Robust workflows for science and engineering

Abramson, David; Bethwaite, Blair; Enticott, Colin; Garic, Slavisa; Peachey, Tom; Michailova, Anushka; Amirriazi, Saleh; Chitters, Ramya

doi:10.1145/1646468.1646469

Cited by 7 publications

(7 citation statements)

References 27 publications

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“…In one case study, we used the NIMROD/G tool to perform an example of this, and in a second, we showed that the more selective and efficient search functions in NIMROD/O allow us to compute parameter values that optimize some objective function. Although we did not demonstrate the third member of the NIMROD family, NIMROD/E, it has also been applied to solving complex problems in cardiac models in the past (Abramson et al 2009). …”

Section: Discussionmentioning

confidence: 74%

“…Currently, we are building a new version of NIMROD, NIMROD/K, that better supports running sweeps over workflows in which multiple tools are connected to solve a single modelling problem (Abramson et al , 2009. NIMROD/K leverages KEPLER (Altintas et al 2005), a multi-institution collaboration, to build a scientific workflow system for the grid.…”

Section: Discussionmentioning

confidence: 99%

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High-throughput cardiac science on the Grid

Abramson

Bernabéu

Bethwaite

et al. 2010

Phil. Trans. R. Soc. A.

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Cardiac electrophysiology is a mature discipline, with the first model of a cardiac cell action potential having been developed in 1962. Current models range from single ion channels, through very complex models of individual cardiac cells, to geometrically and anatomically detailed models of the electrical activity in whole ventricles. A critical issue for model developers is how to choose parameters that allow the model to faithfully reproduce observed physiological effects without over-fitting. In this paper, we discuss the use of a parametric modelling toolkit, called NIMROD, that makes it possible both to explore model behaviour as parameters are changed and also to tune parameters by optimizing model output. Importantly, NIMROD leverages computers on the Grid, accelerating experiments by using available high-performance platforms. We illustrate the use of NIMROD with two case studies, one at the cardiac tissue level and one at the cellular level.

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Section: Discussionmentioning

confidence: 74%

Section: Discussionmentioning

confidence: 99%

High-throughput cardiac science on the Grid

Abramson

Bernabéu

Bethwaite

et al. 2010

Phil. Trans. R. Soc. A.

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“…The average (mean) bytes per write call were 7,976 bytes, with a standard deviation of 1,584 bytes. The median bytes per call were again 2 pages (8,192 bytes)…”

Section: I/o Needs Of the Ocean Modelmentioning

confidence: 99%

“…Section 3 describes ESSE [5], [6], the data assimilation and error estimation approach used. Section 4 describes the ESSE implementation as a MTC application ( [7], [8], [9]) and the options we face in terms of optimizing I/O issues. This is followed in Section 5 by a discussion of the practical MTC use of ESSE on local clusters, Grids and Amazon EC2.…”

Section: Introductionmentioning

confidence: 99%

Many Task Computing for Real-Time Uncertainty Prediction and Data Assimilation in the Ocean

Evangelinos¹,

Lermusiaux

et al. 2011

IEEE Trans. Parallel Distrib. Syst.

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Uncertainty prediction for ocean and climate predictions is essential for multiple applications today. Many-Task Computing can play a significant role in making such predictions feasible. In this manuscript, we focus on ocean uncertainty prediction using the Error Subspace Statistical Estimation (ESSE) approach. In ESSE, uncertainties are represented by an error subspace of variable size. To predict these uncertainties, we perturb an initial state based on the initial error subspace and integrate the corresponding ensemble of initial conditions forward in time, including stochastic forcing during each simulation. The dominant error covariance (generated via SVD of the ensemble) is used for data assimilation. The resulting ocean fields are used as inputs for predictions of underwater sound propagation. ESSE is a classic case of Many Task Computing: It uses dynamic heterogeneous workflows and ESSE ensembles are data intensive applications. We first study the execution characteristics of a distributed ESSE workflow on a medium size dedicated cluster, examine in more detail the I/O patterns exhibited and throughputs achieved by its components as well as the overall ensemble performance seen in practice. We then study the performance/usability challenges of employing Amazon EC2 and the Teragrid to augment our ESSE ensembles and provide better solutions faster.

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“…Kepler pursues another approach which consists in wrapping the resources of other SWMSs, e.g., the Nimrod engine to improve its parameter sweeping support (Abramson et al 2009 ) . Recent developments in the fi eld and the adoption of service-oriented architecture standards by most systems should allow for interoperability.…”

Section: Execution Supportmentioning

confidence: 99%