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EN
The paper presents a simulation and rendering model of three dimensional covective cloud evolution. The model is physically based, however its purpose is graphical. The main stress is put on balancing two parts of a model: the atmsphere simulation with convective motion of air and water vapor combined with rendering of semi-transparent and light-scattering clouds, in order to achieve realistic animation in real-time. We examine and compare two algorithmic approaches based on CPU and GPU computations.
2
Content available remote Monte Carlo Simulations of the Ising Model on GPU
EN
Monte Carlo simulations of two- and three-dimensional Ising model on graphic cards (GPU) are described. The standard Metropolis algorithm has been employed. In the framework of the implementation developed by us, simulations were up to 100 times faster than their sequential CPU analogons. It is possible to perform simulations for systems containing up to 109 spins on Tesla C2050 GPU. As a physical application, higher cumulants for the 3d Ising model have been calculated.
3
Content available remote DNA sequence assembly involving an acyclic graph model
EN
The problem of DNA sequence assembly is well known for its high complexity. Experimental errors of different kinds present in data and huge sizes of the problem instances make this problem very hard to solve. In order to deal with such data, advanced efficient heuristics must be constructed. Here, we propose a new approach to the sequence assembly problem, modeled as the problem of searching for paths in an acyclic digraph. Since the graph representing an assembly instance is not acyclic in general, it is heuristically transformed into the acyclic form. This approach reduces the time of computations significantly and allows to maintain high quality of produced solutions.
EN
In this paper we tackle the problem of approximation and visualization of invariant measures arising from Iterated Function Systems with Probabilities (IFSP) and Recurrent Iterated Function Systems (RIFS) on R³. The measures are generated during the evolution of a stochastic dynamical system, which is a random process commonly known as the chaos game. From the dynamical system viewpoint, an invariant measure gives a temporal information on the long-term behavior of the chaos game related to a given IFSP or RIFS. The non-negative number that the measure takes on for a given subset of space says how often the dynamical system visits that subset during the temporal evolution of the system as time tends to infinity. In order to approximate the measures, we propose a method of measure instancing that can be considered an analogue of object instancing for IFS attractors. Although the IFSP and RIFS invariant measures are generated by the long-term behavior of stochastic dynamical systems, measure instancing makes it possible to compute the value that the measure takes on for a given subset of space in a deterministic way at any accuracy required. To visualize the data obtained with the algorithm, we use direct volume rendering. To incorporate the global structure of invariant measures along with their local properties in an image, a modification of a shading model based on varying density emitters is used. We adapt the model to match the fractal measure context. Then we show how to implement the model on commodity graphics hardware using an approach that combines GPU-based direct volume raycasting and 3D texture slicing used in the object-aligned manner. By means of the presented techniques, visual exploration of 3D IFSP and RIFS measures can be carried out efficiently at interactive frame rates.
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