Synthesis of feedback control law for stabilization of chaotic system oscillations by means of analytic programming - Preliminary study

This research deals with a synthesis of control law for selected discrete chaotic system - logistic equation by means of analytic programming. The novelty of the approach is that a tool for symbolic regression - analytic programming - is used for the purpose of stabilization of higher periodic orbits - oscillations between several values of chaotic system. The paper consists of the descriptions of analytic programming as well as used chaotic system and detailed proposal of cost function used in optimization process. For experimentation, Self-Organizing Migrating Algorithm (SOMA) with analytic programming and Differential evolution (DE) as second algorithm for meta-evolution were used

Vytěžování znalostí z rozsáhlých astronomických datasetů masivně paralelním přístupem

Import 03/11/2016The aim of thesis is survey of the field of processing large data via parallel accesses and subsequent implementation of one algorithm and its parallelization on the chosen architecture. My task is to create an application that will be able to synthesize the model light spectrum of Be star, using this expression to calculate the necessary values for comparison with spectra of unknown astronomical objects. For the purposes of synthesis I use analytic programming, a method of symbolic regression, using evolutionary algorithms. Comparison of spectra is implemented on a multicore processor architecture.Cílem diplomové práce je průzkum oblasti zpracovávání velkých dat paralelními přístupy a následná implementace jednoho algoritmu a jeho paralelizace na zvolené architektuře. Mým úkolem je vytvoření aplikace, která bude schopna syntetizovat světelné spektrum vzorové BE hvězdy, pomocí toho výrazu vypočítat potřebné hodnoty pro porovnání se spektry neznámých astronomických objektů. K účelům syntézy používám analytické programování, jednu z metod symbolické regrese, využívající evolučních algoritmů. Srovnávání spekter je realizováno na multiprocesorové architektuře.460 - Katedra informatikyvelmi dobř

Architectures and GPU-Based Parallelization for Online Bayesian Computational Statistics and Dynamic Modeling

Recent work demonstrates that coupling Bayesian computational statistics methods with dynamic models can facilitate the analysis of complex systems associated with diverse time series, including those involving social and behavioural dynamics. Particle Markov Chain Monte Carlo (PMCMC) methods constitute a particularly powerful class of Bayesian methods combining aspects of batch Markov Chain Monte Carlo (MCMC) and the sequential Monte Carlo method of Particle Filtering (PF). PMCMC can flexibly combine theory-capturing dynamic models with diverse empirical data. Online machine learning is a subcategory of machine learning algorithms characterized by sequential, incremental execution as new data arrives, which can give updated results and predictions with growing sequences of available incoming data. While many machine learning and statistical methods are adapted to online algorithms, PMCMC is one example of the many methods whose compatibility with and adaption to online learning remains unclear. In this thesis, I proposed a data-streaming solution supporting PF and PMCMC methods with dynamic epidemiological models and demonstrated several successful applications. By constructing an automated, easy-to-use streaming system, analytic applications and simulation models gain access to arriving real-time data to shorten the time gap between data and resulting model-supported insight. The well-defined architecture design emerging from the thesis would substantially expand traditional simulation models' potential by allowing such models to be offered as continually updated services. Contingent on sufficiently fast execution time, simulation models within this framework can consume the incoming empirical data in real-time and generate informative predictions on an ongoing basis as new data points arrive. In a second line of work, I investigated the platform's flexibility and capability by extending this system to support the use of a powerful class of PMCMC algorithms with dynamic models while ameliorating such algorithms' traditionally stiff performance limitations. Specifically, this work designed and implemented a GPU-enabled parallel version of a PMCMC method with dynamic simulation models. The resulting codebase readily has enabled researchers to adapt their models to the state-of-art statistical inference methods, and ensure that the computation-heavy PMCMC method can perform significant sampling between the successive arrival of each new data point. Investigating this method's impact with several realistic PMCMC application examples showed that GPU-based acceleration allows for up to 160x speedup compared to a corresponding CPU-based version not exploiting parallelism. The GPU accelerated PMCMC and the streaming processing system can complement each other, jointly providing researchers with a powerful toolset to greatly accelerate learning and securing additional insight from the high-velocity data increasingly prevalent within social and behavioural spheres. The design philosophy applied supported a platform with broad generalizability and potential for ready future extensions. The thesis discusses common barriers and difficulties in designing and implementing such systems and offers solutions to solve or mitigate them

GPU-based implementation of real-time system for spiking neural networks

Real-time simulations of biological neural networks (BNNs) provide a natural platform for applications in a variety of fields: data classification and pattern recognition, prediction and estimation, signal processing, control and robotics, prosthetics, neurological and neuroscientific modeling. BNNs possess inherently parallel architecture and operate in continuous signal domain. Spiking neural networks (SNNs) are type of BNNs with reduced signal dynamic range: communication between neurons occurs by means of time-stamped events (spikes). SNNs allow reduction of algorithmic complexity and communication data size at a price of little loss in accuracy. Simulation of SNNs using traditional sequential computer architectures results in significant time penalty. This penalty prohibits application of SNNs in real-time systems. Graphical processing units (GPUs) are cost effective devices specifically designed to exploit parallel shared memory-based floating point operations applied not only to computer graphics, but also to scientific computations. This makes them an attractive solution for SNN simulation compared to that of FPGA, ASIC and cluster message passing computing systems. Successful implementations of GPU-based SNN simulations have been already reported. The contribution of this thesis is the development of a scalable GPU-based realtime system that provides initial framework for design and application of SNNs in various domains. The system delivers an interface that establishes communication with neurons in the network as well as visualizes the outcome produced by the network. Accuracy of the simulation is emphasized due to its importance in the systems that exploit spike time dependent plasticity, classical conditioning and learning. As a result, a small network of 3840 Izhikevich neurons implemented as a hybrid system with Parker-Sochacki numerical integration method achieves real time operation on GTX260 device. An application case study of the system modeling receptor layer of retina is reviewed

PERFORMANCE ANALYSIS AND FITNESS OF GPGPU AND MULTICORE ARCHITECTURES FOR SCIENTIFIC APPLICATIONS

Recent trends in computing architecture development have focused on exploiting task- and data-level parallelism from applications. Major hardware vendors are experimenting with novel parallel architectures, such as the Many Integrated Core (MIC) from Intel that integrates 50 or more x86 processors on a single chip, the Accelerated Processing Unit from AMD that integrates a multicore x86 processor with a graphical processing unit (GPU), and many other initiatives from other hardware vendors that are underway. Therefore, various types of architectures are available to developers for accelerating an application. A performance model that predicts the suitability of the architecture for accelerating an application would be very helpful prior to implementation. Thus, in this research, a Fitness model that ranks the potential performance of accelerators for an application is proposed. Then the Fitness model is extended using statistical multiple regression to model both the runtime performance of accelerators and the impact of programming models on accelerator performance with high degree of accuracy. We have validated both performance models for all the case studies. The error rate of these models, calculated using the experimental performance data, is tolerable in the high-performance computing field. In this research, to develop and validate the two performance models we have also analyzed the performance of several multicore CPUs and GPGPU architectures and the corresponding programming models using multiple case studies. The first case study used in this research is a matrix-matrix multiplication algorithm. By varying the size of the matrix from a small size to a very large size, the performance of the multicore and GPGPU architectures are studied. The second case study used in this research is a biological spiking neural network (SNN), implemented with four neuron models that have varying requirements for communication and computation making them useful for performance analysis of the hardware platforms. We report and analyze the performance variation of the four popular accelerators (Intel Xeon, AMD Opteron, Nvidia Fermi, and IBM PS3) and four advanced CPU architectures (Intel 32 core, AMD 32 core, IBM 16 core, and SUN 32 core) with problem size (matrix and network size) scaling, available optimization techniques and execution configuration. This thorough analysis provides insight regarding how the performance of an accelerator is affected by problem size, optimization techniques, and accelerator configuration. We have analyzed the performance impact of four popular multicore parallel programming models, POSIX-threading, Open Multi-Processing (OpenMP), Open Computing Language (OpenCL), and Concurrency Runtime on an Intel i7 multicore architecture; and, two GPGPU programming models, Compute Unified Device Architecture (CUDA) and OpenCL, on a NVIDIA GPGPU. With the broad study conducted using a wide range of application complexity, multiple optimizations, and varying problem size, it was found that according to their achievable performance, the programming models for the x86 processor cannot be ranked across all applications, whereas the programming models for GPGPU can be ranked conclusively. We also have qualitatively and quantitatively ranked all the six programming models in terms of their perceived programming effort. The results and analysis in this research indicate and are supported by the proposed performance models that for a given hardware system, the best performance for an application is obtained with a proper match of programming model and architecture

Machine learning on deep neural networks and object tracking applied to motion of airplanes

The aim of this project is to understand the concepts underlying machine learning and how to implement those. To achieve this purpose, an exhaustive study of the origins of this technology has been made, describing the most popular types of neural networks, their history, and the architectures and subsequent implementations. Three implementations of neural networks are presented, using world-known datasets. In the last implementation, an exhaustive study has been realized to achieve the best performance algorithm taking into account different settings. In the second part of the project, Detectron2 has been used, an advanced machine learning program that performs object detection. We have worked with this program and executed a study of the motion of moving airplanes, implementing a new method to track objects given a set of images extracted from a given video

Improvement of Metaheuristic Algorithms Using Symbolic Regression

Jelikož na poli optimalizace stále dochází k vývoji a mnohým výzkumům, je cílem této práce nalezení zlepšení metaheuristických algoritmů SOMA, PSO a DE prostřednictvím analytického programování. Proto se tato práce v prvních částech zabývá rozborem těchto metaheuristických algoritmů a jsou zde také popsány principy analytického programování, jež bylo využito jako metoda symbolické regrese. Všechny algoritmy byly posléze implementovány v jazyce C++ pro účely experimentů, jejichž výsledky jsou poté prezentovány a vyhodnoceny v závěrečných částech. Zlepšení optimalizačních schopností se podařilo nalézt především u algoritmu SOMA. U algoritmů PSO a DE došlo ke zlepšení u vybraných testovacích funkcí.Optimization methods are still under development, and researchers are working on the improvement of current methods. The purpose of this thesis is to find an improvement of three metaheuristic algorithms - SOMA, PSO, and DE. The analytic programming is used as a method of symbolic regression for this purpose. The beginning of this thesis consists of descriptions of SOMA, PSO, and DE, as well as of analytic programming. All algorithms were implemented in the C++ programming language and experiments were performed. The results are evaluated at the end of this thesis. Significant improvement was found for the SOMA algorithm. For PSO and DE, improvements were observed for some of the objective functions.460 - Katedra informatikyvýborn

Rheology and Structure Formation in Complex Polymer Melts

Polymeric materials are ubiquitous in our modern lives. Their many applications in complex materials are accompanied by potentially huge benefits for technological advancement. These applications range from batteries, fuel cells, molecular sieves, tires, and microelectronic devices. The ability to self-assemble into nanostructures in combination with their viscoelastic properties make polymers attractive for this wide range of applications. I perform computer simulations gaining knowledge about their properties for applications and manufacturing, to improve the understanding of these materials. The simulation of multicomponent polymer melts poses an extreme computational challenge. The large spatial extent of defects in self-assembled structures or nonperiodic metastable phases, which are prone to finite size effects, require the study of large system sizes. Hence, I use a soft, coarse-grained polymer model reducing the degrees of freedom to gain insights into long time and length scales. Consistent implementations of these models that scale well on modern GPUs accelerated HPCs hardware enable investigations with up to billions of particles. Consequently, I can address challenges that were deemed intractable before. Firstly, I analyze metastable network phases as a function of the volume fraction, f, of diblock copolymers for polymeric battery electrolytes. One polymer block provides the mechanical stability while the other is ion conducting. The focus lies on the structure of the conducting phase. Due to the trapped metastable states, I investigate systems of extreme sizes with billions of particles circumventing finite size effects. In fact, I identify fractal structures on significant length scales inside the network phase, which influence the transport properties locally. As such, this work highlights the necessity of soft models and scaling implementations obtaining insights on engineering scales. Secondly, I will investigate the simulation of viscoelastic properties of polymeric materials with soft, coarse-grained models. It is particularly challenging to correctly capture the entangled dynamics. The noncrossability of polymer backbones introduces topological constraints on the motion of the chains. A soft, coarse-grained model does not capture this noncrossability automatically. Hence, I utilize a SLSP model to mimic the entanglements via dynamic bonds. With this model and a novel technique to average the stress auto-correlation function G(t), I perform a dynamic mechanical analysis of polymer melts and a cross-linked network. The obtained storage modulus G'(w) and loss modulus G''(w) meet the expectations for a comparison with experimental studies. A nonequilibrium study of diblock copolymers in shear flow completes this work. Shear flow is a powerful method to macroscopically order a metastable microstructure. In a symmetric diblock copolymer melt, the equilibrium microstructure is a lamellar phase. The first step determines the perpendicular orientation of the lamellae in shear flow as stable at all stresses according to the concept of the Rayleighian, R. Further, I study the transition between a grain in the unstable orientation next to a grain in the stable orientation. I identify two different transition pathways. At low applied stresses, the grain boundary of the stable grain grows into the unstable grain. At higher stresses, the unstable orientation is destabilized and forms an intermediate microemulsion-like phase with no local orientation. This intermediate phase turns subsequently into the stable orientation. Oscillatory shear at high frequencies delays the onset of this microemulsion pathway. In a collaboration with Matthias Heck and Manfred Wilhelm at KIT, these transitions have been studied in LAOS experiments as well

Gramatická evoluce v jazyce nezávislém na výpočetní platformě

Import 05/08/2014The thesis aims on implementation of grammar evolution in a platform-independent language. Grammar evolution is an advanced optimization technique from a field of evolutionary algorithms. Its range of applications is very wide and covers many engineering, economical and other fields of science. The thesis is divided into three parts. The first one is rather theoretical and describes the principles of grammar evolution and evolutionary algorithms in general. The second part describes my own implementations of grammar evolution and the experiments for which I have used them. The last part concludes achieved results and also proposes the ways of further research of the topic.Předmětem této diplomové práce je implementace gramatické evoluce v jazyce nezávislém na platformě. Gramatická evoluce je pokročilá optimalizační technika, která spadá do oblasti evolučních algoritmů. Možnosti jejího využití jsou široké a pokrývají spoustu odvětví inženýrských, ekonomických a dalších oborů. Práce je rozdělena do tří částí. První část je teoretická a pojednává o principech gramatické evoluci a evolučních algoritmů obecně. Druhá část popisuje mé implementace programů, které jsem v rámci této diplomové práce vytvořil a experimenty, které jsem s nimi prováděl. Poslední část obsahuje shrnutí této práce a nabízí možnosti dalšího vývoje.460 - Katedra informatikyvýborn