An
Approach to
very fast
Mandelbrot
Iterations
Hi once more, it's ray again and here comes my second tech
article for this issue of Alive. I've recently been inspired to
revisit the Mandelbrot iteration loop after meeting Charon /
Escape at the Paracon 7 party where he came up with the idea of
implementing a Mandelbrot fractal on his FPGA board using 16
hardwaremultiplicators to compute 8 pixels in parallel in one
pixel clock tick AFAIR (amazing stuff, he can now do a 640x480
fractal in 60Hz realtime! A note to Charon: It was really fun
chatting with you, trading ideas and learning some VHDL ;).
You probably know calculating the Mandelbrot set is a rather
complex task in terms of the algorithms time consumption on a
computer (what else). Hence calculating and drawing Mandelbrot
fractals was always (or at least has been) a common benchmark
for testing the performance and speed of a machine. Besides the
fact that the set's fringe holds an infinite amount of
fascinating patterns and shapes only limited by the accuracy of
the numbers used to draw the fractal, which is inherently
fascinating. It's always fun to tweak algorithms that people
claim to know inside out.
Let me try explaining why calculating the Mandelbrot set is so
consumptive in the world of digital computing. Here is the
sequence that is being used to decide whether a point (Cx, Cy)
is in or outside the Mandelbrot set, simple math coming up:
z(n+1) = z(n)^2 + c , c = (Cx, Cy), z(0) = 0+i*0
Note: The Julia set is computed in a very similar manner only
that c = const., z(0) = (Cx, Cy), so the whole explanation goes
for Julia fractals, too basically.
(Cx, Cy) will usually be the screen coordinates of the pixel to
be mapped rescaled to fit the section considered in our fractal.
You will start out at n=0 and iterate until z rises beyond a
maximum value (2 usually) indicating that the sequence diverges
at (Cx, Cy), i.e. the point doesn't lie inside the set. It turns
out that computing z^2 is easier due to the lack of a square
root calculation which makes 2^2 = 4 the upper limit to check
against. You can use the number of iterations (n) to colour the
pixel so it is of course necessary to limit n to your maximum
amount of colours.
Here is a bit of pseudo code of how the set is usually
calculated:
XMIN = 2.5; // the common section
XMAX = +0.8;
YMIN = 1.25;
YMAX = +1.25;
MAXABSZ = 2;
MAXITER = 32; // 32 colours
XRES = 320;
YRES = 200;
float Cx, Cy = YMIN;
for (int y=0;y<YRES;y++) {
Cx = XMIN;
for (int x=0;x<XRES;x++) {
float a=0, b=0, a_new; // z := a + i*b = 0
int n=0;
// Enter the costy iteration loop
do {
a_new = 2*a*b+Cx; // Z(n+1) = z(n)^2+c
b = a*ab*b+Cy;
a = a_new;
} while ((n++ < MAXITER1)&&(a*a+b*b < MAXABSZ*MAXABSZ));
*screen++ = n; // Write out pixel value
Cx += (XMAXXMIN)/XRES;
}
Cy += (YMAXYMIN)/YRES;
}
Now, this is as unoptimized as it gets, 3 multiplications per
pixel (assuming we are doing fixed point operations, caching the
a^2, b^2 values in dataregisters and naturally not counting the
2* which equals a shift or an add respectively).
The next step would be eliminating muls by using a+b instead
of a^2+b^2 to approximate z^2 (changing the fractals look a
bit) and expressing a^2b^2 as (a+b)*(ab) (binomial formula)
cutting the calculus to 2 multiplications per iteration. So your
loop becomes:
do {
a_new = 2*a*b+Cx;
b = (a+b)*(ab)+Cy;
a = a_new;
} while ((n++ < MAXITER1)&&(abs(a)+abs(b) < MAXABSZ*2));
Well here's and now here comes the core idea of my whole
article: Completely remove the multiplications replacing them by
square lookups. The equations resulting by the complex squaring
of z are binomials and thus ideally suited to be used in
interconnection with an old trick that was used to accelerate or
perform multiplications on machines with slow multiplications
(68000) or machines lacking a mul instruction (6502 and so on),
some math coming up again:
I. (a+b)^2 = a^2 + 2*a*b + b^2
II. => 2*a*b = (a+b)^2  a^2  b^2
III. => a*b = ((a+b)^2  a^2  b^2) / 2
That is 3 lookups, 1 add, 2 subs and a shift to perform a mul.
You can even cut it down to 2 lookups by preceding the method a
bit but this isn't the issue here.
Take a look at formula II and notice the terms 2*a*b and a^2,
b^2 which are being reused during the calculations (on the one
hand to compute b(n+1) and to compute z^2 on the other hand).
This idea enables us to make up a pretty tight iteration loop,
at least on machines where memory to register reads and
adds/subs are faster than multiplications as it is the case on
the 68000 and 68030. Working with fixed point arithmetic you can
directly renormalize your square tables, I've found that working
with 5.11 fixed point tables already gives a neat band to play
with and it costs me 64k words, but of course you can always
increase accuracy trading memory.
In a 128x128 window (16 colours) I can almost zoom into the
fractal in realtime on my TT with satisfied me enough to write
up this little tutorial and release my fractal explorer and its
source along with this issue of Alive > "filez/mandel.exp/", it
plots a rather highres set in 48 colours and is supposed to run
on both the TT and Falcon (also CT60).
Note: Computing z' length is now exact again, here's the loop:
// Fixed point arithmetic from here on
MAXINT = 65535;
FRACBITS = 11; // fractional bits
// Set up the square table
unsigned int sqr[MAXINT+1];
for (unsigned long i=0;i<=MAXINT;i++) sqr[i] = (i*i) >> FRACBITS;
int a=0, b=0; // Cx, Cy must be suiting fixed point numbers now
(...)
// Enter the optimized innerloop
for(n=0;n<MAXITER;n++) {
asqr = sqr[a]; // z > MAXABS?
bsqr = sqr[b];
if (asqr+bsqr > MAXABSZ*MAXABSZ) break;
// Chose a's and b's range carefully to avoid a carry in a+b
a = sqr[a+b]asqrbsqr+Cx;
b = asqrbsqr+Cy
}
*screen++ = n;
(...)
Have fun with it; maybe make up a Julia fractal or something. In
case you are about to extend the idea or optimize it further
please drop me a line via ray(at)tscc(dot)de, I might be
interested ;).
Keep it real, stay Atari.
Ray for Alive, 20051222
