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All massive data processing applications can benefit from the ever-increasing processing capacity of modern computers, which is now affordable for anyone's pocket. I will show in this article a basic performance comparison among various CPU / GPU platforms, based on the well-known Mandelbrot set and its surprising graphical representation.

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To conclude this series about programming multitasking applications, I will show the use of a simple mechanism that allows implementing asynchronous methods whose waiting times are used for the execution of other parallel tasks or events triggered by user interface controls.

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So far I have shown examples of multitasking that block the application until they finished. This is not very useful in practice. Usually, we want that the user can continue interacting with the application while the tasks are running in the background; we could want also the threads to interact with the user interface.

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In multitasking applications there is a problem when concurrent access to resources, such as files or memory, when you launch several tasks at once. Two tasks cannot write at the same time in the same memory address, it is required to make sure that some data is not modified while we are reading them or things like that. In this article I will review the tools provided by .NET Framework to deal with these issues.

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After reviewing the classes to implement the basic multitasking, in this article I will review different mechanisms that allow a synchronization between various tasks, all of them defined in the System.Threading namespace, with which you can organize the work when the Interactions between tasks require a particular order.

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With this article begins a series in which I will review the basic mechanisms provided by the .NET Framework for the implementation of multitasking applications. First of all I will show the basic classes that allow you to launch multiple processes and make a performance comparison between them.

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This is the last article of the series dedicated to the WinCA application. This application allows the edition and to execution of cellular automata. To finish, I will review the code that implements the automaton itself, using all the classes and interfaces explained in the previous articles.

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I continue explaining the basics of the WinCA application code, dedicated to the designing and execution of cellular automata. This time I will tell you about the implementation of the cells and other auxiliary classes needed to build and execute automata.

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We continue with the series dedicated to cellular automata and the WinCA application, dedicated to build and executing them. In this article I will explain the code related to the expression system that allows establishing the conditions to change from one state to another.

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I continue to comment on the source code of the WinCA program, dedicated to cellular automata. In this article I will explain the interfaces and classes with which cell states are implemented and their edition. You can use these interfaces and classes as a basis to extend the application features.

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Once reviewed the operation of the WinCA application, dedicated to cellular automata, let's see how the source code is organized. In this article I will explain the properties of cell states, and how they can be extended to add new functionalities by implementing new classes with the corresponding interfaces.

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In this article, third of the series, I continue to explain the operation of the WinCA application, devoted to the construction and execution of cellular automata. This time I will show the language used to define the transitions between the different states of the automaton cells.

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This is the second article in the series about the WinCA application, dedicated to the construction and execution of cellular automata. In this article I will show how to design the diferent states that can have the automaton cells, and the properties that can be assigned to them, using the application editors.

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Cellular automata are mathematical models used to study the evolution of complex dynamic systems by simulating the interactions over time of a large number of elements, called cells. In this series of articles I will present the WinCA application, with which you can build and run this type of objects.

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The Iterated function systems (IFS) are a simple mathematical tool for constructing fractal sets through a series of contractive affine applications. This method was developed by M.F. Barnsley in 1985. In particular, it is useful to obtain a self-similar fractal based on iteratively applying the system of functions to any set, until arriving at a good approximation of the fractal set that constitutes the attractor of the system.

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In this new article in the series on graphic characterization of time series from dynamical systems with chaotic dynamics, I will talk about a way to represent such systems in the domain of space, independently of time, the phase diagram. With this type of diagram, you can see the attractors of the system. An attractor is a point, a curve, in general, a set of points to which converge the system equations, which gives us an idea of the typical behavior of that system.

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Usually, when you perform a data analysis, you suppose that they come from a normal distribution. In fact, you perform a battery of tests to verify that this assumption is met and, otherwise, you try to modify the data so that it is satisfied. This is because most analysis techniques only work properly on normally distributed data. But there are a number of systems that present a complex dynamics where is not valid to apply this hypothesis and wherein adjusting the data only leads to distortions that invalidate the results.

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