/**
 * \page examples Examples
 *
 * There are several examples shipping with %Vc. If you have a suggestion for a useful or interesting
 * example, please contact vc@compeng.uni-frankfurt.de.
 *
 * \li \subpage ex-polarcoord
 * This example explains the basic approaches to vectorization of a given problem. It contains a
 * discussion of storage issues.
 * \li \subpage ex-finitediff
 * \li \subpage ex-matrix
 * \li \subpage ex-mandelbrot
 * \li \subpage ex-buddhabrot
 *
 *********************************************************************************
 *
 * \page ex-polarcoord Polar Coordinates
 *
 * The \c polarcoord example generates 1000 random Cartesian 2D coordinates that are then
 * converted to polar coordinates and printed to the terminal.
 * This is a very simple example but shows the concept of vertical versus horizontal
 * vectorization quite nicely.
 *
 * \section ex_polarcoord_background Background
 *
 * In our problem we start with the allocation and random initialization of 1000 Cartesian 2D
 * coordinates. Thus every coordinate consists of two floating-point values (x and y).
 * \code
 * struct CartesianCoordinate
 * {
 *   float x, y;
 * };
 * CartesianCoordinate input[1000];
 * \endcode
 * \image html polarcoord-cartesian.png "Cartesian coordinate"
 *
 * We want to convert them to 1000 polar coordinates.
 * \code
 * struct PolarCoordinate
 * {
 *   float r, phi;
 * };
 * PolarCoordinate output[1000];
 * \endcode
 * \image html polarcoord-polar.png "Polar coordinate"
 *
 * Recall that:
 * \f[
 * r^2 = x^2 + y^2
 * \f]\f[
 * \tan\phi = y/x
 * \f]
 * (One typically uses \c atan2 to calculate \c phi efficiently.)
 *
 * \section ex_polarcoord_vectorization Identify Vectorizable Parts
 *
 * When you look into vectorization of your application/algorithm, the first task is to identify the
 * data parallelism to use for vectorization.
 * A scalar implementation of our problem could look like this:
 * \code
 * for (int i = 0; i < ArraySize; ++i) {
 *   const float x = input[i].x;
 *   const float y = input[i].y;
 *   output[i].r = std::sqrt(x * x + y * y);
 *   output[i].phi = std::atan2(y, x) * 57.295780181884765625f; // 180/pi
 *   if (output[i].phi < 0.f) {
 *     output[i].phi += 360.f;
 *   }
 * }
 * \endcode
 * The data parallelism inside the loop is minimal. It basically consists of two multiplications
 * that can be executed in parallel. This kind of parallelism is already exploited by all modern
 * processors via pipelining, which is one form of instruction level parallelism (ILP).
 * Thus, if one were to put the x and y values into a SIMD vector, this one multiplication could be
 * executed with just a single SIMD instruction. This vectorization is called \e vertical
 * vectorization, because the vector is placed vertically into the object.
 *
 * There is much more data parallelism in this code snippet, though. The different iteration steps
 * are all independent, which means that subsequent steps do not depend on results of the preceding steps.
 * Therefore, several steps of the loop can be executed in parallel. This is the most
 * straightforward vectorization strategy for our problem:
 * From a loop, always execute N steps in parallel, where N is the number of entries in the SIMD vector.
 * The input values to the loop need to be placed into a vector.
 * Then all intermediate values and results are also vectors. Using the %Vc datatypes a single loop
 * step would then look like this:
 * \code
 * // x and y are of type float_v
 * float_v r = Vc::sqrt(x * x + y * y);
 * float_v phi = Vc::atan2(y, x) * 57.295780181884765625f; // 180/pi
 * phi(output[i].phi < 0.f) += 360.f;
 * \endcode
 * This vectorization is called \e horizontal vectorization, because the vector is placed
 * horizontally over several objects.
 *
 * \section ex_polarcoord_data Data Structures
 *
 * To form the \c x vector from the previously used storage format, one would thus write:
 * \code
 * float_v x;
 * for (int i = 0; i < float_v::Size; ++i) {
 *   x[i] = input[offset + i].x;
 * }
 * \endcode
 * Notice how the memory access is rather inefficient.
 *
 * \subsection ex_polarcoord_data_aos Array of Structs (AoS)
 *
 * The data was originally stored as array of
 * structs (\e AoS). Another way to call it is \e interleaved storage. That's because the entries of
 * the \c x and \c y vectors are interleaved in memory.
 * Let us assume the storage format is given and we cannot change it.
 * We would rather not load and store all our vectors entry by entry as this would lead to
 * inefficient code, which mainly occupies the load/store ports of the processor. Instead, we can use
 * a little helper function %Vc provides to load the data as vectors with subsequent deinterleaving:
 * \code
 * Vc::float_v x, y;
 * Vc::deinterleave(&x, &y, &input[i], Vc::Aligned);
 * \endcode
 * This pattern can be very efficient if the interleaved data members are always accessed together.
 * This optimizes for data locality and thus cache usage.
 *
 * \subsection ex_polarcoord_data_vectors Interleaved Vectors
 *
 * If you can change the data structures, it might be a good option to store interleaved vectors:
 * \code
 * struct CartesianCoordinate
 * {
 *   Vc::float_v x, y;
 * };
 * CartesianCoordinate input[(1000 + Vc::float_v::Size - 1) / Vc::float_v::Size];
 * \endcode
 * Accessing vectors of \c x and \c y is then as simple as accessing the members of a \c
 * CartesianCoordinate object. This can be slightly more efficient than the previous method because
 * the deinterleaving step is not required anymore. On the downside your data structure now depends
 * on the target architecture, which can be a portability concern.
 * In short, the \c sizeof operator returns different values depending on Vc::float_v::Size.
 * Thus, you would have to ensure correct conversion to target independent data
 * formats for any data exchange (storage, network). (But if you are really careful about portable
 * data exchange, you already have to handle endian conversion anyway.)
 *
 * Note the unfortunate complication of determining the size of the array. In order to fit 1000
 * scalar values into the array, the number of vectors times the vector size must be greater or equal
 * than 1000. But integer division truncates.
 *
 * Sadly, there is one last issue with alignment. If the \c CartesianCoordinate object is allocated
 * on the stack everything is fine (because the compiler knows about the alignment restrictions of
 * \c x and \c y and thus of \c CartesianCoordinate). But if \c CartesianCoordinate is allocated on
 * the heap (with \c new or inside an STL container), the correct alignment is not ensured. %Vc provides
 * Vc::VectorAlignedBase, which contains the correct reimplementations of the \c new and \c delete operators:
 * \code
 * struct CartesianCoordinate : public Vc::VectorAlignedBase
 * {
 *   Vc::float_v x, y;
 * }
 * CartesianCoordinate *input = new CartesianCoordinate[(1000 + Vc::float_v::Size - 1) / Vc::float_v::Size];
 * \endcode
 * To ensure correctly aligned storage with STL containers you can use Vc::Allocator:
 * \code
 * struct CartesianCoordinate
 * {
 *   Vc::float_v x, y;
 * }
 * VC_DECLARE_ALLOCATOR(CartesianCoordinate)
 * std::vector<CartesianCoordinate> input((1000 + Vc::float_v::Size - 1) / Vc::float_v::Size);
 * \endcode
 *
 * For a thorough discussion of alignment see \ref intro_alignment.
 *
 * \subsection ex_polarcoord_data_soa Struct of Arrays (SoA)
 *
 * A third option is storage in the form of a single struct instance that contains arrays of the
 * data members:
 * \code
 * template<size_t Size> struct CartesianCoordinate
 * {
 *   float x[Size], y[Size];
 * }
 * CartesianCoordinate<1000> input;
 * \endcode
 * Now all \c x values are adjacent in memory and thus can easily be loaded and stored as vectors.
 * Well, two problems remain:
 * 1. The alignment of \c x and \c y is not defined and therefore not guaranteed. Vector loads and
 * stores thus must assume unaligned pointers, which is bad for performance. Even worse, if an
 * instruction that expects an aligned pointer is executed with an unaligned address the program
 * will crash.
 * 2. The size of the \c x and \c y arrays is not guaranteed to be large enough to allow the last
 * values in the arrays to be loaded/stored as vectors.
 *
 * %Vc provides the Vc::Memory class to solve both issues:
 * \code
 * template<size_t Size> struct CartesianCoordinate
 * {
 *   Vc::Memory<float_v, Size> x, y;
 * }
 * CartesianCoordinate<1000> input;
 * \endcode
 *
 * \section ex_polarcoord_complete The Complete Example
 *
 * Now that we have covered the background and know what we need - let us take a look at the
 * complete example code.
 *
 * \snippet polarcoord/main.cpp includes
 * The example starts with the main include directive to use for %Vc: \c \#include \c <Vc/Vc>.
 * The remaining includes are required for terminal output.
 * Note that we include Vc::float_v into the global namespace.
 * It is not recommended to include the whole %Vc namespace into the global namespace
 * except maybe inside a function scope.
 *
 * \snippet polarcoord/main.cpp memory allocation
 * At the start of the program, the input and output memory is allocated.
 * Of course, you can abstract these variables into structs/classes for Cartesian and polar
 * coordinates.
 * The Vc::Memory class can be used to allocate memory on the stack or on the heap.
 * In this case the memory is allocated on the stack, since the size of the memory is given at
 * compile time.
 * The first \c float value of Vc::Memory (e.g. x_mem[0]) is guaranteed to be aligned to the
 * natural SIMD vector alignment.
 * Also, the size of the allocated memory may be padded at the end to allow access to the last \c
 * float value (e.g. x_mem[999]) with a SIMD vector.
 * Thus, if this example is compiled for a target with a vector width (\c Vc::float_v::Size) of 16
 * entries, the four arrays would internally be allocated as size 1008 (63 vectors with 16 entries =
 * 1008 entries).
 *
 * \snippet polarcoord/main.cpp random init
 * Next the x and y values are initialized with random numbers.
 * %Vc includes a simple vectorized random number generator.
 * The floating point RNGs in %Vc produce values in the range from 0 to 1.
 * Thus the value has to be scaled and subtracted to get into the desired range of -1 to 1.
 * The iteration over the memory goes from 0 (no surprise) to a value determined by the Vc::Memory
 * class. In the case of fixed-size allocation, this number is also available to the compiler as a
 * compile time constant. Vc::Memory has two functions to use as upper bound for iterations:
 * Vc::Memory::entriesCount and Vc::Memory::vectorsCount. The former returns the same number as was
 * used for allocation. The latter returns the number of SIMD vectors that fit into the (padded)
 * allocated memory. Thus, if Vc::float_v::Size were 16, \c x_mem.vectorsCount() would expand to 63.
 * Inside the loop, the memory i-th vector is then set to a random value.
 *
 * \warning Please do not use this RNG until you have read its documentation. It may not be as
 * random as you need it to be.
 *
 * \snippet polarcoord/main.cpp conversion
 * Finally we arrive at the conversion of the Cartesian coordinates to polar coordinates.
 * The for loop is equivalent to the one above.
 *
 * Inside the loop we first assign the x and y values to local variables.
 * This is not necessary; but it can help the compiler with optimization. The issue is that when you
 * access values from some memory area, the compiler cannot always be sure that the pointers to
 * memory do not alias (i.e. point to the same location). Thus, the compiler might rather take the
 * safe way out and load the value from memory more often than necessary. By using local variables,
 * the compiler has an easy task to prove that the value does not change and can be cached in a
 * register. This is a general issue, and not a special issue with SIMD. In this case mainly serves
 * to make the following code more readable.
 *
 *
 *
 * \snippet polarcoord/main.cpp output
 *
 *********************************************************************************
 *
 * \page ex-finitediff Finite Differences
 *
 *********************************************************************************
 *
 * \page ex-matrix Matrix Class
 *
 *********************************************************************************
 *
 * \page ex-buddhabrot Buddhabrot
 *
 *********************************************************************************
 *
 * \page ex-mandelbrot Mandelbrot
 *
 * This example draws a colorized Mandelbrot image on screen using Qt4 widgets.
 *
 * The example uses a simple class to abstract complex numbers. In principle, one could just use
 * std::complex, if it would perform well enough. But especially the norm function is very slow for
 * scalar float/double. Also, complex multiplication is correctly implemented to handle NaN and
 * infinity. This is not required for Mandelbrot as these special cases will not occur.
 * Additionally, the provided complex abstraction stores the square of the real and imaginary parts
 * to help the compiler in optimizing the code as good as possible.
 * \snippet mandelbrot/mandel.cpp MyComplex
 *
 * Mandelbrot uses the function z = z² + c for iteration.
 * \snippet mandelbrot/mandel.cpp P function
 *
 *********************************************************************************
 *
 * \page ex-buddhabrot Buddhabrot
 *
 */
