<p>The development process for new aircraft configurations needs to be more efficient in terms of performance, cost and time to market. The potential to influence these factors is highest in early design phases. Thus, high confidence must be established in the product earlier than today. To accomplish this, the concept of virtual product development needs to be established. This implies having a mathematical representation of the product and its associated properties and functions, often obtained through numerical simulations. Building confidence in the product early in the development process through simulations postpones expensive testing and verification to later development stages when the design is more mature.</p><p>To use this in aerodynamic design will mean introducing more advanced physical modelling of the flow as well as significantly reducing the turn around time for flow solutions.</p><p>This work describes the benefit of using parallel computers for flow simulations in the aircraft design process. Reduced turn around time for flow simulations is a prerequisite for non-linear flow modelling in early design stages and a condition for introducing high-end turbulence models and unsteady simulations in later stages of the aircraft design process. The outcome also demonstrates the importance of bridging the gap between the research community and industrial applications.</p><p>The computer platforms are very important to reduce the turn around time for flow simulations. With the recent popularity of Linux–clusters it is now possible to design cost efficient systems for a specific application. Two flow solvers are investigated for parallel</p><p>performance on various clusters. Hardware and software factors influencing the efficiency are analyzed and recommendations are made for cost efficiency and peak performance.</p> / Report code: LiU-TEK-LIC-2006:27.

The development process for new aircraft configurations needs to be more efficient in terms of performance, cost and time to market. The potential to influence these factors is highest in early design phases. Thus, high confidence must be established in the product earlier than today. To accomplish this, the concept of virtual product development needs to be established. This implies having a mathematical representation of the product and its associated properties and functions, often obtained through numerical simulations. Building confidence in the product early in the development process through simulations postpones expensive testing and verification to later development stages when the design is more mature. To use this in aerodynamic design will mean introducing more advanced physical modelling of the flow as well as significantly reducing the turn around time for flow solutions. This work describes the benefit of using parallel computers for flow simulations in the aircraft design process. Reduced turn around time for flow simulations is a prerequisite for non-linear flow modelling in early design stages and a condition for introducing high-end turbulence models and unsteady simulations in later stages of the aircraft design process. The outcome also demonstrates the importance of bridging the gap between the research community and industrial applications. The computer platforms are very important to reduce the turn around time for flow simulations. With the recent popularity of Linux–clusters it is now possible to design cost efficient systems for a specific application. Two flow solvers are investigated for parallel performance on various clusters. Hardware and software factors influencing the efficiency are analyzed and recommendations are made for cost efficiency and peak performance. / Report code: LiU-TEK-LIC-2006:27.

Simulation of large-scale and complex reservoirs requires fine and detailed gridding, which involves a significant amount of memory and is computationally expensive. Nowadays, clusters of PCs and high-performance computing (HPC) centers are widely available. These systems allow parallel processing, which helps large-scale simulations run faster and more efficient. In this research project, we developed a parallel version of The University of Texas Compositional Simulator (UTCOMP). The parallel UTCOMP is capable of running on both shared and distributed memory parallel computers. This parallelization included all physical features of the original code, such as higher-order finite difference, physical dispersion, and asphaltene precipitation. The parallelization was verified for several case studies using multiple processors. The parallel simulator produces outputs required for visualizing simulation results using the S3graph visualization software. The efficiency of the parallel simulator was assessed in terms of speedup using various numbers of processors. Subsequently, we improved the coding and implementation in the simulator in order to minimize the communications between the processors to improve the parallel efficiency to carry out the simulations. To improve the efficiency of the linear solver in the simulator, we implemented three well-known high-performance parallel solver packages (SAMG, Hypre, and PETSc) in the parallel simulator. Then, the performances of the solver packages were improved in terms of the input parameters for solving large-scale reservoir simulation problems. The developed parallel simulator has expanded the capability of the original code for simulating large-scale reservoir simulation case studies. In other words, with sufficient number of processors, a field-scale simulation with a million grid cells can be performed in few hours. Several case studies are presented to show the performance of the parallel simulator. / text

High-order accurate finite difference methods have been applied to the acoustic wave equation in discontinuous media and curvilinear geometries, using the SBP-SAT method. Strict stability is shown for the 2-D wave equation with general boundary conditions. The fourth-order accurate method for the 3-D wave equation has been implemented in C and parallelized using MPI. The implementation has been verified against an analytical solution and runs efficiently on a large number of processors.

Made available in DSpace on 2014-12-17T14:56:13Z (GMT). No. of bitstreams: 1 KayoGS_DISSERT.pdf: 4975392 bytes, checksum: 5d113169a6356e5e7704aec116237caf (MD5) Previous issue date: 2013-03-25 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / This paper analyzes the performance of a parallel implementation of Coupled Simulated Annealing (CSA) for the unconstrained optimization of continuous variables problems. Parallel processing is an efficient form of information processing with emphasis on exploration of simultaneous events in the execution of software. It arises primarily due to high computational performance demands, and the difficulty in increasing the speed of a single processing core. Despite multicore processors being easily found nowadays, several algorithms are not yet suitable for running on parallel architectures. The algorithm is characterized by a group of Simulated Annealing (SA) optimizers working together on refining the solution. Each SA optimizer runs on a single thread executed by different processors. In the analysis of parallel performance and scalability, these metrics were investigated: the execution time; the speedup of the algorithm with respect to increasing the number of processors; and the efficient use of processing elements with respect to the increasing size of the treated problem. Furthermore, the quality of the final solution was verified. For the study, this paper proposes a parallel version of CSA and its equivalent serial version. Both algorithms were analysed on 14 benchmark functions. For each of these functions, the CSA is evaluated using 2-24 optimizers. The results obtained are shown and discussed observing the analysis of the metrics. The conclusions of the paper characterize the CSA as a good parallel algorithm, both in the quality of the solutions and the parallel scalability and parallel efficiency / O presente trabalho analisa o desempenho paralelo de uma implementa??o do Simulated Annealing Acoplado (CSA, do ingl?s Coupled Simulated Annealing) para otimiza??o de vari?veis cont?nuas sem restri??es. O processamento paralelo ? uma forma eficiente de processamento de informa??o com ?nfase na explora??o de eventos simult?neos na execu??o de um software. Ele surge principalmente devido ?s elevadas exig?ncias de desempenho computacional e ? dificuldade em aumentar a velocidade de um ?nico n?cleo de processamento. Apesar das CPUs multiprocessadas, ou processadores multicore, serem facilmente encontrados atualmente, diversos algoritmos ainda n?o s?o adequados para executar em arquiteturas paralelas. O algoritmo do CSA ? caracterizado por um grupo de otimizadores Simulated Annealing (SA) trabalhando em conjunto no refinamento da solu??o. Cada otimizador SA ? executado em uma ?nica thread, e essas executadas por diferentes processadores. Na an?lise de desempenho e escalabilidade paralela, as m?tricas investigadas foram: o tempo de execu??o; o speedup do algoritmo com respeito ao aumento do n?mero de processadores; e a efici?ncia na utiliza??o de elementos de processamento com rela??o ao aumento da inst?ncia do problema tratado. Al?m disso, foi verificada a qualidade da solu??o final. Para o estudo, esse trabalho analisa uma vers?o paralela do CSA e sua vers?o serial equivalente. Ambos algoritmos foram analisados sobre 14 fun??es de refer?ncia. Para cada uma dessas fun??es, o CSA ? avaliado utilizando de 2 a 24 otimizadores. Os resultados obtidos s?o exibidos e comentados observando-se as m?tricas de an?lise. As conclus?es do trabalho caracterizam o CSA como um bom algoritmo paralelo, seja na qualidade das solu??es como na escalabilidade e efici?ncia paralela

Made available in DSpace on 2014-12-17T14:56:18Z (GMT). No. of bitstreams: 1 DemetriusAMC_DISSERT.pdf: 2429364 bytes, checksum: 57aaf24560c189720b218dbca0ef1a56 (MD5) Previous issue date: 2014-01-24 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / This work presents a scalable and efficient parallel implementation of the Standard Simplex algorithm in the multicore architecture to solve large scale linear programming problems. We present a general scheme explaining how each step of the standard Simplex algorithm was parallelized, indicating some important points of the parallel implementation. Performance analysis were conducted by comparing the sequential time using the Simplex tableau and the Simplex of the CPLEXR IBM. The experiments were executed on a shared memory machine with 24 cores. The scalability analysis was performed with problems of different dimensions, finding evidence that our parallel standard Simplex algorithm has a better parallel efficiency for problems with more variables than constraints. In comparison with CPLEXR , the proposed parallel algorithm achieved a efficiency of up to 16 times better / Este trabalho apresenta uma implementa??o paralela escal?vel e eficiente do algoritmo Simplex padr?o em arquitetura de processadores multicore para resolver problemas de programa??o linear de grande escala. Apresenta-se um esquema geral explicando como foi paralelizado cada passo do algoritmo simplex padr?o, apontando pontos importantes da implementa??o paralela. Foram realizadas an?lises de desempenho atrav?s da compara??o dos tempos sequenciais utilizando o Simplex tableau e Simplex do CPLEXR da IBM. Os experimentos foram realizados em uma m?quina de mem?ria compartilhada com 24 n?cleos. A an?lise de escalabilidade foi feita com problemas de diferentes dimens?es, encontrando evid?ncias de que a implementa??o paralela proposta do algoritmo simplex padr?o tem melhor efici?ncia paralela para problemas com mais vari?veis do que restri??es. Na compara??o com CPLEXR , o algoritmo proposto paralelo obteve uma efici?ncia de at? 16 vezes maior