HPC - Arnold’s cat map

Moreno Marzolla

Last updated: 2023-11-03

Arnold’s cat map is a continuous chaotic function that has been studied in the ’60s by the Russian mathematician Vladimir Igorevich Arnold. In its discrete version, the function can be understood as a transformation of a bitmapped image \(P\) of size \(N \times N\) into a new image \(P'\) of the same size. For each \(0 \leq x, y < N\), the pixel of coordinates \((x,y)\) in \(P\) is mapped into a new position \(C(x, y) = (x', y')\) in \(P'\) such that

\[ x' = (2x + y) \bmod N, \qquad y' = (x + y) \bmod N \]

(“mod” is the integer remainder operator, i.e., operator % of the C language). We may assume that \((0, 0)\) is top left and \((N-1, N-1)\) is bottom right, so that the bitmap can be encoded as a regular two-dimensional C matrix.

The transformation corresponds to a linear “stretching” of the image, that is then broken down into triangles that are rearranged as shown in Figure 1.

Figure 1: Arnold’s cat map
Figure 1: Arnold’s cat map

Arnold’s cat map has interesting properties. Let \(C^k(x, y)\) be the \(k\)-th iterate of \(C\), i.e.:

\[ C^k(x, y) = \begin{cases} (x, y) & \mbox{if $k=0$}\\ C(C^{k-1}(x,y)) & \mbox{if $k>0$} \end{cases} \]

Therefore, \(C^2(x,y) = C(C(x,y))\), \(C^3(x,y) = C(C(C(x,y)))\), and so on.

If we apply \(C\) once, we get a severely distorted version of the input. If we apply \(C\) on the result, we get an even more distorted image. As we keep applying \(C\), the original image is no longer discernible. However, after a certain number of iterations, that depends on the image size \(N\) and has been proved to never exceed \(3N\), we get back the original image! (Figure 2).

Figure 2: Some iterations of the cat map
Figure 2: Some iterations of the cat map

The minimum recurrence time for an image is the minimum positive integer \(k \geq 1\) such that \(C^k(x, y) = (x, y)\) for all \((x, y)\). The minimum recurrence time is the minimum number of iterations of the cat map that produce the starting image. For example, the minimum recurrence time for cat1368.pgm of size \(1368 \times 1368\) is \(36\).

The minimum recurrence time depends on the image size \(N\). No closed formula is known to compute the minimum recurrence time given the image size \(N\), although there are results and bounds that apply to specific cases.

You are given a serial program that computes the \(k\)-th iterate of Arnold’s cat map on a square image. The program reads the input from standard input in PGM (Portable GrayMap) format. The results is printed to standard output in PGM format. For example:

    ./omp-cat-map 100 < cat1368.pgm > cat1368-100.pgm

applies the cat map \(k=100\) times on cat1368.phm and saves the result to cat1368-100.pgm.

To display a PGM image you might need to convert it to a different format, e.g., JPEG. Under Linux you can use convert from the ImageMagick package:

    convert cat1368-100.pgm cat1368-100.jpeg

Modify the function cat_map() to make use of shared-memory parallelism using OpenMP. You might want to take advantage from the fact that Arnold’s cat map is invertible, and this implies that any two different points \((x_1, y_1)\) and \((x_2, y_2)\) are always mapped to different points \((x'_1, y'_1) = C(x_1, y_1)\) and \((x'_2, y'_2) = C(x_2, y_2)\). Therefore, the output image \(P'\) can be filled up in parallel without race conditions (however, see below for some caveats).

To compile:

    gcc -std=c99 -Wall -Wpedantic -fopenmp omp-cat-map.c -o omp-cat-map

To execute:

    ./omp-cat-map k < input_file > output_file


    ./omp-cat-map 100 < cat1368.pgm > cat1368-100.pgm


The provided function cat_map() is based on the following template:

for (i=0; i<k; i++) {
        for (y=0; y<N; y++) {
                for (x=0; x<N; x++) {
                        (x', y') = C(x, y);
                        P'(x', y') = P(x, y);
        P = P';

The two inner loops build \(P'\) from \(P\); the outer loop applies this transformation \(k\) times, using the result of the previous iteration as the source image. Therefore, the outer loop can not be parallelized (the result of an iteration is used as input for the next one). Therefore, in the version above you can either:

  1. Parallelize the y loop only, or

  2. Parallelize the x loop only, or

  3. Parallelize both the y and x loops using the collapse(2) clause.

(I suggest to try options 1 and/or 3. Option 2 does not appear to be efficient in practice: why?).

We can apply the loop interchange transformation to rewrite the code above as follows:

for (y=0; y<N; y++) {
        for (x=0; x<N; x++) {
                xcur = x; ycur = y;
                for (i=0; i<k; i++) {
                        (xnext, ynext) = C(xcur, ycur);
                        xcur = xnext;
                        ycur = ynext;
                P'(xnext, ynext) = P(x, y)

This version can be understood as follows: the two outer loops iterate over all pixels \((x, y)\). For each pixel, the inner loop computes the target position \((\mathit{xnext}, \mathit{ynext}) = C^k(x,y)\) that the pixel of coordinates \((x, y)\) will occupy after \(k\) iterations of the cat map.

In this second version, we have the following options:

  1. Parallelize the outermost loop on y, or

  2. Parallelize the middle loop on x, or

  3. Parallelize the two outermost loops with the collapse(2) directive.

(I suggest to try option c).

Intuitively, we might expect that (c) performs better than (3), because:

Interestingly, this does not appear to be the case (at least, not on every processor). Table 1 shows the execution time of two versions of the cat_map() function (“No loop interchange” refers to option (3); “Loop interchange” refers to option (c)). The program has been compiled with:

    gcc -O0 -fopenmp omp-cat-map.c -o omp-cat-map

(-O0 prevents the compiler fro making code transformations that might alter the functions too much) and executed as:

    ./omp-cat-map 2048 < cat1368.pgm > /dev/null

Each measurement is the average of five independent executions.

Table 1: Execution time (in seconds; lower is better) of the command ./omp-cat-map 2048 < cat1368.pgm > /dev/null using all processor cores, with different implementations of the cat map iteration.
Processor Cores GHz GCC version No loop interchange Loop interchange
Intel Xeon E3-1220 4 3.5 11.4.0 6.84 12.90
Intel Xeon E5-2603 12 1.7 9.4.0 6.11 7.74
Intel i7-4790 4+4 3.6 9.4.0 6.05 5.89
Intel i7-9800X 8+8 3.8 11.4.0 2.25 2.34
Intel i5-11320H 4+4 4.5 9.4.0 3.94 4.01
Intel Atom N570 2+2 1.6 7.5.0 128.69 92.47
Raspberry Pi 4 4 1.5 8.3.0 27.10 27.24

On some platforms (Intel i5, i7 and Raspberry Pi 4) there is little or no difference between the two versions. Loop interchange provides a significant performance boost on the very old Intel Atom N570 processor, but provides worse performance on the Xeon processors.

To probe further

What is the minimum recurrence time of image cat1024.pgm of size \(1024 \times 1024\)? To answer this question we need to iterate the cat map and stop as soon as we get back the initial image.

It turns out that there is a smarter way, that does not even require an input image but only its size \(N\). To understand how it works, let us suppose that we know that one particular pixel of the image, say \((x_1, y_1)\), has minimum recurrence time 15. This means that after 15 iterations, the pixel at coordinates \((x_1, y_1)\) will return to its starting position. Suppose that another pixel of coordinates \((x_2, y_2)\) has minimum recurrence time 21. How many iterations of the cat map are required to have both pixels back to their original positions?

The answer is \(105\), which is the least common multiple (LCM) of 15 and 21. From this observation we can devise the following algorithmn for computing the minimum recurrence time of an image of size \(N \times N\). Let \(T(x,y)\) be the minimum recurrence time of the pixel of coordinates \((x, y)\), \(0 \leq x, y < N\). Then, the minimum recurrence time of the whole image is the least common multiple of all \(T(x, y)\).

omp-cat-map-rectime.c contains an incomplete skeleton of a program that computes the minimum recurrence time of a square image of size \(N \times N\). Complete the program and then produce a parallel version using the appropriate OpenMP directives.

Table 2 shows the minimum recurrence time for some \(N\).

Table 2: Minimum recurrence time for some image sizes \(N\)
\(N\) Minimum recurrence time
64 48
128 96
256 192
512 384
1368 36

Figure 3 shows the minimum recurrence time as a function of \(N\). Despite the fact that the actual values jump, there is a clear tendency to align along straight lines.

Figure 3: Minimum recurrence time as a function of the image size N
Figure 3: Minimum recurrence time as a function of the image size \(N\)