Goal-Directed Grid-Enabled Computing for Legacy Simulations

Page, Ernest H.; Litwin, Laurie; McMahon, Matthew T.; Wickham, Brian; Shadid, Mike; Chang, Elizabeth

doi:10.1109/ccgrid.2012.60

Cited by 12 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Systems like SakerGrid [Kite et al 2011] or MEG [Page et al 2012] are similar to SAFE in that their aim is to automate simulation experiment execution, but they are independent of a specific simulation system and focus on large-scale distributed experiment execution (e.g., on a grid). Both tools offer graphical user interfaces and some support for experiment design (e.g., MEG includes support for simulation-based optimization), but (to the best of our knowledge) they do not provide custom DSLs to set up simulation experiments.…”

Section: Simulation Experimentationmentioning

confidence: 99%

Sessl

Ewald

Uhrmacher

2014

ACM Trans. Model. Comput. Simul.

View full text Add to dashboard Cite

This article introduces SESSL (Simulation Experiment Specification via a Scala Layer), an embedded domain-specific language for simulation experiments. It serves as an additional software layer between users and simulation systems and is implemented in Scala. SESSL supports multiple simulation systems and offers various features (e.g., for experiment design, performance analysis, result reporting, and simulation-based optimization). It supports "cutting-edge" experiments by allowing to add custom code, enables a reuse of functionality across simulation systems, and improves the reproducibility of simulation experiments. ACM Reference Format:Roland Ewald and Adelinde M. Uhrmacher. 2014. SESSL: A domain-specific language for simulation experiments. ACM Trans. Model.

show abstract

Section: Simulation Experimentationmentioning

confidence: 99%

Sessl

Ewald

Uhrmacher

2014

ACM Trans. Model. Comput. Simul.

View full text Add to dashboard Cite

show abstract

“…The MITRE Elastic Goal-directed simulation framework (MEG) is a middleware framework to allow simulationists to access computing resources easily without modifying existing simulation applications. 32 In particular, the objective of MEG is to embrace various grid schedulers; thus, the authors have adopted the Gridway meta-scheduler, 33 which works with various distributed resource management systems, such as HT Condor, 26 Globus, 27 and Sun Grid Engine (the name has since changed to Oracle Grid Engine). 28 Moreover, the MEG utilized third-party data processing and visualization tools to help the simulationist to gather simulation results from distributed resources and to analyze the simulation results easily.…”

Section: Related Workmentioning

confidence: 99%

DEXSim: an experimental environment for distributed execution of replicated simulators using a concept of single simulation multiple scenarios

2014

View full text Add to dashboard Cite

This paper presents an efficient and scalable experimental environment for distributed execution of replicated simulators. By taking a performance-centered approach, the proposed technique makes the best use of distributed hardware resources for faster data collection. Accordingly, the primary contribution of this work is to describe how the environment improves scalability and utilizes distributed hardware resources efficiently. To do this, we suggest a new concept of single simulation multiple scenarios and propose a distributed execution simulation framework regarding the following three aspects: (1) layered architecture model design;(2) protocol definitions interacting with them; and (3) framework implementation. The proposed model architecture and protocol definitions guarantee a straightforward structural scalability and an efficient load-balanced utilization between hardware resources. Moreover, the framework operates simulation execution automatically without users' extra work. In order to prove the efficiency of the proposed framework, we performed three extensive experiments with different models, that is, different systems. The experimental results show that simulation performance increases proportionally with the number of hardware resources, minimizing the overhead of the proposed framework's utilization.

show abstract

“…Web-based and distributed simulation techniques have become popular to conduct these online simulations (Taylor 2011). Although Distributed Simulation middleware is now widely use for Defense applications (Page et al 2012), its practical use in non-military applications is still very limited. As indicated by recent surveys (Strassburger, Schulze and Fujimoto 2008), the lack of practical plug-and-play interoperability makes most simulation middleware are complex to interoperate, and their composition scalability is limited.…”

Section: Ubiquitous Simulation In the Cloud: Simulation Everywherementioning

confidence: 99%

“…These players need to transform data into hypothesis building and critical decision-making, and to change their models in response to new hypotheses, usually involving multiple highly specialized experts working together in geographically distant areas. The Grid and Cloud computing paradigms introduced new ways of sharing computing power and storage in heterogeneous environments (Vanmechelen et al 2012;Page et al 2012;Ribault and Wainer 2012). Resources are virtualized as services consumed on demand (with minimal limitation for resource location).…”

Section: Ubiquitous Simulation In the Cloud: Simulation Everywherementioning

confidence: 99%

Panel on grand challenges for modeling and simulation

Taylor¹,

Fujimoto²,

Page³

et al. 2012

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)

Self Cite

View full text Add to dashboard Cite

It has been a decade since the Workshop on Grand Challenge for Modeling & Simulation (M&S) was held at Dagstuhl in Germany (www.dagstuhl.de/02351). Grand challenges provide a critical focal point for research and development and can potentially create the critical mass needed to bring substantial transformation and benefit to a community. The Workshop addressed a wide variety of M&S theoretical, methodological and technological issues across many application areas. This Panel reflects on progress made since the Workshop, new Grand Challenges that have emerged over the past ten years and key M&S milestones for the next decade.

show abstract

Goal-Directed Grid-Enabled Computing for Legacy Simulations

Cited by 12 publications

References 19 publications

Sessl

Sessl

DEXSim: an experimental environment for distributed execution of replicated simulators using a concept of single simulation multiple scenarios

Panel on grand challenges for modeling and simulation

Contact Info

Product

Resources

About