1,321 research outputs found
Analysis of nonlinear behavior of loudspeakers using the instantaneous frequency:Abstracts of papers
Simulation and analysis of adaptive routing and flow control in wide area communication networks
This thesis presents the development of new simulation and analytic models for the performance analysis of wide area communication networks. The models are used to analyse adaptive routing and flow control in fully connected circuit switched and sparsely connected packet switched networks. In particular the performance of routing algorithms derived from the L(_R-I) linear learning automata model are assessed for both types of network. A novel architecture using the INMOS Transputer is constructed for simulation of both circuit and packet switched networks in a loosely coupled multi- microprocessor environment. The network topology is mapped onto an identically configured array of processing centres to overcome the processing bottleneck of conventional Von Neumann architecture machines. Previous analytic work in circuit switched work is extended to include both asymmetrical networks and adaptive routing policies. In the analysis of packet switched networks analytic models of adaptive routing and flow control are integrated to produce a powerful, integrated environment for performance analysis The work concludes that routing algorithms based on linear learning automata have significant potential in both fully connected circuit switched networks and sparsely connected packet switched networks
An MPI-based 2D data redistribution library for dense arrays
In HPC, data redistributions (reorganizations) are used in parallel applications to improve performance and/or provide data-locality compatibility with sequences of parallel operations. Data reorganization refers to changing the logical and physical arrangement of data (such as dense arrays or matrices distributed over peer processes in a parallel program). This operation can be achieved by applying transformations such as transpositions or rotations or changing how data is mapped across the process grid P by Q, all of which are accomplished either with message passing or distributed shared memory operations. In this project, we restrict ourselves to a distributed memory model and message passing, not a shared-memory model, nor do we use distributed shared memory APIs. Our primary goal is to generate a library capable of diverse data reorganizations. We aim to develop a high-level Application Programming Interface (API) that directly works with the Message Passing Interface (MPI) to accomplish data redistributions in data-parallel applications and libraries, such as the polymath library, a library of many algorithms all of which implement parallel dense matrix multiplication algorithms with flexible data layouts. Using the reorganization mode of process grid shapes with constant total processes, we plan to observe the performance trends of the polymath dense parallel matrix-multiplication library (which is related research) based on different grid shapes, problem sizes, numbers of processes and decide whether it is more efficient to redistribute data or not to achieve the highest performance. We will test other redistributions for some of the process shapes used with the polymath library to identify how redistribution impacts performance. These tests will provide us with the information to determine if redistribution is worthwhile for non-optimal process layout (as established with the polymath system). Besides changing the shape of the data in terms of its layout on a logical process topology, we also plan to study and demonstrate data transpose algorithms in this library, another useful redistribution mechanism for dense linear algebra in distributed-memory parallel computing
Attitude determination by Kalman filtering, volume I
Satellite attitude determination by simulation program using Monte Carlo sampling and Kalman-Schmidt filte
Hypergraph Partitioning in the Cloud
The thesis investigates the partitioning and load balancing problem which has many applications in High Performance Computing (HPC). The application to be partitioned is described with a graph or hypergraph. The latter is of greater interest as hypergraphs, compared to graphs, have a more general structure and can be used to model more complex relationships between groups of objects such as non-symmetric dependencies. Optimal graph and hypergraph partitioning is known to be NP-Hard but good polynomial time heuristic algorithms have been proposed.
In this thesis, we propose two multi-level hypergraph partitioning algorithms. The algorithms are based on rough set clustering techniques. The first algorithm, which is a serial algorithm, obtains high quality partitionings and improves the partitioning cut by up to 71\% compared to the state-of-the-art serial hypergraph partitioning algorithms. Furthermore, the capacity of serial algorithms is limited due to the rapid growth of problem sizes of distributed applications. Consequently, we also propose a parallel hypergraph partitioning algorithm. Considering the generality of the hypergraph model, designing a parallel algorithm is difficult and the available parallel hypergraph algorithms offer less scalability compared to their graph counterparts. The issue is twofold: the parallel algorithm and the complexity of the hypergraph structure. Our parallel algorithm provides a trade-off between global and local vertex clustering decisions. By employing novel techniques and approaches, our algorithm achieves better scalability than the state-of-the-art parallel hypergraph partitioner in the Zoltan tool on a set of benchmarks, especially ones with irregular structure.
Furthermore, recent advances in cloud computing and the services they provide have led to a trend in moving HPC and large scale distributed applications into the cloud. Despite its advantages, some aspects of the cloud, such as limited network resources, present a challenge to running communication-intensive applications and make them non-scalable in the cloud. While hypergraph partitioning is proposed as a solution for decreasing the communication overhead within parallel distributed applications, it can also offer advantages for running these applications in the cloud. The partitioning is usually done as a pre-processing step before running the parallel application. As parallel hypergraph partitioning itself is a communication-intensive operation, running it in the cloud is hard and suffers from poor scalability. The thesis also investigates the scalability of parallel hypergraph partitioning algorithms in the cloud, the challenges they present, and proposes solutions to improve the cost/performance ratio for running the partitioning problem in the cloud.
Our algorithms are implemented as a new hypergraph partitioning package within Zoltan. It is an open source Linux-based toolkit for parallel partitioning, load balancing and data-management designed at Sandia National Labs. The algorithms are known as FEHG and PFEHG algorithms
RIACS
Topics considered include: high-performance computing; cognitive and perceptual prostheses (computational aids designed to leverage human abilities); autonomous systems. Also included: development of a 3D unstructured grid code based on a finite volume formulation and applied to the Navier-stokes equations; Cartesian grid methods for complex geometry; multigrid methods for solving elliptic problems on unstructured grids; algebraic non-overlapping domain decomposition methods for compressible fluid flow problems on unstructured meshes; numerical methods for the compressible navier-stokes equations with application to aerodynamic flows; research in aerodynamic shape optimization; S-HARP: a parallel dynamic spectral partitioner; numerical schemes for the Hamilton-Jacobi and level set equations on triangulated domains; application of high-order shock capturing schemes to direct simulation of turbulence; multicast technology; network testbeds; supercomputer consolidation project
Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions
Welcome to ROBOTICA 2009. This is the 9th edition of the conference on Autonomous Robot Systems and Competitions, the third time with IEEE‐Robotics and Automation Society Technical Co‐Sponsorship. Previous editions were held since 2001 in Guimarães, Aveiro, Porto, Lisboa, Coimbra and Algarve. ROBOTICA 2009 is held on the 7th May, 2009, in Castelo Branco , Portugal.
ROBOTICA has received 32 paper submissions, from 10 countries, in South America, Asia and Europe. To evaluate each submission, three reviews by paper were performed by the international program committee. 23 papers were published in the proceedings and presented at the conference. Of these, 14 papers were selected for oral presentation and 9 papers were selected for poster presentation. The global acceptance ratio was 72%.
After the conference, eighth papers will be published in the Portuguese journal Robótica, and the best student paper will be published in IEEE Multidisciplinary Engineering Education Magazine.
Three prizes will be awarded in the conference for: the best conference paper, the best student paper and the best presentation. The last two, sponsored by the IEEE Education Society ‐ Student Activities Committee.
We would like to express our thanks to all participants. First of all to the authors, whose quality work is the essence of this conference. Next, to all the members of the international program committee and reviewers, who helped us with their expertise and valuable time. We would also like to deeply thank the invited speaker, Jean Paul Laumond, LAAS‐CNRS France, for their excellent contribution in the field of humanoid robots. Finally, a word of appreciation for the hard work of the secretariat and volunteers.
Our deep gratitude goes to the Scientific Organisations that kindly agreed to sponsor the Conference, and made it come true.
We look forward to seeing more results of R&D work on Robotics at ROBOTICA 2010, somewhere in Portugal
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