10 research outputs found
Extending Perfect Spatial Hashing to Index Tuple-based Graphs Representing Super Carbon Nanotubes
In this paper, we demonstrate how to extend perfect spatial hashing (PSH) in order to hash multidimensional scientific data. As a use case we employ the problem domain of indexing nodes in a graph that represents Super Carbon Nanotubes (SCNTs). The goal of PSH is to hash multidimensional data without collisions. Since PSH results from the research on computer graphics, its principles and methods have only been tested on 2- and 3-dimensional problems. In our case, we need to hash up to 28 dimensions. In contrast to the original applications of PSH, we do not focus on GPUs as target hardware but on an efficient CPU implementation. Thus, this paper highlights the extensions to the original algorithm to make it suitable for higher dimensions. Comparing the compression and performance results of the new PSH based graphs and a structure-tailored custom data structure in our parallelized SCNT simulation software, we find that PSH in some cases achieves better compression by a factor of 1.7 while only increasing the total runtime by several percent. In particular, after our extension, PSH can also be employed to index sparse multidimensional scientific data from other domains where PSH can avoid additional index-structures like KD- or R-trees
Enhanced Living Environments
This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area
Generalized averaged Gaussian quadrature and applications
A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal
MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications
Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described
Memory-Efficient and Parallel Simulation of Super Carbon Nanotubes
Carbon nanotubes (CNTs) received much attention since their description in Nature in 1991.
In principle, a carbon nanotube is a rolled up sheet of graphene, which can be imagined as a
honeycomb grid of carbon atoms. This allotrope of carbon has many interesting properties like
high tensile strength at very low weight or its high temperature resistance. This motivates the
application of CNTs in material science to create new carbon nanotube enforced materials. They
also possess interesting electronic properties since CNTs show either metallic or semiconducting
behavior, depending on their configuration.
The synthesis of branched carbon nanotubes allows the connection of straight CNTs to carbon
nanotubes networks with branched tubes employed as junction elements. One of these networks
are the so-called super carbon nanotubes (SCNTs) that were proposed in 2006. In that case,
each carbon-carbon bond within the honeycomb grid is replaced by a CNT of equal size and
each carbon atom by a Y-branched tube with three arms of equal length and a regular angle of
120° between the arms. This results in a structure that originates from tubes and regains the
outer shape of a tube. It is also possible to repeat this process, replacing carbon-carbon bonds
not with CNTs but with SCNTs, leading to very regular and self-similar structures of increasingly
higher orders.
Simulations demonstrate that the SCNTs also exhibit very interesting mechanical properties.
They are even more flexible than CNTs and thus are good candidates for high strength com-
posites or actuators with very low weight. Other applications arise again in microelectronics
because of their configurable electronic behavior and in biology due to the biocompatibility of
SCNTs.
Despite progress in synthesizing processes for straight and branched CNTs, the production
of SCNTs is still beyond current technological capabilities. In addition, real experiments at
nanoscale are expensive and complex and hence, simulations are important to predict properties
of SCNTs and to guide the experimental research. The atomic-scale finite element method
(AFEM) already provides a well-established approach for simulations of CNTs at the atomic
level. However, the model size of SCNTs grows very fast for larger tubes and the arising n-body
and linear equation systems quickly exceed the memory capacity of available computer systems.
This renders infeasible the simulation of large SCNTs on an atomic level, unless the regular
structure of SCNTs can be taken into account to reduce the memory footprint.
This thesis presents ways to exploit the symmetry and hierarchy within SCNTs enabling the
simulation of higher order SCNTs. We develop structure-tailored and memory-saving data struc-
tures which allow the storage of very large SCNTs models up to several billions of atoms while
providing fast data access. We realize this with a novel graph data structure called Compressed Symmetric Graphs which is able to dynamically recompute large parts of structural information
for tubes instead of storing them.
We also present a new structure-aware and SMP-parallelized matrix-free solver for the linear
equation systems involving the stiffness matrix, which employs an efficient caching mechanism
for the data during the sparse matrix-vector multiplication. The matrix-free solver is twice as
fast as a compressed row storage format-based reference solver, requiring only half the memory
while caching all contributions of the matrix employed. We demonstrate that this solver, in
combination with the Compressed Symmetric Graphs, is able to instantiate equation systems
with matrices of an order higher than 5∗10^7 on a single compute node, while still fully caching
all matrix data
Extending Perfect Spatial Hashing to Index Tuple-based Graphs Representing Super Carbon Nanotubes
In this paper, we demonstrate how to extend perfect spatial hashing (PSH) to the problem domain of indexing nodes in a graph that represents of Super Carbon Nanotubes (SCNTs). The goal of PSH is to hash multidimensional data without collisions. Since PSH results from the research on computer graphics, its principles and methods have only been tested on 2− and 3−dimensional problems. In our case, we need to hash up to 28 dimensions. In contrast to the original applications of PSH, we do not focus on GPUs as target hardware but on an efficient CPU implementation. Thus, this paper highlights the extensions to the original algorithm to make it suitable for higher dimensions and the representation of SCNTs. Comparing the compression and performance results of the new PSH based graphs and a structure-tailored custom data structure in our parallelized SCNT simulation software, we find, that PSH in some cases achieves better compression by a factor of 1.7 while only increasing the total runtime by several percent. In particular, after our extension, PSH can also be employed to index sparse multidimensional scientific data from other domains
Task Allocation in Foraging Robot Swarms:The Role of Information Sharing
Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms