A Generic Web Service for Running Parameter Sweep Experiments in Distributed Computing Environment

Volkov, Sergey; Sukhoroslov, Oleg V.

doi:10.1016/j.procs.2015.11.054

Cited by 14 publications

(4 citation statements)

References 12 publications

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“…A generic Everest application, the so-called Parameter Sweep, (http://everest.distcomp. org/docs/ps) [44], was used to run a number of independent tasks at the cluster via a special Everest agent installed on the cluster. The calculation of exact and automodel solutions for a single value of parameter a in the Voight line shape (6) was an independent task.…”

Section: Methodsmentioning

confidence: 99%

Automodel Solutions of Biberman-Holstein Equation for Stark Broadening of Spectral Lines

Kukushkin

Neverov

Sdvizhenskii

et al. 2018

Atoms

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Section: Methodsmentioning

confidence: 99%

Automodel Solutions of Biberman-Holstein Equation for Stark Broadening of Spectral Lines

Kukushkin

Neverov

Sdvizhenskii

et al. 2018

Atoms

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“…It will provide developers with simple Python API to exchange data between Pyomo optimization models and AMPL-compatible solvers. In addition to the solve-ampl-stub service another Everest-application to solve a set of independent problems more effectively by the generic Everest Parameter Sweep application [20] are developing now.…”

Section: Solvers and Everest Optimization Servicementioning

confidence: 99%

Balanced Identification as an Intersection of Optimization and Distributed Computing

Sokolov,

Voloshinov

2019

Preprint

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Technology of formal quantitative estimation of the conformity of the mathematical models to the available dataset is presented. Main purpose of the technology is to make easier the model selection decision-making process for the researcher. The technology is a combination of approaches from the areas of data analysis, optimization and distributed computing including: cross-validation and regularization methods, algebraic modelling in optimization and methods of optimization, automatic discretization of differential and integral equation, optimization REST-services. The technology is illustrated by a demo case study. General mathematical formulation of the method is presented. It is followed by description of the main aspects of algorithmic and software implementation. Success story list of the presented approach already is rather long. Nevertheless, domain of applicability and important unresolved issues are discussed.

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“…There are numerous available tools and frameworks for running parameter studies in cluster and grid-enabled systems. Commonly, these workflow management systems need to be installed by administrators as system-wide software, and include web interfaces for user interaction [28,33,38,43]. Also, some of these tools have a high number of modules interacting with one another which makes the system not ideal for either novice computer users or simple jobs.…”

Section: Related Workmentioning

confidence: 99%

PaPaS

Ponce

Stephenson

Lenhart

et al. 2018

Proceedings of the Practice and Experience on Advanced Research Computing

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The current landscape of scientific research is widely based on modeling and simulation, typically with complexity in the simulation's flow of execution and parameterization properties. Execution flows are not necessarily straightforward since they may need multiple processing tasks and iterations. Furthermore, parameter and performance studies are common approaches used to characterize a simulation, often requiring traversal of a large parameter space. High-performance computers offer practical resources at the expense of users handling the setup, submission, and management of jobs. This work presents the design of PaPaS, a portable, lightweight, and generic workflow framework for conducting parallel parameter and performance studies. Workflows are defined using parameter files based on keyword-value pairs syntax, thus removing from the user the overhead of creating complex scripts to manage the workflow. A parameter set consists of any combination of environment variables, files, partial file contents, and command line arguments. PaPaS is being developed in Python 3 with support for distributed parallelization using SSH, batch systems, and C++ MPI. The PaPaS framework will run as user processes, and can be used in single/multi-node and multi-tenant computing systems. An example simulation using the BehaviorSpace tool from NetLogo and a matrix multiply using OpenMP are presented as parameter and performance studies, respectively. The results demonstrate that the PaPaS framework offers a simple method for defining and managing parameter studies, while increasing resource utilization.

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A Generic Web Service for Running Parameter Sweep Experiments in Distributed Computing Environment

Cited by 14 publications

References 12 publications

Automodel Solutions of Biberman-Holstein Equation for Stark Broadening of Spectral Lines

Automodel Solutions of Biberman-Holstein Equation for Stark Broadening of Spectral Lines

Balanced Identification as an Intersection of Optimization and Distributed Computing

PaPaS

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