23,351 research outputs found
A grid-enabled problem solving environment for parallel computational engineering design
This paper describes the development and application of a piece of engineering software that provides a problem solving environment (PSE) capable of launching, and interfacing with, computational jobs executing on remote resources on a computational grid. In particular it is demonstrated how a complex, serial, engineering optimisation code may be efficiently parallelised, grid-enabled and embedded within a PSE.
The environment is highly flexible, allowing remote users from different sites to collaborate, and permitting computational tasks to be executed in parallel across multiple grid resources, each of which may be a parallel architecture. A full working prototype has been built and successfully applied to a computationally demanding engineering optimisation problem. This particular problem stems from elastohydrodynamic lubrication and involves optimising the computational model for a lubricant based on the match between simulation results and experimentally observed data
Steering in computational science: mesoscale modelling and simulation
This paper outlines the benefits of computational steering for high
performance computing applications. Lattice-Boltzmann mesoscale fluid
simulations of binary and ternary amphiphilic fluids in two and three
dimensions are used to illustrate the substantial improvements which
computational steering offers in terms of resource efficiency and time to
discover new physics. We discuss details of our current steering
implementations and describe their future outlook with the advent of
computational grids.Comment: 40 pages, 11 figures. Accepted for publication in Contemporary
Physic
Enhancing Energy Production with Exascale HPC Methods
High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose
processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale
simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of
Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and
from the Brazilian Ministry of Science, Technology and Innovation through Rede
Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the
Intel Corporation, which enabled us to obtain the presented experimental results in
uncertainty quantification in seismic imagingPostprint (author's final draft
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Leveraging legacy codes to distributed problem solving environments: A web service approach
This paper describes techniques used to leverage high performance legacy codes as CORBA components to a distributed problem solving environment. It first briefly introduces the software architecture adopted by the environment. Then it presents a CORBA oriented wrapper generator (COWG) which can be used to automatically wrap high performance legacy codes as CORBA components. Two legacy codes have been wrapped with COWG. One is an MPI-based molecular dynamic simulation (MDS) code, the other is a finite element based computational fluid dynamics (CFD) code for simulating incompressible Navier-Stokes flows. Performance comparisons between runs of the MDS CORBA component and the original MDS legacy code on a cluster of workstations and on a parallel computer are also presented. Wrapped as CORBA components, these legacy codes can be reused in a distributed computing environment. The first case shows that high performance can be maintained with the wrapped MDS component. The second case shows that a Web user can submit a task to the wrapped CFD component through a Web page without knowing the exact implementation of the component. In this way, a userâs desktop computing environment can be extended to a high performance computing environment using a cluster of workstations or a parallel computer
An Extensible Timing Infrastructure for Adaptive Large-scale Applications
Real-time access to accurate and reliable timing information is necessary to
profile scientific applications, and crucial as simulations become increasingly
complex, adaptive, and large-scale. The Cactus Framework provides flexible and
extensible capabilities for timing information through a well designed
infrastructure and timing API. Applications built with Cactus automatically
gain access to built-in timers, such as gettimeofday and getrusage,
system-specific hardware clocks, and high-level interfaces such as PAPI. We
describe the Cactus timer interface, its motivation, and its implementation. We
then demonstrate how this timing information can be used by an example
scientific application to profile itself, and to dynamically adapt itself to a
changing environment at run time
Performance evaluation of a distributed integrative architecture for robotics
The eld of robotics employs a vast amount of coupled sub-systems. These need to interact
cooperatively and concurrently in order to yield the desired results. Some hybrid algorithms
also require intensive cooperative interactions internally. The architecture proposed lends it-
self amenable to problem domains that require rigorous calculations that are usually impeded
by the capacity of a single machine, and incompatibility issues between software computing
elements. Implementations are abstracted away from the physical hardware for ease of de-
velopment and competition in simulation leagues. Monolithic developments are complex, and
the desire for decoupled architectures arises. Decoupling also lowers the threshold for using
distributed and parallel resources. The ability to re-use and re-combine components on de-
mand, therefore is essential, while maintaining the necessary degree of interaction. For this
reason we propose to build software components on top of a Service Oriented Architecture
(SOA) using Web Services. An additional bene t is platform independence regarding both
the operating system and the implementation language. The robot soccer platform as well
as the associated simulation leagues are the target domain for the development. Furthermore
are machine vision and remote process control related portions of the architecture currently
in development and testing for industrial environments. We provide numerical data based on
the Python frameworks ZSI and SOAPpy undermining the suitability of this approach for the
eld of robotics. Response times of signi cantly less than 50 ms even for fully interpreted,
dynamic languages provides hard information showing the feasibility of Web Services based
SOAs even in time critical robotic applications
Building Blocks for Control System Software
Software implementation of control laws for industrial systems seem straightforward, but is not. The computer code stemming from the control laws is mostly not more than 10 to 30% of the total. A building-block approach for embedded control system development is advocated to enable a fast and efficient software design process.\ud
We have developed the CTJ library, Communicating Threads for JavaÂż,\ud
resulting in fundamental elements for creating building blocks to implement communication using channels. Due to the simulate-ability, our building block method is suitable for a concurrent engineering design approach. Furthermore, via a stepwise refinement process, using verification by simulation, the implementation trajectory can be done efficiently
Harnessing the Power of Many: Extensible Toolkit for Scalable Ensemble Applications
Many scientific problems require multiple distinct computational tasks to be
executed in order to achieve a desired solution. We introduce the Ensemble
Toolkit (EnTK) to address the challenges of scale, diversity and reliability
they pose. We describe the design and implementation of EnTK, characterize its
performance and integrate it with two distinct exemplar use cases: seismic
inversion and adaptive analog ensembles. We perform nine experiments,
characterizing EnTK overheads, strong and weak scalability, and the performance
of two use case implementations, at scale and on production infrastructures. We
show how EnTK meets the following general requirements: (i) implementing
dedicated abstractions to support the description and execution of ensemble
applications; (ii) support for execution on heterogeneous computing
infrastructures; (iii) efficient scalability up to O(10^4) tasks; and (iv)
fault tolerance. We discuss novel computational capabilities that EnTK enables
and the scientific advantages arising thereof. We propose EnTK as an important
addition to the suite of tools in support of production scientific computing
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