Extreme Heterogeneity 2018 - Productive Computational Science in the Era of Extreme Heterogeneity: Report for DOE ASCR Workshop on Extreme Heterogeneity

Vetter, Jeffrey S.; Brightwell, Ron; Gokhale, Maya; McCormick, Pat; Ross, Robert; Shalf, John; Antypas, Katie; Donofrio, David; Humble, Travis S.; Schuman, Catherine D.; Essen, Brian Van; Yoo, Shinjae; Aiken, Alex; Bernholdt, David E.; Byna, Suren; Cameron, Kirk W.; Cappello, Frank; Chapman, Barbara; Chien, Andrew; Hall, Mary; Hartman-Baker, Rebecca; Lan, Zhiling; Lang, Michael; Leidel, John D.; Li, Sherry; Lucas., Robert; Mellor-Crummey, John; Peltz, Paul; Peterka, Thomas; Strout, Michelle Mills; Wilke, Jeremiah J

doi:10.2172/1473756

Cited by 63 publications

(34 citation statements)

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“…Unless we face up to a new transistor technology (e.g., see [451]) to replace current metal-oxide semiconductor transistor technologies, this doubling trend saturates as chip manufacturing sector reaches the limits of the atomic scale. This leads to more effective use of transistors through more efficient architectures (e.g., see [452] for a recent discussion in extreme heterogeneity). The HPC community has started to move forward to incorporate GPU based accelerators and beyond (e.g., TPUs [453]) for not only graphics rendering but also scientific computing applications.…”

Section: ) Computationalmentioning

confidence: 99%

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

2020

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A digital twin can be defined as an adaptive model of a complex physical system. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

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Section: ) Computationalmentioning

confidence: 99%

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

2020

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“…This is also one of the recommendations of the 2014 NSF XSEDE reproducibility workshop (James et al, 2014). A recent DOE Office of Science report on Extreme Heterogeneity defines “partial reproducibility,” where bitwise reproducibility can be achieved in debugging mode at much lower speed, but the constraint is relaxed in production runs (Vetter et al, 2018). In addition, the report stresses the importance of defining different levels of reproducibility appropriate for specific applications and study the trade-offs between reproducibility and performance.…”

Section: Related Workmentioning

confidence: 99%

Computational reproducibility of scientific workflows at extreme scales

Pouchard

Baldwin

Elsethagen

et al. 2019

The International Journal of High Performance Computing Applica

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We propose an approach for improved reproducibility that includes capturing and relating provenance characteristics and performance metrics, in a hybrid queriable system, the ProvEn server. The system capabilities are illustrated on two use cases: scientific reproducibility of results in the ACME climate simulations and performance reproducibility in molecular dynamics workflows on HPC computing platforms.

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“…Future architectures are becoming more heterogeneous and complex [59]. As such, the role of languages, compilers, runtime systems, and performance and debugging tools will becoming increasingly important for productivity and performance portability.…”

Section: Future Trendsmentioning

confidence: 99%

ECP Software Technology Capability Assessment Report

Heroux

Computing²,

Carter

et al. 2018

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The Exascale Computing Project (ECP) Software Technology (ST) Focus Area is responsible for developing critical software capabilities that will enable successful execution of ECP applications, and for providing key components of a productive and sustainable Exascale computing ecosystem that will position the US Department of Energy (DOE) and the broader high performance (HPC) community with a firm foundation for future extreme-scale computing capabilities. This ECP ST Capability Assessment Report (CAR) provides an overview and assessment of current ECP ST capabilities and activities, giving stakeholders and the broader HPC community information that can be used to assess ECP ST progress and plan their own efforts accordingly. ECP ST leaders commit to updating this document on regular basis (every six to 12 months). Highlights from this version of the report are presented here. What is new in CAR V2.0: CAR V2.0 contains the following updates relative to CAR V1.5. • We introduce the FY20-23 project structure. ECP ST now consists of 6 (up from 5) level-3 (L3) technical areas, introducing the NNSA ST L3 area, which brings into one L3 the ECP open source development efforts at NNSA labs that are of particular importance to the rest of ECP. The number of ECP ST level-4 (L4) subprojects has been reduced from 55 to 33. The strategic aggregation of projects into fewer and larger units enables us to better manage L4 subprojects consistently as a portfolio. See Section 1.2. • We describe new and enhanced project management processes and resources including our iterative planning process, new KPP-3 capability integration process, a product dictionary and dependency management database. These new project features and related dashboards enable more insight and better information to effectively manage efforts across ECP. See Section 2. • The two-page summaries of each ECP L4 projects have been updated to reflect recent progress and next steps. See Section 4. • The Extreme-scale Scientific Software Stack (E4S) is further described. The third release, which is also the first major public release Version 1.0, was November 18, 2019. E4S is the primary integration and delivery vehicle for ECP ST capabilities. See Section 2.1.1. • The ECP ST SDK effort has further refined its groupings. See Section 2.1.2. The Exascale Computing Project Software Technology (ECP ST) focus area represents the key bridge between Exascale systems and the scientists developing applications that will run on those platforms. ECP ST efforts contribute to 70 software products (Section 2.1.3) in six technical areas (Table 1). Since the publishing of CAR V1.5, we have introduced a product dictionary of official product names, which enables more rigorous mapping of ECP ST deliverables to stakeholders (Section 2.1.4). Programming Models & Runtimes: In addition to developing key enhancements to MPI and OpenMP for scalable systems with accelerated node architectures, we are working on performance portability layers (Kokkos and RAJA) and participating in OpenMP and O...

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Extreme Heterogeneity 2018 - Productive Computational Science in the Era of Extreme Heterogeneity: Report for DOE ASCR Workshop on Extreme Heterogeneity

Cited by 63 publications

References 0 publications

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Computational reproducibility of scientific workflows at extreme scales

ECP Software Technology Capability Assessment Report

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