Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

Mapping large-scale FEM-graphs to highly parallel computers with grid-like topology by self-organization

We consider the problem of mapping large scale FEM graphs for the solution of partial differential equations to highly parallel distributed memory computers. Typically, these programs show a low-dimensional grid-like communication structure. We argue that conventional domain decomposition methods that are usually employed today are not well suited for future highly parallel computers as they do not take into account the interconnection structure of the parallel computer resulting in a large communication overhead. Therefore we propose a new mapping heuristic which performs both, partitioning of the solution domain and processor allocation in one integrated step. Our procedure is based on the ability of Kohonen neural networks to exploit topological similarities of an input space and a grid-like structured network to compute a neighborhood preserving mapping between the set of discretization points and the parallel computer. We report about results of mapping up to 44,000-node FEM graphs to a 4096-processor parallel computer and demonstrate the capability of the proposed scheme for dynamic remapping considering adaptive refinement of the discretization graph

A new load balancing heuristic using self-organizing maps

Ankara : The Department of Computer Engineering and Information Science and the Institute of Engineering and Science of Bilkent Univ., 1999.Thesis (Master's) -- Bilkent University, 1999.Includes bibliographical references leaves 68-71.Atun, MuratM.S

Computational methods and software systems for dynamics and control of large space structures

Two key areas of crucial importance to the computer-based simulation of large space structures are discussed. The first area involves multibody dynamics (MBD) of flexible space structures, with applications directed to deployment, construction, and maneuvering. The second area deals with advanced software systems, with emphasis on parallel processing. The latest research thrust in the second area involves massively parallel computers

Data Mining in Internet of Things Systems: A Literature Review

The Internet of Things (IoT) and cloud technologies have been the main focus of recent research, allowing for the accumulation of a vast amount of data generated from this diverse environment. These data include without any doubt priceless knowledge if could correctly discovered and correlated in an efficient manner. Data mining algorithms can be applied to the Internet of Things (IoT) to extract hidden information from the massive amounts of data that are generated by IoT and are thought to have high business value. In this paper, the most important data mining approaches covering classification, clustering, association analysis, time series analysis, and outlier analysis from the knowledge will be covered. Additionally, a survey of recent work in in this direction is included. Another significant challenges in the field are collecting, storing, and managing the large number of devices along with their associated features. In this paper, a deep look on the data mining for the IoT platforms will be given concentrating on real applications found in the literatur