京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23544号 / 情博第774号 / 新制||情||132(附属図書館) / 京都大学大学院情報学研究科先端数理科学専攻 / (主査)准教授 藤原 宏志, 教授 磯 祐介, 教授 田口 智清 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Over the last decades the market for financial derivatives has grown dramatically to values of global importance. With the digital automation of the markets, programs able to efficiently value financial derivatives has become key to market competitiveness and thus garnered considerable interest. This report explores the potential efficiency gains of employing modern technology in GPU computing to price financial options, using the binomial option pricing model. The model is implemented using both CPU and GPU hardware and results compared in terms of computational efficiency. According to this thesis, GPU computing can considerably improve option pricing runtimes. / Under de senaste decennierna har marknaden för finansiella derivatinstrument vuxit till värden av global betydelse. Med ökande digitalisering av marknaden har program som effektivt kan värdera derivatinstrument blivit avgörande för konkurrenskraft och därför givits avsevärt intresse. Denna rapport utforskar vilka möjliga ökningar i effektivitet som kan nås genom att använda modern teknik för GPU-beräkningar för att värdera finansiella optioner genom den binomiala optionsvärderingsmodellen. Modellen implementeras både med CPU-, och GPU-hårdvara och resultaten jämförs i termer av beräkningseffektivitet. Enligt denna studie kan GPU-beräkingar avsevärt förbättra körtider för optionsvärderingar.

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Lattice Boltzmann method (LBM) has been developed as a powerful numerical approach to simulate the complex fluid flow and heat transfer phenomena during the past two decades. As a mesoscale method based on the kinetic theory, LBM has several advantages compared with traditional numerical methods such as physical representation of microscopic interactions, dealing with complex geometries and highly parallel nature. Lattice Boltzmann method has been applied to solve various fluid behaviors and heat transfer process like conjugate heat transfer, magnetic and electric field, diffusion and mixing process, chemical reactions, multiphase flow, phase change process, non-isothermal flow in porous medium, microfluidics, fluid-structure interactions in biological system and so on. In addition, as a non-body-conformal grid method, the immersed boundary method (IBM) could be applied to handle the complex or moving geometries in the domain. The immersed boundary method could be coupled with lattice Boltzmann method to study the heat transfer and fluid flow problems. Heat transfer and fluid flow are solved on Euler nodes by LBM while the complex solid geometries are captured by Lagrangian nodes using immersed boundary method. Parallel computing has been a popular topic for many decades to accelerate the computational speed in engineering and scientific fields. Today, almost all the laptop and desktop have central processing units (CPUs) with multiple cores which could be used for parallel computing. However, the cost of CPUs with hundreds of cores is still high which limits its capability of high performance computing on personal computer. Graphic processing units (GPU) is originally used for the computer video cards have been emerged as the most powerful high-performance workstation in recent years. Unlike the CPUs, the cost of GPU with thousands of cores is cheap. For example, the GPU (GeForce GTX TITAN) which is used in the current work has 2688 cores and the price is only 1,000 US dollars. The release of NVIDIA's CUDA architecture which includes both hardware and programming environment in 2007 makes GPU computing attractive. Due to its highly parallel nature, lattice Boltzmann method is successfully ported into GPU with a performance benefit during the recent years. In the current work, LBM CUDA code is developed for different fluid flow and heat transfer problems. In this dissertation, lattice Boltzmann method and immersed boundary method are used to study natural convection in an enclosure with an array of conduting obstacles, double-diffusive convection in a vertical cavity with Soret and Dufour effects, PCM melting process in a latent heat thermal energy storage system with internal fins, mixed convection in a lid-driven cavity with a sinusoidal cylinder, and AC electrothermal pumping in microfluidic systems on a CUDA computational platform. It is demonstrated that LBM is an efficient method to simulate complex heat transfer problems using GPU on CUDA.

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General purpose graphical processing units were proven to be useful for accelerating computationally intensive algorithms. Their capability to perform massive parallel computing significantly improve performance of many algorithms. This thesis focuses on using graphical processors (GPUs) to accelerate algorithms based on adversarial search. We investigate whether or not the adversarial algorithms are suitable for single instruction multiple data (SIMD) type of parallelism, which GPU provides. Therefore, parallel versions of selected algorithms accelerated by GPU were implemented and compared with the algorithms running on CPU. Obtained results show significant speed improvement and proof the applicability of GPU technology in the domain of adversarial search algorithms.

Challenging science requirements and complex space missions are driving the need for fast and robust space trajectory design and simulation tools. The main aim of this thesis is to develop new and improved high performance algorithms and solution techniques for commonly encountered problems in astrodynamics. Five major problems are considered and their state-of-the art algorithms are systematically improved. Theoretical and methodological improvements are combined with modern computational techniques, resulting in increased algorithm robustness and faster runtime performance. The five selected problems are 1) Multiple revolution Lambert problem, 2) High-fidelity geopotential (gravity field) computation, 3) Ephemeris computation, 4) Fast and accurate sensitivity computation, and 5) High-fidelity multiple spacecraft simulation. The work being presented enjoys applications in a variety of fields like preliminary mission design, high-fidelity trajectory simulation, orbit estimation and numerical optimization. Other fields like space and environmental science to chemical and electrical engineering also stand to benefit.

Orientador: Prof. Dr. Luiz Carlos da Silva Rozante / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Ciência da Computação, 2015. / A inferência de redes de regulação gênica é um dos problemas cruciais no campo de Biologia de Sistemas. É ainda um problema em aberto, principalmente devido à alta dimensionalidade (milhares de genes) com um número limitado de amostras (dezenas), tornando difícil estimar dependências entre genes. Além do problema de estimação, outro obstáculo é a inerente complexidade computacional dos métodos de inferência de GRNs. Este trabalho teve como foco contornar problemas de desempenho de uma técnica baseada em perturbação de sinais para inferir dependências entre genes. Um dos passos principais consiste em resolver o problema da Transversal Mínima (do Inglês Hitting Set, ou HSP), o qual é NPDifícil. Existem diversas propostas para se obter soluções aproximadas ou exatas para esse problema. Uma dessas propostas consiste em um algoritmo baseado em GPU (Graphical Processing Unit) para se obter as soluções exatas do HSP. Entretanto, tal método não é escalável para GRNs de tamanho real. Foi proposto nesse trabalho, portanto, uma extensão desse algoritmo para resolver o HSP, que é capaz de lidar com conjuntos de entrada contendomilhares de variáveis, pela introdução de inovações nas estruturas de dados e um mecanismo de ordenação que permite um descarte eficiente de candidatos que não são solução do HSP. Foi provida uma implementação em CPU multi-core e em clusters de GPU. Os resultados experimentais mostraram que o uso do mecanismo de ordenação fornece speedups de até 3,5 na implementação em CPU. Além disso, utilizando uma única GPU, foi obtido um speedup adicional de até 4,7, em comparação com uma implementação multithreaded em CPU. Porfim, o uso de oito GPUs de um cluster de GPU forneceu um speedup adicional de até 6,6. Combinando todas as técnicas, foram obtidos speedups acima de 60 para a parte paralela do algoritmo. / Gene regulatory networks inference is one of the crucial problems of the Systems Biology field. It is still an open problem, mainly because of its high dimensionality (thousands of genes) with a limited number of samples (dozens), making it difficult to estimate dependenciesamong genes. Besides the estimation problem, another important hindrance is the inherent computational complexity of GRN inference methods. In this work, we focus on circumventing performance issues of a technique based on signal perturbations to infer gene dependencies. One of its main steps consists in solving the Hitting Set problem (HSP), which is NP-Hard. There are many proposals to obtain approximate or exact solutions to this problem. One of these proposals consists of a Graphical Processing Unit (GPU) based algorithm to obtain exact solutions to the HSP. However, such method is not scalable for real size GRNs. We propose an extension of the HSP algorithm to deal with input sets containing thousands of variables by introducing innovations in the data structures and a sorting scheme to allow efficient discarding of Hitting Set non-solution candidates. We provide an implementation for multi-core CPUs and GPU clusters. Our experimental results show that the usage of the sorting scheme brings speedups of up to 3.5 in the CPU implementation. Moreover, using a single GPU, we could obtain an additional speedup of up to 4.7, in comparison with the multithreaded CPU implementation. Finally, usage of eight GPUs from a GPU cluster brought an additional speedup of up to 6.6. Combining all techniques, speedups above 60 were obtained for the parallel part of the algorithm.

While tribology involves the study of friction, wear, and lubrication of interacting surfaces, the tribosurfaces are the pair of surfaces in sliding contact with a fluid (or particulate) media between them. The ubiquitous nature of tribology is evident from the usage of its principles in all aspects of life, such as the friction promoting behavior of shoes on slippery water-lubricated walkways and tires on roadways to the wear of fingernails during filing or engine walls during operations. These tribosurface interfaces, due to the small length scales, are difficult to model for contact mechanics, fluid mechanics and particle dynamics, be it via theory, experiments or computations. Also, there is no simple constitutive law for a tribosurface with a particulate media. Thus, when trying to model such a tribosurface, there is a need to calibrate the particulate media against one or more property characterizing experiments. Such a calibrated media, which is the “virtual avatar” of the real particulate media, can then be used to provide predictions about its behavior in engineering applications. This thesis proposes and attempts to validate an approach that leverages experiments and modeling, which comprises of physics-based modeling and machine learning enabled surrogate modeling, to study particulate media in two key particle matrix industries: metal powder-bed additive manufacturing (in Part II), and energy resource rock drilling (in Part III). The physics-based modeling framework developed in this thesis is called the Particle-Surface Tribology Analysis Code (P-STAC) and has the physics of particle dynamics, fluid mechanics and particle-fluid-structure interaction. The Computational Particle Dynamics (CPD) is solved by using the industry standard Discrete Element Method (DEM) and the Computational Fluid Dynamics (CFD) is solved by using finite difference discretization scheme based on Chorin's projection method and staggered grids. Particle-structure interactions are accounted for by using a state-of-the art Particle Tessellated Surface Interaction Scheme and the fluid-structure interaction is accounted for by using the Immersed Boundary Method (IBM). Surrogate modeling is carried out using back propagation neural network. The tribosurface interactions encountered during the spreading step of the powder-bed additive manufacturing (AM) process which involve a sliding spreader (rolling and sliding for a roller) and particulate media consisting of metal AM powder, have been studied in Part II. To understand the constitutive behavior of metal AM powders, detailed rheometry experiments have been conducted in Chapter 5. CPD module of P-STAC is used to simulate the rheometry of an industry grade AM powder (100-250microns Ti-6Al-4V), to determine a calibrated virtual avatar of the real AM powder (Chapter 6). This monodispersed virtual avatar is used to perform virtual spreading on smooth and rough substrates in Chapter 7. The effect of polydispersity in DEM modeling is studied in Chapter 8. A polydispersed virtual avatar of the aforementioned AM powder has been observed to provide better validation against single layer spreading experiments than the monodispersed virtual avatar. This experimentally validated polydispersed virtual avatar has been used to perform a battery of spreading simulations covering the range of spreader speeds. Then a machine learning enabled surrogate model, using back propagation neural network, has been trained to study the spreading results generated by P-STAC and provide much more data by performing regression. This surrogate model is used to generate spreading process maps linking the 3D printer inputs of spreader speeds to spread layer properties of roughness and porosity. Such maps (Chapters 7 and 8) can be used by a 3D-printer technician to determine the spreader speed setting which corresponds to the desired spread layer properties and has the maximum spread throughout. The tribosurface interactions encountered during the drilling of energy resource rocks which involve a rotary and impacting contact of the drill bit with the rock formation in the presence of drilling fluids have been studied in Part III. This problem involves sliding surfaces with fluid (drilling mud) and particulate media (intact and drilled rock particles). Again, like the AM powder, the particulate media, viz. the rock formation being drilled into, does not have a simple and a well-defined constitutive law. An index test detailed in ASTM D 5731 can be used as a characterization test while trying to model a rock using bonded particle DEM. A model to generate weak concrete-like virtual rock which can be considered to be a mathematical representation of a sandstone has been introduced in Chapter 10. Benchtop drilling experiments have been carried out on two sandstones (Castlegate sandstone from the energy rich state of Texas and Crab Orchard sandstone from Tennessee) in Chapter 11. Virtual drilling has been carried out on the aforementioned weak concrete-like virtual rock. The rate of penetration (RoP) of the drill bit has been found to be directly proportional to the weight on bit (WoB). The drilling in dry conditions resulted in a higher RoP than the one which involved the use of water as the drilling fluid. P-SATC with the bonded DEM and CFD modules was able to predict both these findings but only qualitatively (Chapter 11)