The first goal of this thesis is to develop and validate a modeling tool into which fundamental combustion chemistry and aerosol dynamics theory are implemented for investigating soot formation/oxidation in multi-dimensional laminar coflow diffusion flames taking into account soot polydispersity and fractal-like aggregate structure. The second goal is to use the tool to study soot aggregate formation/oxidation in experimentally studied laminar coflow diffusion flames to advance the understanding of soot aggregate formation/oxidation mechanism. The first part of the thesis deals with the large CPU time problem when detailed models are coupled together. Using the domain decomposition method, a high performance parallel flame code is successfully developed. An advanced sectional aerosol dynamics model which can model fractal-like aggregate structure is successfully implemented into the parallel flame code. The performance of the parallel code is demonstrated through its application to the modeling of soot formation/oxidation in a laminar coflow CH4/air diffusion flame. The parallel efficiency reaches as high as 83%. The second part of the thesis numerically explores soot aggregate formation in a laminar coflow C2H4/air diffusion flame using detailed PAH-based combustion chemistry and a PAH-based soot formation/oxidation model. Compared to the measured data, flame temperature, axial velocity, C2H2 and OH concentrations, soot volume fraction, the average diameter and the number density of primary particles are reasonably well predicted. However, it is very challenging to predict effectively the average degree of particle aggregation. To do so, particle-particle and fluid-particle interactions that may cause non-unitary soot coagulation efficiency need to be considered. The original coagulation model is enhanced in this thesis to accommodate soot coagulation efficiency. Different types of soot coagulation efficiency are numerically investigated. It is found that a simple adjustment of soot coagulation efficiency from 100% to 20% provides good predictions on soot aggregate structure as well as flame properties. In the third part of the thesis, the effects of oxidation-driven soot aggregate fragmentation on aggregate structure and soot oxidation rate are studied. Three fragmentation models with different fragmentation patterns are developed and implemented into the sectional aerosol dynamics model. The implementation of oxidation-driven aggregate fragmentation significantly improves the prediction of soot aggregate structure in the soot oxidation region.

The first goal of this thesis is to develop and validate a modeling tool into which fundamental combustion chemistry and aerosol dynamics theory are implemented for investigating soot formation/oxidation in multi-dimensional laminar coflow diffusion flames taking into account soot polydispersity and fractal-like aggregate structure. The second goal is to use the tool to study soot aggregate formation/oxidation in experimentally studied laminar coflow diffusion flames to advance the understanding of soot aggregate formation/oxidation mechanism. The first part of the thesis deals with the large CPU time problem when detailed models are coupled together. Using the domain decomposition method, a high performance parallel flame code is successfully developed. An advanced sectional aerosol dynamics model which can model fractal-like aggregate structure is successfully implemented into the parallel flame code. The performance of the parallel code is demonstrated through its application to the modeling of soot formation/oxidation in a laminar coflow CH4/air diffusion flame. The parallel efficiency reaches as high as 83%. The second part of the thesis numerically explores soot aggregate formation in a laminar coflow C2H4/air diffusion flame using detailed PAH-based combustion chemistry and a PAH-based soot formation/oxidation model. Compared to the measured data, flame temperature, axial velocity, C2H2 and OH concentrations, soot volume fraction, the average diameter and the number density of primary particles are reasonably well predicted. However, it is very challenging to predict effectively the average degree of particle aggregation. To do so, particle-particle and fluid-particle interactions that may cause non-unitary soot coagulation efficiency need to be considered. The original coagulation model is enhanced in this thesis to accommodate soot coagulation efficiency. Different types of soot coagulation efficiency are numerically investigated. It is found that a simple adjustment of soot coagulation efficiency from 100% to 20% provides good predictions on soot aggregate structure as well as flame properties. In the third part of the thesis, the effects of oxidation-driven soot aggregate fragmentation on aggregate structure and soot oxidation rate are studied. Three fragmentation models with different fragmentation patterns are developed and implemented into the sectional aerosol dynamics model. The implementation of oxidation-driven aggregate fragmentation significantly improves the prediction of soot aggregate structure in the soot oxidation region.

Modern computers are capable of completing most of the tasks in fairly short time, however there are areas in what calculations can last for months and even years. Parallel algorithms are one of the ways to accelerate long-lasting calculations. In the thesis we analyze parallel computing technologies OpenMP (shared memory) and MPI (distributed memory), cons and pros of their architecture. We identify potential parts of program code of Curonian lagoon model for parallelizing, in which chosen parallel computing technologies OpenMP and MPI is applied. Also we make the runtime speedup analysis.

Egzistuoja du matematinių problemų sprendimo būdai: skaitmeninis ir simbolinis. Simbolinis sprendimo būdas manipuliuoja simboliniais objektais, tokiais kaip loginės ar algebrinės formulės, taisyklės ar programos. Priešingai nei skaitmeninis būdas, pagrindinis simbolinių skaičiavimų tikslas yra matematinės išraiškos supaprastinimas. Dažniausiai galutinis atsakymas būna racionalusis skaičius arba formulė, todėl simboliniai skaičiavimai gali būti naudojami: • surasti tikslų matematinės problemos sprendimą, • supaprastinti matematinį modelį. Nedidelės apimties matematinėms išraiškoms supaprastinti užtenka ir vieno kompiuterio, tačiau yra tokių išraiškų, kurioms supaprastinti nebeužtenka vieno kompiuterio atminties ar procesoriaus, todėl geriausias sprendimas šioje situacijoje yra lygiagretieji skaičiavimai kompiuterių klasteryje. Pagrindinė problema lygiagrečiuose skaičiavimuose yra duomenų paskirstymo algoritmo efektyvumas. Šiame darbe yra pateikti vieno iš paskirstymo algoritmų ir kelių jo modifikacijų eksperimentiniai tyrimai. / There are two methods of mathematical problems solving: the digital, and symbolic. Symbolic solutions manipulate symbolic objects, such as logical or algebraic formulas, rules or programs. In contrast to the digital solution, the main purpose of the symbolic calculations is the symbolic simplification of mathematical expressions. In most cases, the final answer is rational number, or formula, and therefore symbolic calculations can be used: (1) • to identify the precise solution of the mathematical problem, • to simplify the mathematical model. For calculation of small mathematical expression it is enough one computer. But there are expressions which need more then one computer memory capacity or processing power. In these cases best solution is parallel calculations in computer cluster. The main problem of parallel calculations is the efficiency of distribution algorithm. This work presents experimental studies of one distribution algorithm and of several it‘s modifications.

Big Data can be characterized by 3 V's. * Big Volume refers to the unprecedented growth in the amount of data. * Big Velocity refers to the growth in the speed of moving data in and out management systems. * Big Variety refers to the growth in the number of different data formats. Managing Big Data requires fundamental changes in the architecture of data management systems. Data storage should continue being innovated in order to adapt to the growth of data. They need to be scalable while maintaining high performance regarding data accesses. This thesis focuses on building scalable data management systems for Big Data. Our first and second contributions address the challenge of providing efficient support for Big Volume of data in data-intensive high performance computing (HPC) environments. Particularly, we address the shortcoming of existing approaches to handle atomic, non-contiguous I/O operations in a scalable fashion. We propose and implement a versioning-based mechanism that can be leveraged to offer isolation for non-contiguous I/O without the need to perform expensive synchronizations. In the context of parallel array processing in HPC, we introduce Pyramid, a large-scale, array-oriented storage system. It revisits the physical organization of data in distributed storage systems for scalable performance. Pyramid favors multidimensional-aware data chunking, that closely matches the access patterns generated by applications. Pyramid also favors a distributed metadata management and a versioning concurrency control to eliminate synchronizations in concurrency. Our third contribution addresses Big Volume at the scale of the geographically distributed environments. We consider BlobSeer, a distributed versioning-oriented data management service, and we propose BlobSeer-WAN, an extension of BlobSeer optimized for such geographically distributed environments. BlobSeer-WAN takes into account the latency hierarchy by favoring locally metadata accesses. BlobSeer-WAN features asynchronous metadata replication and a vector-clock implementation for collision resolution. To cope with the Big Velocity characteristic of Big Data, our last contribution feautures DStore, an in-memory document-oriented store that scale vertically by leveraging large memory capability in multicore machines. DStore demonstrates fast and atomic complex transaction processing in data writing, while maintaining high throughput read access. DStore follows a single-threaded execution model to execute update transactions sequentially, while relying on a versioning concurrency control to enable a large number of simultaneous readers.

Recently, interest in the Graphics Processing Unit (GPU) for general purpose parallel applications development and research has grown. Much of the current research on the GPU focuses on the acceleration of regular problems, as irregular problems typically do not provide the same level of performance on the hardware. We explore the potential of the GPU by investigating four problems on the GPU with regular and/or irregular properties: lookback option pricing (regular), single-source shortest path (irregular), maximum flow (irregular), and the task matching problem using multi-swarm particle swarm optimization (regular with elements of irregularity). We investigate the design, implementation, optimization, and performance of these algorithms on the GPU, and compare the results. Our results show that the regular problem achieves greater performance and requires less development effort than the irregular problems. However, we find the GPU to still be capable of providing high levels of acceleration for irregular problems.

This project implements a parallel algorithm for Computed Tomography based on the Algebraic Reconstruction Technique (ART) algorithm. This technique for reconstructing pictures from projections is useful for applications such as Computed Tomography (CT or CAT). The algorithm requires fewer views, and hence less radiation, to produce an image of comparable or better quality. However, the approach is not widely used because of its computationally intensive nature in comparison with rival technologies. A faster ART algorithm could reduce the amount of radiation needed for CT imaging by producing a better image with fewer projections. A reconstruction from projections version of the ART algorithm for two dimensions was implemented in parallel using the Message Passing Interface (MPI) and OpenMP extensions for C. The message passing implementation did not result in faster reconstructions due to prohibitively long and variant communication latency. The shared memory implementation produced positive results, showing a clear computational advantage for multiple processors and measured efficiency ranging from 60-95%. Consistent with the literature, image quality proved to be significantly better compared to the industry standard Filtered Backprojection algorithm especially when reconstructing from fewer projection angles.

The workshop ¨Portierbare numerische Simulationen auf parallelen Architekturen¨ (¨Portable numerical simulations on parallel architectures¨) was organized by the Fac- ulty of Informatics/Professorship Computer Architecture at 18 April 1996 and held in the framework of the Sonderforschungsbereich (Joint Research Initiative) ¨Numerische Simulationen auf massiv parallelen Rechnern¨ (SFB 393) (¨Numerical simulations on massiv parallel computers¨) ( http://www.tu-chemnitz.de/~pester/sfb/sfb393.html ) The SFB 393 is funded by the German National Science Foundation (DFG). The purpose of the workshop was to bring together scientists using parallel computing to provide integrated discussions on portability issues, requirements and future devel- opments in implementing parallel software efficiently as well as portable on Clusters of Symmetric Multiprocessorsystems. I hope that the present paper gives the reader some helpful hints for further discussions in this field.

This project implements a parallel algorithm for Computed Tomography based on the Algebraic Reconstruction Technique (ART) algorithm. This technique for reconstructing pictures from projections is useful for applications such as Computed Tomography (CT or CAT). The algorithm requires fewer views, and hence less radiation, to produce an image of comparable or better quality. However, the approach is not widely used because of its computationally intensive nature in comparison with rival technologies. A faster ART algorithm could reduce the amount of radiation needed for CT imaging by producing a better image with fewer projections. A reconstruction from projections version of the ART algorithm for two dimensions was implemented in parallel using the Message Passing Interface (MPI) and OpenMP extensions for C. The message passing implementation did not result in faster reconstructions due to prohibitively long and variant communication latency. The shared memory implementation produced positive results, showing a clear computational advantage for multiple processors and measured efficiency ranging from 60-95%. Consistent with the literature, image quality proved to be significantly better compared to the industry standard Filtered Backprojection algorithm especially when reconstructing from fewer projection angles.

Recently, interest in the Graphics Processing Unit (GPU) for general purpose parallel applications development and research has grown. Much of the current research on the GPU focuses on the acceleration of regular problems, as irregular problems typically do not provide the same level of performance on the hardware. We explore the potential of the GPU by investigating four problems on the GPU with regular and/or irregular properties: lookback option pricing (regular), single-source shortest path (irregular), maximum flow (irregular), and the task matching problem using multi-swarm particle swarm optimization (regular with elements of irregularity). We investigate the design, implementation, optimization, and performance of these algorithms on the GPU, and compare the results. Our results show that the regular problem achieves greater performance and requires less development effort than the irregular problems. However, we find the GPU to still be capable of providing high levels of acceleration for irregular problems.