ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2019
Multiscale computing for science and engineering in the era of exascale performance.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 22 citations in OpenAlex.
- Uncertainty quantification patterns for multiscale models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- Article
- Towards blood flow in the virtual human: efficient self-coupling of HemeLB.Interface focus · 2021Article
- Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models.Frontiers in bioengineering and biotechnology · 2021Review
- Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction.Interface focus · 2020Review
- From digital hype to analogue reality: Universal simulation beyond the quantum and exascale eras.Journal of computational science · 2020Article
- The application of big data to cardiovascular disease: paths to precision medicine.The Journal of clinical investigation · 2020Review
- Multiscale modelling, simulation and computing: from the desktop to the exascale.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this position paper, we discuss two relevant topics: (i) generic multiscale computing on emerging exascale high-performing computing environments, and (ii) the scaling of such applications towards the exascale. We will introduce the different phases when developing a multiscale model and simulating it on available computing infrastructure, and argue that we could rely on it both on the conceptual modelling level and also when actually executing the multiscale simulation, and maybe should further develop generic frameworks and software tools to facilitate multiscale computing. Next, we focus on simulating multiscale models on high-end computing resources in the face of emerging exascale performance levels. We will argue that although applications could scale to exascale performance relying on weak scaling and maybe even on strong scaling, there are also clear arguments that such scaling may no longer apply for many applications on these emerging exascale machines and that we need to resort to what we would call multi-scaling. This article is part of the theme issue 'Multiscale modelling, simulation and computing: from the desktop to the exascale'.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.