728,501 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
Parallel computing and the generation of basic plasma data
Comprehensive simulations of the processing plasmas used in semiconductor fabrication will depend on the availability of basic data for many microscopic processes that occur in the plasma and at the surface. Cross sections for electron collisions, a principal mechanism for producing reactive species in these plasmas, are among the most important such data; however, electron-collision cross sections are difficult to measure, and the available data are, at best, sketchy for the polyatomic feed gases of interest. While computational approaches to obtaining such data are thus potentially of significant value, studies of electron collisions with polyatomic gases at relevant energies are numerically intensive. In this article, we report on the progress we have made in exploiting large-scale distributed-memory parallel computers, consisting of hundreds of interconnected microprocessors, to generate electron-collision cross sections for gases of interest in plasma simulations
State-of-the-Art in Parallel Computing with R
R is a mature open-source programming language for statistical computing and graphics. Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel computing. This paper presents an overview of techniques for parallel computing with R on computer clusters, on multi-core systems, and in grid computing. It reviews sixteen different packages, comparing them on their state of development, the parallel technology used, as well as on usability, acceptance, and performance. Two packages (snow, Rmpi) stand out as particularly useful for general use on computer clusters. Packages for grid computing are still in development, with only one package currently available to the end user. For multi-core systems four different packages exist, but a number of issues pose challenges to early adopters. The paper concludes with ideas for further developments in high performance computing with R. Example code is available in the appendix
A low-cost parallel implementation of direct numerical simulation of wall turbulence
A numerical method for the direct numerical simulation of incompressible wall
turbulence in rectangular and cylindrical geometries is presented. The
distinctive feature resides in its design being targeted towards an efficient
distributed-memory parallel computing on commodity hardware. The adopted
discretization is spectral in the two homogeneous directions; fourth-order
accurate, compact finite-difference schemes over a variable-spacing mesh in the
wall-normal direction are key to our parallel implementation. The parallel
algorithm is designed in such a way as to minimize data exchange among the
computing machines, and in particular to avoid taking a global transpose of the
data during the pseudo-spectral evaluation of the non-linear terms. The
computing machines can then be connected to each other through low-cost network
devices. The code is optimized for memory requirements, which can moreover be
subdivided among the computing nodes. The layout of a simple, dedicated and
optimized computing system based on commodity hardware is described. The
performance of the numerical method on this computing system is evaluated and
compared with that of other codes described in the literature, as well as with
that of the same code implementing a commonly employed strategy for the
pseudo-spectral calculation.Comment: To be published in J. Comp. Physic
Parallel containers: a tool for applying parallel computing applications on clusters
Parallel and cluster computing remain somewhat difficult to apply quickly for many applications
domains. Recent developments in computer libraries such as the Standard Template
Library of the C++ language and the Message Passing Package associated with the Python
Language provide a way to implement very high level parallel containers in support of application
programming. A parallel container is an implementation of a data structure such as a
list, or vector, or set, that has associated with it the necessary methods and state knowledge
to distribute the contents of the structure across the memory of a parallel computer or a
computer cluster. A key idea is that of the parallel iterator which allows a single high level
statement written by the applications programmer to invoke a parallel operation across the
entire data structureâs contents while avoiding the need for knowledge of how the distribution
is actually carried out. This transparency approach means that optimised parallel algorithms
can be separated from the applications domain code, maximising reuse of the parallel computing
infrastructure and libraries. This paper describes our initial experiments with C++
parallel containers
A GPU-Computing Approach to Solar Stokes Profile Inversion
We present a new computational approach to the inversion of solar
photospheric Stokes polarization profiles, under the Milne-Eddington model, for
vector magnetography. Our code, named GENESIS (GENEtic Stokes Inversion
Strategy), employs multi-threaded parallel-processing techniques to harness the
computing power of graphics processing units GPUs, along with algorithms
designed to exploit the inherent parallelism of the Stokes inversion problem.
Using a genetic algorithm (GA) engineered specifically for use with a GPU, we
produce full-disc maps of the photospheric vector magnetic field from polarized
spectral line observations recorded by the Synoptic Optical Long-term
Investigations of the Sun (SOLIS) Vector Spectromagnetograph (VSM) instrument.
We show the advantages of pairing a population-parallel genetic algorithm with
data-parallel GPU-computing techniques, and present an overview of the Stokes
inversion problem, including a description of our adaptation to the
GPU-computing paradigm. Full-disc vector magnetograms derived by this method
are shown, using SOLIS/VSM data observed on 2008 March 28 at 15:45 UT
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