UNEDF: Advanced Scientific Computing Collaboration Transforms the Low-Energy Nuclear Many-Body Problem

The demands of cutting-edge science are driving the need for larger and faster computing resources. With the rapidly growing scale of computing systems and the prospect of technologically disruptive architectures to meet these needs, scientists face the challenge of effectively using complex computa...

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Vydané v:Journal of physics. Conference series Ročník 402; číslo 1; s. 12033 - 12
Hlavní autori: Nam, H, Stoitsov, M, Nazarewicz, W, Bulgac, A, Hagen, G, Kortelainen, M, Maris, P, Pei, J C, Roche, K J, Schunck, N, Thompson, I, Vary, J P, Wild, S M
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bristol IOP Publishing 20.12.2012
Predmet:
Algorithms
Architecture (computers)
Classification
Collaboration
Computation
Leadership
Networks
Physics
Reactions (nuclear)
Scientists
Transforms
ISSN:1742-6588, 1742-6596
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Shrnutí:The demands of cutting-edge science are driving the need for larger and faster computing resources. With the rapidly growing scale of computing systems and the prospect of technologically disruptive architectures to meet these needs, scientists face the challenge of effectively using complex computational resources to advance scientific discovery. Multi-disciplinary collaborating networks of researchers with diverse scientific backgrounds are needed to address these complex challenges. The UNEDF SciDAC collaboration of nuclear theorists, applied mathematicians, and computer scientists is developing a comprehensive description of nuclei and their reactions that delivers maximum predictive power with quantified uncertainties. This paper describes UNEDF and identifies attributes that classify it as a successful computational collaboration. We illustrate significant milestones accomplished by UNEDF through integrative solutions using the most reliable theoretical approaches, most advanced algorithms, and leadership-class computational resources.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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AC05-76RL01830
PNNL-SA-83190
USDOE Office of Science (SC)
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/402/1/012033