24,268 research outputs found
21st Century Simulation: Exploiting High Performance Computing and Data Analysis
This paper identifies, defines, and analyzes the limitations imposed on Modeling and Simulation by outmoded
paradigms in computer utilization and data analysis. The authors then discuss two emerging capabilities to
overcome these limitations: High Performance Parallel Computing and Advanced Data Analysis. First, parallel
computing, in supercomputers and Linux clusters, has proven effective by providing users an advantage in
computing power. This has been characterized as a ten-year lead over the use of single-processor computers.
Second, advanced data analysis techniques are both necessitated and enabled by this leap in computing power.
JFCOM's JESPP project is one of the few simulation initiatives to effectively embrace these concepts. The
challenges facing the defense analyst today have grown to include the need to consider operations among non-combatant
populations, to focus on impacts to civilian infrastructure, to differentiate combatants from non-combatants,
and to understand non-linear, asymmetric warfare. These requirements stretch both current
computational techniques and data analysis methodologies. In this paper, documented examples and potential
solutions will be advanced. The authors discuss the paths to successful implementation based on their experience.
Reviewed technologies include parallel computing, cluster computing, grid computing, data logging, OpsResearch,
database advances, data mining, evolutionary computing, genetic algorithms, and Monte Carlo sensitivity analyses.
The modeling and simulation community has significant potential to provide more opportunities for training and
analysis. Simulations must include increasingly sophisticated environments, better emulations of foes, and more
realistic civilian populations. Overcoming the implementation challenges will produce dramatically better insights,
for trainees and analysts. High Performance Parallel Computing and Advanced Data Analysis promise increased
understanding of future vulnerabilities to help avoid unneeded mission failures and unacceptable personnel losses.
The authors set forth road maps for rapid prototyping and adoption of advanced capabilities. They discuss the
beneficial impact of embracing these technologies, as well as risk mitigation required to ensure success
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
Parallel Graph Partitioning for Complex Networks
Processing large complex networks like social networks or web graphs has
recently attracted considerable interest. In order to do this in parallel, we
need to partition them into pieces of about equal size. Unfortunately, previous
parallel graph partitioners originally developed for more regular mesh-like
networks do not work well for these networks. This paper addresses this problem
by parallelizing and adapting the label propagation technique originally
developed for graph clustering. By introducing size constraints, label
propagation becomes applicable for both the coarsening and the refinement phase
of multilevel graph partitioning. We obtain very high quality by applying a
highly parallel evolutionary algorithm to the coarsened graph. The resulting
system is both more scalable and achieves higher quality than state-of-the-art
systems like ParMetis or PT-Scotch. For large complex networks the performance
differences are very big. For example, our algorithm can partition a web graph
with 3.3 billion edges in less than sixteen seconds using 512 cores of a high
performance cluster while producing a high quality partition -- none of the
competing systems can handle this graph on our system.Comment: Review article. Parallelization of our previous approach
arXiv:1402.328
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