General Concepts for Experimental Validation of ASCI Code Applications

Trucano, Timothy Guy; Pilch, M.; Oberkampf, William L.

doi:10.2172/800777

Cited by 84 publications

(67 citation statements)

References 31 publications

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“…The present authors and their colleagues [2,3,20,[61][62][63][64] have argued that validation experiments constitute a new type of experiment. A validation experiment is conducted for the primary purpose of determining the predictive accuracy of a computational model or group of models.…”

Section: Characteristics Of Validation Experimentsmentioning

confidence: 91%

“…During the past several years, a group of researchers at Sandia National Laboratories has been developing methodological guidelines and procedures for designing and conducting a validation experiment [2,4,[20][21][22][61][62][63][64]. These guidelines and procedures have emerged as part of a concerted effort in the NNSA ASC program to provide a rigorous foundation for V&V for computer codes that are important elements of the U.S. nuclear weapons program [65].…”

Section: Characteristics Of Validation Experimentsmentioning

confidence: 99%

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Verification and validation benchmarks

Oberkampf

Trucano²

2008

Nuclear Engineering and Design

183

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Verification and validation (V&V) are the primary means to assess the accuracy and reliability of computational simulations. V&V methods and procedures have fundamentally improved the credibility of simulations in several high-consequence fields, such as nuclear reactor safety, underground nuclear waste storage, and nuclear weapon safety. Although the terminology is not uniform across engineering disciplines, code verification deals with assessing the reliability of the software coding, and solution verification deals with assessing the numerical accuracy of the solution to a computational model. Validation addresses the physics modeling accuracy of a computational simulation by comparing the computational results with experimental data. Code verification benchmarks and validation benchmarks have been constructed for a number of years in every field of computational simulation. However, no comprehensive guidelines have been proposed for the construction and use of V&V benchmarks. For example, the field of nuclear reactor safety has not focused on code verification benchmarks, but it has placed great emphasis on developing validation benchmarks. Many of these validation benchmarks are closely related to the operations of actual reactors at near-safety-critical conditions, as opposed to being more fundamental-physics benchmarks. This paper presents recommendations for the effective design and use of code verification benchmarks based on manufactured solutions, classical analytical solutions, and highly accurate numerical solutions. In addition, this paper presents recommendations for the design and use of validation benchmarks, highlighting the careful design of building-block experiments, the estimation of experimental measurement uncertainty for both inputs and outputs to the code, validation metrics, and the role of model calibration in validation. It is argued that the understanding of predictive capability of a computational model is built on the level of achievement in V&V activities, how closely related the V&V benchmarks are to the actual application of interest, and the quantification of uncertainties related to the application of interest.

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Section: Characteristics Of Validation Experimentsmentioning

confidence: 91%

Section: Characteristics Of Validation Experimentsmentioning

confidence: 99%

Verification and validation benchmarks

Oberkampf

Trucano²

2008

Nuclear Engineering and Design

183

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show abstract

“…This methodology adheres to the spirit of experimental validation as described in [32,33,34,41] because validation referents are intended to be representative of the empirical (that is, "real") performance of the full applications. However, its important to note that this V&V process is executed within the goals of miniapps, and thus we are not concerned with ensuring any sort of V&V in regard to the application goals such as correctness of the algorithms and output.…”

Section: Methodsmentioning

confidence: 99%

Assessing the role of mini-applications in predicting key performance characteristics of scientific and engineering applications

Barrett

Doerfler

Heroux

et al. 2015

Journal of Parallel and Distributed Computing

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Computational science and engineering application programs are typically large, complex, and dynamic, and are often constrained by distribution limitations. As a means of making tractable rapid explorations of scientific and engineering application programs in the context of new, emerging, and future computing architectures, a suite of "miniapps" has been created to serve as proxies for full scale applications. Each miniapp is designed to represent a key performance characteristic that does or is expected to significantly impact the runtime performance of an application program. In this paper we introduce a methodology for assessing the ability of these miniapps to effectively represent these performance issues. We applied this methodology to four miniapps, examining the linkage between them and an application they are intended to represent. Herein we evaluate the fidelity of that linkage. This work represents the initial steps required to begin to answer the question, "Under what conditions does a miniapp represent a key performance characteristic in a full app?"

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“…Toward that end, we are developing a general methodology for assessing the validity of the relationship between for use across the Mantevo suite [5]. This methodology adheres to the spirit of experimental validation as described in [19,18,12,13] because validation referents are intended to be representative of the empirical (that is, "real") performance of the full applications. Our focus is strictly on the computational runtime characteristics.…”

Section: Toward Validationmentioning

confidence: 99%

MiniGhost : a miniapp for exploring boundary exchange strategies using stencil computations in scientific parallel computing.

Barrett¹,

Heroux²,

Vaughan³

2012

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A broad range of scientific computation involves the use of difference stencils. In a parallel computing environment, this computation is typically implemented by decomposing the spacial domain, inducing a "halo exchange" of process-owned boundary data. This approach adheres to the Bulk Synchronous Parallel (BSP) model. Because commonly available architectures provide strong inter-node bandwidth relative to latency costs, many codes "bulk up" these messages by aggregating data into a message as a means of reducing the number of messages. A renewed focus on non-traditional architectures and architecture features provides new opportunities for exploring alternatives to this programming approach.In this report we describe miniGhost, a "miniapp" designed for exploration of the capabilities of current as well as emerging and future architectures within the context of these sorts of applications. MiniGhost joins the suite of miniapps developed as part of the Mantevo project,

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General Concepts for Experimental Validation of ASCI Code Applications

Cited by 84 publications

References 31 publications

Verification and validation benchmarks

Verification and validation benchmarks

Assessing the role of mini-applications in predicting key performance characteristics of scientific and engineering applications

MiniGhost : a miniapp for exploring boundary exchange strategies using stencil computations in scientific parallel computing.

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