## Algorithm description

The main idea behind XaoS is that it is not required to calculate the whole image every frame. Most pixels are already calculated in the previous frames. You usually don't have exactly the pixels you want, but all within a range lower than a step between pixels are acceptable. That is why the image flickers a bit and why points do not blink randomly as in precalculated animations. This document describes some of the most important algorithms in XaoS @itemize @bullet @item Saving Previous Pixels @item Approximation Algorithm @item Moving Pixels to New Positions @item Calculating New Pixels @item Symmetry @item Calculation of Mandelbrot Set @item Dynamic Resolution @item Autopilot @end itemize

### Saving Previous Pixels

Ideally, all precalculated points should be saved and used for building successive frames. I could not figure out a practical way to implement this. To save all frames for half an hour would require 24 Mb of memory, and searching the saved frames would be more computationally expensive than recalculating an entirely new frame.

One way was later used by program Frang. It remembers all pixels as x,y and value fields and when it builds new image, it draws all pixels to it and then browses image and fills it by new pixels. Possibly some rle cache should be used for calculated pixels. Frang actually uses algorithm, that takes away pixels out of screen, so it behaves exactly in same way as algorihm described here. At the other hand, this method seems to require much more memory than XaoS algorithm and drawing pixels/browsing image cost quite a lot, so algorithm descibed here seems to be faster. Since it never requires examining of whole image and new image is constructed using block move operations.

For this reason only the last generated frame is used as reference. This way the memory requirements are proportional to xsize * ysize. It can be shown that this method is only about 2-5% slower during zooming. Of course unzooming back to once browsed areas is much slower. Because only the previous frame is used, another optimization can be performed: Imaginary and real parts of the calculated image are not precise since they are the result of successive iterations of same algorithm. In order to prevent errors from being distributed to the following frames their exact coordinates need to be known. Fortunately, it isn't neccassary to save their values since it is known that all real components in a row and all imaginary components in a column are equal. Thus, the only things that must be saved are the real components for every row and the imaginary components for every column. This allows for a substantial speed-up in approximation because the calculation requires less data. Of course, some rows and columns fall out of the threshold and new ones need to be calculate to fill in the gaps in the frame. Obviously, much less work is done. There are only xsize + ysize calculations instead of xsize * ysize. So the main loop in XaoS looks like this: @itemize @bullet @item Make approximations for rows @item Make approximations for columns @item Move old pixels to their new positions @item Calculate pixels for which there is no good approximation for their row @item Calculate pixels for which ther is not good approcimation for their column but there is one for their row @end itemize

### Approximation Algorithm

@unnumberedsubsec Introduction to problem You can see that the approximation algorithm is central to the implementation of XaoS. If the guess is incorrect the image will look strange, boundaries will not be smooth and the zoom will flicker. On the other hand, if it adds more new rows or columns than required, zooming will become much slower. Also, in the instance of doubling (i.e., using an old row or column more than once) the resolution will lower. It is important to keep the increasing imaginary and real components in the correct order. If a row and column of complex coordinates follows one with higher coordinate values an improved approximation can be attained by swapping their values. The algorithm needs to be relatively fast. It is only used for xsize + ysize values but if its speed is proportional to O(n^2), it can be slower than a whole recalculation of the image. Speeds of O(n) or O(n * log(n)) are acceptable. @unnumberedsubsec Some simple algorithms to solve it Initially, a very simple algorithm was used: Find the old row/column nearest the row/column that needs to be regenerated. If the difference between them is less than one step (step = (end - beginning) / resolution) then use it. Otherwise, recalculate a new one. Finding the nearest row/column pair is very simple since it is always greater or equal to the pair needing to be generated. Surprisingly, this simple algorithm has almost all the problems described above. Doubling was fixed by lowering the limit to step / 2. This cause a considerable slowdown so the limit was returned to step. Instead, the algorithm was changed to search for only row/column pairs that are greater than the previous frame's row/column pairs. This is the algorithm that was used in version 1.0 This algorithm still added to many new rows and columns and did not generate smooth boundaries. For version 1.1 a heuristic was added that preferred approximating rows/columns with lower values. This way it did not occupy possible rows/columns for the next approximation. The result was a speedup by a magnitude of four. In versions 1.1 to 2.0 many improvements were made to the heuristic to give it added performance. The following example tries to explain how complicated the problem is (O is the old coordinates and X is the values to be approximated):

```        X1        X2        X3        X4        X5        X6        X7
O1 O2                    O3 O4 O5                   O6 O7 O8
```

The normal algorithm will aproximate X1 by O2, X3 by O4 but nothing more. For the algorithm with threshold step instead of step / 2:

```  O2 to X1
O3 to X2
O4 to X3
O5 to X4
O6 to X5
O8 to X6
```

But this will fail with X7. The second algorithm which relies on lower values will do the following:

```  O1 to X1
O3 to X2
O4 to X3
O5 to X4
O6 to X5
O7 to X6
O8 to X7
```

O1 to X1 is wrong. And there is many and many other situations that may occur. But you may see that the normal algorithm will calculate 4 new rows/columns but the heuristic saves all of these calculations. @unnumberedsubsec Current algorithms used In version 2.1 work on this heuristic was disabled after I discovered a suprisingly simple algorithm that solves all these problems. First I decided to define exactly what is best aproximation. This should be done by defining a price for every aproximation and choose the aproximation with the lowest price. Prices are defined as such: Aproximating row/column x by y costs dist(x, y) ^ 2. This prefers two smaller approximation errors before a single larger error and describes my goal quite well. The cost for adding a new row/column specifies when it is better to do a bad approximation and when to add a new row/column. I use (4 * step) * (4 * step). This means that the approximation is acceptable when dist(x, y) < 4 * step. Otherwise, adding a new row/column costs less. Now the best approximation is known. All that is required is a fast algorithm to do this. Surprisingly, this is possible in linear time using a relatively simple dynamic algorithm. It uses approximations of length < n to make a guess at the length of n. It can start by approximating one row/column and then again for two, three up to xsize/ysize rows/columns. The algorithm starts by calculating prices for all possible new positions for old row/column 1. Because of the pricing there are maximally 8 new positions. (Other ones must cost more than adding new row/column). Of course it is possible that there are no new positions.

For calculating the price of aproximations for row/column 2 I may use previous one: Try new position n. Calculate the price and add the best aproximation for the previous (row/column 1) one that uses a new position lower than n(prohibits doubling or swapping). This shoud be one of 8 positions or eventually adding of new one and not using row/column 1 at all.

The same method can be used for the rest of the rows/columns. At the end the best price may be found for the last row/column and return by the way it was calculated. (For this I need the saved "calculated using" values.) At this step the best approximation has been determined.

To fill the table, 9 * n steps are required and n steps to backtrack best aproximation. The only problem is that this algorithm is still a little slow (chiefly because of slow memory access on Intel architectures). -But with some optimizing it works well.

This algorithm is almost perfect except that it occaisonally adds new rows/columns to the wrong locations. It does not prefer to add new rows/columns into holes. But it does not seem that this is the real problem. The last optimization made was based upon the face that added rows/columns do not have the exact real and imaginary components calculated by (beginning + x * step) but lies at the average of left and right neighbours. This makes the boundaries smooth and distributes coordinates better. It also has the added benefit of making the input better for future approximations.

Another danger during implementation if this algorithm is that adding new rows/columns into their idead possitions should cause missordered results, since some rows/columns should be off more that is distance between them. To avoid this, I use algorithm that always examine start and end of block of new rows/columns and linearly interpolates value between them. Special care needs to be at the blocks that start at the begining or overs at the end.

Implementation should be much faster using custom fixedpoint routines --- first recalculate values that 0 means start of image and 65536 means end. Than calculation is much cleaner. Values <0 and >65536 are of screen, calculation is independent at scale and many thinks should be precalculated -- like tables for calculating price from distance. Also dividing main loops into many specialized parts and avoiding filing unnecesay parts of table helps. So current algorithm in XaoS is about 5 or 6 times faster than first naive implementation.

### Moving Pixels to New Positions

Since XaoS is using the approximation algorithm the following table is filled for every row/column: @itemize @bullet @item calculate @item oldpoint @item position @end itemize calculate is 1 if the current row/column is new and needs to be calculated or 0 if no old pixels need to be moved. oldpoint is a pointer to the old row/column that corresponds to the new one. This pixel needs to be copied to the new location. position is the real and imaginary components of the coordinates used for future approximations. Because almost all points will be moved, the solution seems to be simple: for every new point look at the row and column table; copy it if required.

There is the problem that this minimally needs three memory reads for every pixel (read calculate, oldpoint and index of old point). This is too slow, so a small optimization is performed. Instead rewriting the piece of code in assembly, normal memcpy is used to move blocks of pixels to their new locations. This minimizes the internal loop and access can be done more quickly since memcpy is usually optimized for each architecture.

Using the row table, a list of blocks to move for every row is created. With this new table all the pixels can be moved quickly. This increased the speed of XaoS about four times and made this function so fast that it is no longer a problem. (In fact, it takes much less processing than all other parts of XaoS.)

### Calculating New Pixels

The above optimizations make XaoS very fast, but another 30% increase in speed is aquired by using a clever method for calculating the new pixels. Many methods are known for saving calculations during the generation of fractal images. The most powerful is boundary detection. It relies on the fact that the Mandelbrot Set is connected with lakes. You need only one pixel at the boundary, then traverse the whole set and then fill the solid area inside. This method saves many calculations but is too complex for adding just one line. Many claim that it does not introduce any errors, but this is not true. It is possible for a connected part of the lake to be so small that it is not visible in smaller resolutions. In this case, boundary detection misses the whole area. This algorithm is acutually used just for calculating of new images (i.e. at the startup).

XaoS uses modification of method known as solid guessing. The pixels at the boundaries of a rectangle are calculated. If they are all the same you may assume that this rectangle does not does not contain anything and fill it.

This algorithm is further modified to operate on added lines. For this it is at least as good as boundary detection and produces more tangible errors. When adding a single line, the upper and lower line may be examined for the nearest three pixels. If they are all the same then it is assumed that 9x9 pixels are the same. This disables all calculations inside solid areas and calculates as many points as boundary detection. The only possibility of creating a larger error with this method as opposed to boundary detection is in the instance that the shape of the set is so sharp that it does not set any of the tested points but comes from the right (i.e., uncalculated) location. This situation is not very common.

Later, rules were added for new rows and columns that crossed each other. In this instance you can test only four pixels. This situation is very rare. It is hoped that it does not introduce many errors.

If multiple blocks of new lines need to be calculated there are not reference pixels to use for solid guessing. Interlacing does the trick. By calculating the odd lines without any guessing, the guessing algorithm is now possible for the remaining uncalculated lines. This simple trick saves about 30% of the calculation of the main Mandelbrot image.

A similar approximation can also be done for the X coordinate. This makes it possible to improve solid guessing at even pixels because all surrounding pixels are available, further reducing errors.

### Symmetry

Many fractals are horizontally or vertically symmetrical. This is implemented in the approximation code. When there is no good approximation available, try to mirror the opposite side if the line is available.

This method primarily speeds up the initial image.

### Calculation of the Mandelbrot Set

Internal Mandelbrot caluclation loop is unrolled -- it calculates first 8 iterations using normal method and then it expects that number of iterations will be probably large so it switches into mode, where it calcualtes iteratins in block of 8 with one bailout test at the end. When bailout is received, saved values from previous iterations is restored and last 8 iterations are recalcualted slowly to get exact values. This leps a lot especially at pentium, where conditionals in floating point code is slow.

Another stuff is periodicity checking. XaoS has both version of loops --- with ans without periodicity checks. In most cases it uses nonperiodicity checking version. Periodicity check version is used just in case, some inside set pixel has beed found during solid guessing paste around. This is done mainly because periodicity checking verson of loop is significnatly slower.

### Dynamic Resolution

The above optimizations often do not help enough and image calculation is still too slow. One option was to reduce the framerate, but a framerate lower than 5 frames per second is unbearable. Another option is simply to calculate only the details that can be determined within a time interval.

Rows/columns not calculated are simple approximated by referencing the nearest other row/column. The result is an image with larger pixels. One problem is the fact that the order of calculating the rows/columns is significant. Previous versions of XaoS simply calculated all rows from top to bottom and then columns from left to right. Using the dynamic resolution code with this algorithm would result in distorted images. This was solved by adding priority to every row/column and calculating the high priority row/column first. The algorithm for adding these priorities is as follows:

@item Find middle row/column of uncalculated block. Priority is the size of the block (in floating point coordinates) @item Start function for left block and right block

This function produces quite good results. It tends to make same size rectangles on the whole image and does not depend on resolution.

Another interesting optimization is that during the zoom it is more advantageous to calculate rows/columns in the center of the zoom instead of the borders since these will be in the viewport longer and the user is usually focusing on center of the zoom anyhow.

This is done by simply adding to the calculated priority normal_priority / (abs(newposition - oldposition) / step + 1). This prefers rows/columns that do not move a great deal. Of course, unzooming uses the formula reversed.

The last variable to consider is the time interval for one frame. Setting it too low makes the calculation slow. Setting it too high makes the framerate too low. So the amount of time spent in other parts of the program is calculated and multiplied by 5 to determine the interval. If time is then lower than 15FPS, 15FPS is used instead, since slower animations are unacceptable. At the other hand if it is higher than 35FPS, it is set to 35FPS, since higher framerate is just wasting of computer resources. When image is not animating, this values is changed, so framerate is choosed between 5FPS and 15FPS. This caused that images are calcualted quickly after zooms stops.

### Autopilot

Another interesting algorithm controls the autopilot. It is actually quite simple. Interesting parts are found at the boundaries of the set. It randomly looks around and zooms to the first area containing both outside and inside set points. Some fractals (such as the Newton) do not have points inside the set at all. In this case it selects a point where many (more than 2) different colors are around. (i.e., It zooms into noisy areas.)

In the instance that there are no such areas, the autopilot will unzoom. Also detects oscilating.

Current implementation also does detection of out of range numbers and randomly choosed points are choosed near the old one, to avoid too often changes of direction.

### SMP support

Since version 3.0 XaoS suports SMP. This is done using threads. Most of XaoS routines should be threaded easily -- for example moveoldpoints just divides image into n equal part and each part is proceded by one processor. Only unthreaded part is realloc table calculation routines. I don't see any way to paraelize it except it calculates both - x and y approximation at one time (using two processors). Another interesting algorithm to paraelize is boundary trace. See comments `btrace.c` for discusion about current implementation. Only problem of current implementation I see is possibility, that calculation is divided into too many parts (realloc tables, move points, calcualte, symetries, dynamic resolution) and tasks needs to synchronize between each part. So this should be too slow at real SMP box.