Search for dissertations about: "high-performance computing implementation"

Showing result 1 - 5 of 32 swedish dissertations containing the words high-performance computing implementation.

2. 1. High Performance Adaptive Finite Element Methods : With Applications in Aerodynamics

   Author : Niclas Jansson; Johan Hoffman; Johan Jansson; Vincent Heuveline; KTH; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES;

   Abstract : The massive computational cost for resolving all scales in a turbulent flow makes a direct numerical simulation of the underlying Navier-Stokes equations impossible in most engineering applications. Recent advances in adaptive finite element methods offer a new powerful tool in Computational Fluid Dynamics (CFD). READ MORE

4. 2. Design of High Performance Computing Software for Genericity and Variability

   Author : Malin Ljungberg; Michael Thuné; Kurt Otto; Hans Petter Langtangen; Uppsala universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; PDE solver; high-performance; coordinate invariant; curvilinear coordinates; symmetry exploiting; generalized Fourier transform; finite difference; expression templates; feature modeling; variability; Beräkningsvetenskap; Scientific Computing;

   Abstract : Computer simulations have emerged as a cost efficient complement to laboratory experiments, as computers have become increasingly powerful. The aim of the present work is to explore the ideas of some state of the art software development practices, and ways in which these can be useful for developing high performance research codes. READ MORE

5. 3. High-Performance Finite Element Methods : with Application to Simulation of Diffusion MRI and Vertical Axis Wind Turbines

   Author : Van-Dang Nguyen; Johan Hoffman; Johan Jansson; Axel Målqvist; KTH; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; High performance finite element method; computational diffusion MRI; turbulent flow; vertical axis wind turbine.; Computer Science; Datalogi;

   Abstract : The finite element methods (FEM) have been developed over decades, and together with the growth of computer engineering, they become more and more important in solving large-scale problems in science and industry. The objective of this thesis is to develop high-performance finite element methods (HP-FEM), with two main applications in mind: computational diffusion magnetic resonance imaging (MRI), and simulation of the turbulent flow past a vertical axis wind turbine (VAWT). READ MORE

6. 4. High-Performance Network-on-Chip Design for Many-Core Processors

   Author : Boqian Wang; Zhonghai Lu; Kun-Chih Chen; KTH; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Network-on-Chip; Chip Multi Many-core Processors; Multiprocessor System-on-Chip; High-Performance Computing; Cache Coherence; Virtual Channel Reservation; Admission Control; Artificial Neural Network; AXI4; Quality of Service; Network-on-Chip; Chip Multi Many-core Processors; Multiprocessor Sys-tem on a Chip; High-Performance Computing; Cache Coherence; Virtual Channel Reser-vation; Admission Control; Artificial Neural Network; AXI4; Quality of Servic; Informations- och kommunikationsteknik; Information and Communication Technology;

   Abstract : With the development of on-chip manufacturing technologies and the requirements of high-performance computing, the core count is growing quickly in Chip Multi/Many-core Processors (CMPs) and Multiprocessor System-on-Chip (MPSoC) to support larger scale parallel execution. Network-on-Chip (NoC) has become the de facto solution for CMPs and MPSoCs in addressing the communication challenge. READ MORE

7. 5. High-Performance Computing For Support Vector Machines

   Author : Shirin Tavara; Alexander Schliep; Alexander Karlsson; Richard Johansson; Högskolan i Skövde; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; Skövde Artificial Intelligence Lab SAIL ; Skövde Artificial Intelligence Lab SAIL ; INF301 Data Science; INF301 Data Science;

   Abstract : Machine learning algorithms are very successful in solving classification and regression problems, however the immense amount of data created by digitalization slows down the training and predicting processes, if solvable at all. High-Performance Computing(HPC) and particularly parallel computing are promising tools for improving the performance of machine learning algorithms in terms of time. READ MORE