? I can't. And
how in the Nine Billion Names of God is it that we're capable of
instantly recognizing one face out of the thousands we've seen in our
lifetimes—even years later, from a different angle and in different
light? Although we take them for granted, our pattern-matching
capabilities are surely a miracle on the order of loaves and fishes.
By "pattern matching," I mean more than just recognition, though. I mean
that we are generally able to take complex and often seemingly woefully
inadequate data, instantaneously match it in an incredibly flexible way
to our past experience, extrapolate, and reach amazing conclusions,
something that computers can scarcely do at all. Crossword puzzles are
an excellent example; given a couple of letters and a cryptic clue,
we're somehow able to come up with one out of several hundred thousand
words that we know. Try writing a program to do that! What's more, we
don't process data in the serial brute-force way that computers do.
Solutions tend to be virtually instantaneous or not at all; none of
those "N log N" or "N^2"^ execution times for us.
It goes without saying that pattern matching is good; more than that,
it's a large part of what we are, and, generally, the faster we are at
it, the better. Not always, though. Sometimes insufficient information
really is insufficient, and, in our haste to get the heady rush of
coming up with a solution, incorrect or less-than-optimal conclusions
are reached, as anyone who has ever done the *Times* Sunday crossword
will attest. Still, my grandfather does that puzzle every Sunday *in
ink*. What's his secret? Patience and discipline. He never fills a word
in until he's confirmed it in his head via intersecting words, no matter
how strong the urge may be to put something down where he can see it and
feel like he's getting somewhere.
There's a surprisingly close parallel to programming here. Programming
is certainly a sort of pattern matching in the sense I've described
above, and, as with crossword puzzles, following your programming
instincts too quickly can be a liability. For many programmers, myself
included, there's a strong urge to find a workable approach to a
particular problem and start coding it *right now*, what some people
call "hacking" a program. Going with the first thing your programming
pattern matcher comes up with can be a lot of fun; there's instant
gratification and a feeling of unbounded creativity. Personally, I've
always hungered to get results from my work as soon as possible; I
gravitated toward graphics for its instant and very visible
gratification. Over time, however, I've learned patience.
> ![](images/i.jpg)
> I've come to spend an increasingly large portion of my time choosing
> algorithms, designing, and simply giving my mind quiet time in which to
> work on problems and come up with non-obvious approaches before coding;
> and I've found that the extra time up front more than pays for itself in
> both decreased coding time and superior programs.
In this chapter, I'm going to walk you through a simple but illustrative
case history that nicely points up the wisdom of delaying gratification
when faced with programming problems, so that your mind has time to chew
on the problems from other angles. The alternative solutions you find by
doing this may seem obvious, once you've come up with them. They may not
even differ greatly from your initial solutions. Often, however, they
will be much better—and you'll never even have the chance to decide
whether they're better or not if you take the first thing that comes
into your head and run with it.
#### The Case for Delayed Gratification {#Heading3}
Once upon a time, I set out to read *Algorithms*, by Robert Sedgewick
(Addison-Wesley), which turned out to be a wonderful, stimulating, and
most useful book, one that I recommend highly. My story, however,
involves only what happened in the first 12 pages, for it was in those
pages that Sedgewick discussed Euclid's algorithm.
Euclid's algorithm (discovered by Euclid, of Euclidean geometry fame, a
very long time ago, way back when computers still used core memory) is a
straightforward algorithm that solves one of the simplest problems
imaginable: finding the greatest common integer divisor (GCD) of two
positive integers. Sedgewick points out that this is useful for reducing
a fraction to its lowest terms. I'm sure it's useful for other things,
as well, although none spring to mind. (A long time ago, I wrote an
article about optimizing a bit of code that wasn't even vaguely
time-critical, and got swamped with letters telling me so. I knew it
wasn't time-critical; it was just a good example. So for now, close your
eyes and *imagine* that finding the GCD is not only necessary but must
also be done as quickly as possible, because it's perfect for the point
I want to make here and now. Okay?)
The problem at hand, then, is simply this: Find the largest integer
value that evenly divides two arbitrary positive integers. That's all
there is to it. So warm up your pattern matchers...and go!
### The Brute-Force Syndrome {#Heading4}
I have a funny feeling that you'd already figured out how to find the
GCD before I even said "go." That's what I did when reading
*Algorithms;* before I read another word, I had to figure it out for
myself. Programmers are like that; give them a problem and their eyes
immediately glaze over as they try to solve it before you've even shut
your mouth. That sort of instant response can certainly be impressive,
but it can backfire, too, as it did in my case.
You see, I fell victim to a common programming pitfall, the
"brute-force" syndrome. The basis of this syndrome is that there are
many problems that have obvious, brute-force solutions—with one small
drawback. The drawback is that if you were to try to apply a brute-force
solution by hand—that is, work a single problem out with pencil and
paper or a calculator—it would generally require that you have the
patience and discipline to work on the problem for approximately seven
hundred years, not counting eating and sleeping, in order to get an
answer. Finding all the prime numbers less than 1,000,000 is a good
example; just divide each number up to 1,000,000 by every lesser number,
and see what's left standing. For most of the history of humankind,
people were forced to think of cleverer solutions, such as the Sieve of
Eratosthenes (we'd have been in big trouble if the ancient Greeks had
had computers), mainly because after about five minutes of brute
force-type work, people's attention gets diverted to other important
matters, such as how far a paper airplane will fly from a second-story
window.
Not so nowadays, though. Computers love boring work; they're very
patient and disciplined, and, besides, one human year = seven dog years
= two zillion computer years. So when we're faced with a problem that
has an obvious but exceedingly lengthy solution, we're apt to say, "Ah,
let the computer do that, it's fast," and go back to making paper
airplanes. Unfortunately, brute-force solutions tend to be slow even
when performed by modern-day microcomputers, which are capable of
several MIPS except when I'm late for an appointment and want to finish
a compile and run just one more test before I leave, in which case the
crystal in my computer is apparently designed to automatically revert to
1 Hz.)
The solution that I instantly came up with to finding the GCD is about
as brute- force as you can get: Divide both the larger integer (iL) and
the smaller integer (iS) by every integer equal to or less than the
smaller integer, until a number is found that divides both evenly, as
shown in Figure 10.1. This works, but it's a lousy solution, requiring
as many as iS\*2 divisions; *very* expensive, especially for large
values of iS. For example, finding the GCD of 30,001 and 30,002 would
require 60,002 divisions, which alone, disregarding tests and branches,
would take about 2 seconds on an 8088, and more than 50 milliseconds
even on a 25 MHz 486—a *very* long time in computer years, and not
insignificant in human years either.
Listing 10.1 is an implementation of the brute-force approach to GCD
calculation. Table 10.1 shows how long it takes this approach to find
the GCD for several integer pairs. As expected, performance is extremely
poor when iS is large.
![**Figure 10.1**  *Using a brute-force algorithm to find a GCD.*](images/10-01.jpg)
**Integer pairs for which to find GCD**
| | 90 & 27 | 42 & 998 | 453 & 121 | 27432 & 165 | 27432 & 17550 |
|----------------------------------------------------------------------------------------|-------------|--------------|--------------|--------------|----------------|
| **Listing 10.1** (Brute force) | 60µs (100%) | 110µs (100%) | 311ms (100%) | 426µs (100%) | 43580µs (100%) |
| **Listing 10.2** (Subtraction) | 25 (42%) | 72 (65%) | 67 (22%) | 280 (66%) | 72 (0.16%) |
| **Listing 10.3** (Division: code recursive Euclid's algorithm) | 20 (33%) | 33 (30%) | 48 (15%) | 32 (8%) | 53 (0.12%) |
| **Listing 10.4** (C version of data recursive Euclid's algorithm; normal optimization) | 12 (20%) | 17 (15%) | 25 (8%) | 16 (4%) | 26 (0.06%) |
| **Listing 10.4** (/Ox = maximumoptimization) | 12 (20%) | 16 (15%) | 20 (6%) | 15 (4%) | 23 (0.05%) |
| **Listing 10.5** (Assembly version of data recursive Euclid's algorithm) | 10 (17%) | 10 (9%) | 15 (5%) | 10 (2%) | 17 (0.04%) |
Table: Table 10.1 Performance of GCD algorithm implementations.
**Note:** Performance of Listings 10.1 through 10.5 in finding the
greatest common divisors of various pairs of integers. Times are in
microseconds. Percentages represent execution time as a percentage of
the execution time of Listing 10.1 for the same integer pair. Listings
10.1-10.4 were compiled with Microsoft C /C++ except as noted, the
default optimization was used. All times measured with the Zen timer
(from Chapter 3) on a 20 MHz cached 386.
**LISTING 10.1 L10-1.C**
```c
/* Finds and returns the greatest common divisor of two positive
integers. Works by trying every integral divisor between the
smaller of the two integers and 1, until a divisor that divides
both integers evenly is found. All C code tested with Microsoft
and Borland compilers.*/
unsigned int gcd(unsigned int int1, unsigned int int2) {
unsigned int temp, trial_divisor;
/* Swap if necessary to make sure that int1 >= int2 */
if (int1 < int2) {
temp = int1;
int1 = int2;
int2 = temp;
}
/* Now just try every divisor from int2 on down, until a common
divisor is found. This can never be an infinite loop because
1 divides everything evenly */
for (trial_divisor = int2; ((int1 % trial_divisor) != 0) ||
((int2 % trial_divisor) != 0); trial_divisor—)
;
return(trial_divisor);
}
```
#### Wasted Breakthroughs {#Heading5}
Sedgewick's first solution to the GCD problem was pretty much the one I
came up with. He then pointed out that the GCD of iL and iS is the same
as the GCD of iL-iS and iS. This was obvious (once Sedgewick pointed it
out); by the very nature of division, any number that divides iL evenly
nL times and iS evenly nS times must divide iL-iS evenly nL-nS times.
Given that insight, I immediately designed a new, faster approach, shown
in Listing 10.2.
**LISTING 10.2 L10-2.C**
```c
/* Finds and returns the greatest common divisor of two positive
integers. Works by subtracting the smaller integer from the
larger integer until either the values match (in which case
that's the gcd), or the larger integer becomes the smaller of
the two, in which case the two integers swap roles and the
subtraction process continues. */
unsigned int gcd(unsigned int int1, unsigned int int2) {
unsigned int temp;
/* If the two integers are the same, that's the gcd and we're
done */
if (int1 == int2) {
return(int1);
}
/* Swap if necessary to make sure that int1 >= int2 */
if (int1 < int2) {
temp = int1;
int1 = int2;
int2 = temp;
}
/* Subtract int2 from int1 until int1 is no longer the larger of
the two */
do {
int1 -= int2;
} while (int1 > int2);
/* Now recursively call this function to continue the process */
return(gcd(int1, int2));
}
```
Listing 10.2 repeatedly subtracts iS from iL until iL becomes less than
or equal to iS. If iL becomes equal to iS, then that's the GCD;
alternatively, if iL becomes *less* than iS, iL and iS switch values,
and the process is repeated, as shown in Figure 10.2. The number of
iterations this approach requires relative to Listing 10.1 depends
heavily on the values of iL and iS, so it's not always faster, but, as
Table 10.1 indicates, Listing 10.2 is generally much better code.
![**Figure 10.2**  *Using repeated subtraction algorithm to find a GCD.*](images/10-02.jpg)
Listing 10.2 is a far graver misstep than Listing 10.1, for all that
it's faster. Listing 10.1 is obviously a hacked-up, brute-force
approach; no one could mistake it for anything else. It could be speeded
up in any of a number of ways with a little thought. (Simply skipping
testing all the divisors between iS and iS/2, not inclusive, would cut
the worst-case time in half, for example; that's not a particularly
*good* optimization, but it illustrates how easily Listing 10.1 can be
improved.) Listing 10.1 is a hack job, crying out for inspiration.
Listing 10.2, on the other hand, has gotten the inspiration—and largely
wasted it through haste. Had Sedgewick not told me otherwise, I might
well have assumed that Listing 10.2 was optimized, a mistake I would
never have made with Listing 10.1. I experienced a conceptual
breakthrough when I understood Sedgewick's point: A smaller number can
be subtracted from a larger number without affecting their GCD, thereby
inexpensively reducing the scale of the problem. And, in my hurry to
make this breakthrough reality, I missed its full scope. As Sedgewick
says on the very next page, the number that one gets by subtracting iS
from iL until iL is less than iS is precisely the same as the remainder
that one gets by dividing iL by iS—again, this is inherent in the nature
of division—and *that* is the basis for Euclid's algorithm, shown in
Figure 10.3. Listing 10.3 is an implementation of Euclid's algorithm.
**LISTING 10.3 L10-3.C**
```c
/* Finds and returns the greatest common divisor of two integers.
Uses Euclid's algorithm: divides the larger integer by the
smaller; if the remainder is 0, the smaller integer is the GCD,
otherwise the smaller integer becomes the larger integer, the
remainder becomes the smaller integer, and the process is
repeated. */
static unsigned int gcd_recurs(unsigned int, unsigned int);
unsigned int gcd(unsigned int int1, unsigned int int2) {
unsigned int temp;
/* If the two integers are the same, that's the GCD and we're
done */
if (int1 == int2) {
return(int1);
}
/* Swap if necessary to make sure that int1 >= int2 */
if (int1 < int2) {
temp = int1;
int1 = int2;
int2 = temp;
}
/* Now call the recursive form of the function, which assumes
that the first parameter is the larger of the two */
return(gcd_recurs(int1, int2));
}
static unsigned int gcd_recurs(unsigned int larger_int,
unsigned int smaller_int)
{
int temp;
/* If the remainder of larger_int divided by smaller_int is 0,
then smaller_int is the gcd */
if ((temp = larger_int % smaller_int) == 0) {
return(smaller_int);
}
/* Make smaller_int the larger integer and the remainder the
smaller integer, and call this function recursively to
continue the process */
return(gcd_recurs(smaller_int, temp));
}
```
As you can see from Table 10.1, Euclid's algorithm is superior,
especially for large numbers (and imagine if we were working with large
*longs!*).
> ![](images/i.jpg)
> Had I been implementing GCD determination without Sedgewick's help, I
> would surely not have settled for Listing 10.1—but I might well have
> ended up with Listing 10.2 in my enthusiasm over the "brilliant"
> discovery of subtracting the lesser Using Euclid's algorithm to find a
> GCD number from the greater. In a commercial product, my lack of
> patience and discipline could have been costly indeed.
![**Figure 10.3**  *Using Euclid's algorithm to find a GCD.*](images/10-03.jpg)
Give your mind time and space to wander around the edges of important
programming problems before you settle on any one approach. I titled
this book's first chapter "The Best Optimizer Is between Your Ears," and
that's still true; what's even more true is that the optimizer between
your ears does its best work not at the implementation stage, but at the
very beginning, when you try to imagine how what you want to do and what
a computer is capable of doing can best be brought together.
### Recursion {#Heading6}
Euclid's algorithm lends itself to recursion beautifully, so much so
that an implementation like Listing 10.3 comes almost without thought.
Again, though, take a moment to stop and consider what's really going
on, at the assembly language level, in Listing 10.3. There's recursion
and then there's recursion; code recursion and data recursion, to be
exact. Listing 10.3 is code recursion—recursion through calls—the sort
most often used because it is conceptually simplest. However, code
recursion tends to be slow because it pushes parameters and calls a
subroutine for every iteration. Listing 10.4, which uses data recursion,
is much faster and no more complicated than Listing 10.3. Actually, you
could just say that Listing 10.4 uses a loop and ignore any mention of
recursion; conceptually, though, Listing 10.4 performs the same
recursive operations that Listing 10.3 does.
**LISTING 10.4 L10-4.C**
```c
/* Finds and returns the greatest common divisor of two integers.
Uses Euclid's algorithm: divides the larger integer by the
smaller; if the remainder is 0, the smaller integer is the GCD,
otherwise the smaller integer becomes the larger integer, the
remainder becomes the smaller integer, and the process is
repeated. Avoids code recursion. */
unsigned int gcd(unsigned int int1, unsigned int int2) {
unsigned int temp;
/* Swap if necessary to make sure that int1 >= int2 */
if (int1 < int2) {
temp = int1;
int1 = int2;
int2 = temp;
}
/* Now loop, dividing int1 by int2 and checking the remainder,
until the remainder is 0. At each step, if the remainder isn't
0, assign int2 to int1, and the remainder to int2, then
repeat */
for (;;) {
/* If the remainder of int1 divided by int2 is 0, then int2 is
the gcd */
if ((temp = int1 % int2) == 0) {
return(int2);
}
/* Make int2 the larger integer and the remainder the
smaller integer, and repeat the process */
int1 = int2;
int2 = temp;
}
}
```
#### Patient Optimization {#Heading7}
At long last, we're ready to optimize GCD determination in the classic
sense. Table 10.1 shows the performance of Listing 10.4 with and without
Microsoft C/C++'s maximum optimization, and also shows the performance
of Listing 10.5, an assembly language version of Listing 10.4. Sure, the
optimized versions are faster than the unoptimized version of Listing
10.4—but the gains are small compared to those realized from the
higher-level optimizations in Listings 10.2 through 10.4.
**LISTING 10.5 L10-5.ASM**
```nasm
; Finds and returns the greatest common divisor of two integers.
; Uses Euclid's algorithm: divides the larger integer by the
; smaller; if the remainder is 0, the smaller integer is the GCD,
; otherwise the smaller integer becomes the larger integer, the
; remainder becomes the smaller integer, and the process is
; repeated. Avoids code recursion.
;
;
;
; C near-callable as:
; unsigned int gcd(unsigned int int1, unsigned int int2);
; Parameter structure:
parms struc
dw ? ;pushed BP
dw ? ;pushed return address
int1 dw ? ;integers for which to find
int2 dw ? ; the GCD
parms ends
.model small
.code
public _gcd
align 2
_gcd proc near
push bp ;preserve caller's stack frame
mov bp,sp ;set up our stack frame
push si ;preserve caller's register variables
push di
;Swap if necessary to make sure that int1 >= int2
mov ax,int1[bp]
mov bx,int2[bp]
cmp ax,bx ;is int1 >= int2?
jnb IntsSet ;yes, so we're all set
xchg ax,bx ;no, so swap int1 and int2
IntsSet:
; Now loop, dividing int1 by int2 and checking the remainder, until
; the remainder is 0. At each step, if the remainder isn't 0, assign
; int2 to int1, and the remainder to int2, then repeat.
GCDLoop:
;if the remainder of int1 divided by
; int2 is 0, then int2 is the gcd
sub dx,dx ;prepare int1 in DX:AX for division
div bx ;int1/int2; remainder is in DX
and dx,dx ;is the remainder zero?
jz Done ;yes, so int2 (BX) is the gcd
;no, so move int2 to int1 and the
; remainder to int2, and repeat the
; process
mov ax,bx ;int1 = int2;
mov bx,dx ;int2 = remainder from DIV
;—start of loop unrolling; the above is repeated three times—
sub dx,dx ;prepare int1 in DX:AX for division
div bx ;int1/int2; remainder is in DX
and dx,dx ;is the remainder zero?
jz Done ;yes, so int2 (BX) is the gcd
mov ax,bx ;int1 = int2;
mov bx,dx ;int2 = remainder from DIV
;—
sub dx,dx ;prepare int1 in DX:AX for division
div bx ;int1/int2; remainder is in DX
and dx,dx ;is the remainder zero?
jz Done ;yes, so int2 (BX) is the gcd
mov ax,bx ;int1 = int2;
mov bx,dx ;int2 = remainder from DIV
;—
sub dx,dx ;prepare int1 in DX:AX for division
div bx ;int1/int2; remainder is in DX
and dx,dx ;is the remainder zero?
jz Done ;yes, so int2 (BX) is the gcd
mov ax,bx ;int1 = int2;
mov bx,dx ;int2 = remainder from DIV
;—end of loop unrolling—
jmp GCDLoop
align2
Done:
mov ax,bx ;return the GCD
pop di ;restore caller's register variables
pop si
pop bp ;restore caller's stack frame
ret
_gcd endp
end
```
Assembly language optimization is pattern matching on a local scale.
Frankly, it's also the sort of boring, brute-force work that people are
lousy at; compilers could out-optimize you at this level with one pass
tied behind their back *if* they knew as much about the code you're
writing as you do, which they don't.
> ![](images/i.jpg)
> Design optimization—conceptual breakthroughs in understanding the
> relationships between the needs of an application, the nature of the
> data the application works with, and what the computer can do—is global
> pattern matching.
Computers are *much* worse at that sort of pattern matching than humans;
computers have no way to integrate vast amounts of disparate
information, much of it only vaguely defined or subject to change.
People, oddly enough, are *better* at global optimization than at local
optimization. For one thing, it's more interesting. For another, it's
complex and imprecise enough to allow intuition and inspiration, two
vastly underrated programming tools, to come to the fore. And, as I
pointed out earlier, people tend to perform instantaneous solutions to
even the most complex problems, while computers bog down in
geometrically or exponentially increasing execution times. Oh, it may
take days or weeks for a person to absorb enough information to be able
to reach a solution, and the solution may only be near-optimal—but the
solution itself (or, at least, each of the pieces of the solution)
arrives in a flash.
Those flashes are your programming pattern matcher doing its job. *Your*
job is to give your pattern matcher the opportunity to get to know each
problem and run through it two or three times, from different angles, to
see what unexpected solutions it can come up with.
Pull back the reins a little. Don't measure progress by lines of code
written today; measure it instead by overall progress and by quality.
Relax and listen to that quiet inner voice that provides the real
breakthroughs. Stop, look, listen—and think. Not only will you find that
it's a more productive and creative way to program—but you'll also find
that it's more fun.
And think what you could do with all those extra computer years!