Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. 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 mostly from a modeling perspective. 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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Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

We present here a report produced by a workshop on 'Addressing failures in exascale computing' held in Park City, Utah, 4-11 August 2012. The charter of this workshop was to establish a common taxonomy about resilience across all the levels in a computing system, discuss existing knowledge on resilience across the various hardware and software layers of an exascale system, and build on those results, examining potential solutions from both a hardware and software perspective and focusing on a combined approach.

The workshop brought together participants with expertise in applications, system software, and hardware; they came from industry, government, and academia, and their interests ranged from theory to implementation. The combination allowed broad and comprehensive discussions and led to this document, which summarizes and builds on those discussions.

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Addressing failures in exascale computing

Scientific discovery and engineering innovation requires unifying traditionally separated high-performance computing and big data analytics.

Exascale computing and big data

Harnessing big data, deep data, and smart data from state-of-the-art imaging might accelerate the design and realization of advanced functional materials. Here we discuss new opportunities in materials design enabled by the availability of big data in imaging and data analytics approaches, including their limitations, in material systems of practical interest. We specifically focus on how these tools might help realize new discoveries in a timely manner. Such methodologies are particularly appropriate to explore in light of continued improvements in atomistic imaging, modelling and data analytics methods.

Big-deep-smart data in imaging for guiding materials design.

We consider multiphysics applications from algorithmic and architectural perspectives, where âalgorithmicâ includes both mathematical analysis and computational complexity, and âarchitecturalâ includes both software and hardware environments. Many diverse multiphysics applications can be reduced, en route to their computational simulation, to a common algebraic coupling paradigm. Mathematical analysis of multiphysics coupling in this form is not always practical for realistic applications, but model problems representative of applications discussed herein can provide insight. A variety of software frameworks for multiphysics applications have been constructed and refined within disciplinary communities and executed on leading-edge computer systems. We examine several of these, expose some commonalities among them, and attempt to extrapolate best practices to future systems. From our study, we summarize challenges and forecast opportunities.

https://journals.sagepub.com/doi/pdf/10.1177/1094342012468181

Multiphysics simulations: Challenges and opportunities

Over the last 20 years, the open-source community has provided more and more software on which the worldâs high-performance computing systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. However, although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual petascale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/ exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and graphics processing units. This report describes the work of the community to prepare for the challenges of exascale computing, ultimately combing their efforts in a coordinated International Exascale Software Project.

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The International Exascale Software Project roadmap

The ibm® Blue Gene®/q (bg/q) supercomputer system was developed in partnership between ibm, the Argonne National Laboratory, and the Lawrence Livermore National Laboratory. Blue Gene/Q is a bright example of the United States Department of Energy and IBM's capability and seamlessly brings together science and technology and the people that make this possible. Mira is a system, due in large part to the insights gained through years of partnership on the Blue Gene family among Argonne, Lawrence Livermore, and ibm. The ibm®Blue Gene®/l supercomputer debuted in November 2004, followed by the second generation three years later. Indeed, the second and third generations, called Blue Gene®/p and Blue Gene®/q, respectively, were conceived before Blue Gene/L was formally announced. With the deployment of Sequoia in Livermore, and the Mira 48-rack system in Argonne, Blue Gene/Q began to accumulate its trophies. The system software objectives on Blue Gene/Q are ultra-scalability, high reliability, and delivering the full performance capability of the hardware to applications.

Mark Hereld

Papers

The International Exascale Software Project roadmap

Blue Gene/Q: Sequoia and Mira