4,872 research outputs found
Modelling and simulation framework for reactive transport of organic contaminants in bed-sediments using a pure java object - oriented paradigm
Numerical modelling and simulation of organic contaminant reactive transport in the environment is being increasingly
relied upon for a wide range of tasks associated with risk-based decision-making, such as prediction of contaminant
profiles, optimisation of remediation methods, and monitoring of changes resulting from an implemented remediation
scheme. The lack of integration of multiple mechanistic models to a single modelling framework, however, has
prevented the field of reactive transport modelling in bed-sediments from developing a cohesive understanding of
contaminant fate and behaviour in the aquatic sediment environment. This paper will investigate the problems involved
in the model integration process, discuss modelling and software development approaches, and present preliminary
results from use of CORETRANS, a predictive modelling framework that simulates 1-dimensional organic contaminant
reaction and transport in bed-sediments
On Modelling and Analysis of Dynamic Reconfiguration of Dependable Real-Time Systems
This paper motivates the need for a formalism for the modelling and analysis
of dynamic reconfiguration of dependable real-time systems. We present
requirements that the formalism must meet, and use these to evaluate well
established formalisms and two process algebras that we have been developing,
namely, Webpi and CCSdp. A simple case study is developed to illustrate the
modelling power of these two formalisms. The paper shows how Webpi and CCSdp
represent a significant step forward in modelling adaptive and dependable
real-time systems.Comment: Presented and published at DEPEND 201
Many-Task Computing and Blue Waters
This report discusses many-task computing (MTC) generically and in the
context of the proposed Blue Waters systems, which is planned to be the largest
NSF-funded supercomputer when it begins production use in 2012. The aim of this
report is to inform the BW project about MTC, including understanding aspects
of MTC applications that can be used to characterize the domain and
understanding the implications of these aspects to middleware and policies.
Many MTC applications do not neatly fit the stereotypes of high-performance
computing (HPC) or high-throughput computing (HTC) applications. Like HTC
applications, by definition MTC applications are structured as graphs of
discrete tasks, with explicit input and output dependencies forming the graph
edges. However, MTC applications have significant features that distinguish
them from typical HTC applications. In particular, different engineering
constraints for hardware and software must be met in order to support these
applications. HTC applications have traditionally run on platforms such as
grids and clusters, through either workflow systems or parallel programming
systems. MTC applications, in contrast, will often demand a short time to
solution, may be communication intensive or data intensive, and may comprise
very short tasks. Therefore, hardware and software for MTC must be engineered
to support the additional communication and I/O and must minimize task dispatch
overheads. The hardware of large-scale HPC systems, with its high degree of
parallelism and support for intensive communication, is well suited for MTC
applications. However, HPC systems often lack a dynamic resource-provisioning
feature, are not ideal for task communication via the file system, and have an
I/O system that is not optimized for MTC-style applications. Hence, additional
software support is likely to be required to gain full benefit from the HPC
hardware
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
Fast semidefinite programming with feedforward neural networks
Semidefinite programming is an important optimization task, often used in
time-sensitive applications. Though they are solvable in polynomial time, in
practice they can be too slow to be used in online, i.e. real-time
applications. Here we propose to solve feasibility semidefinite programs using
artificial neural networks. Given the optimization constraints as an input, a
neural network outputs values for the optimization parameters such that the
constraints are satisfied, both for the primal and the dual formulations of the
task. We train the network without having to exactly solve the semidefinite
program even once, thus avoiding the possibly time-consuming task of having to
generate many training samples with conventional solvers. The neural network
method is only inconclusive if both the primal and dual models fail to provide
feasible solutions. Otherwise we always obtain a certificate, which guarantees
false positives to be excluded. We examine the performance of the method on a
hierarchy of quantum information tasks, the Navascu\'es-Pironio-Ac\'in
hierarchy applied to the Bell scenario. We demonstrate that the trained neural
network gives decent accuracy, while showing orders of magnitude increase in
speed compared to a traditional solver
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