The CardioRisk project: Improvement of cardiovascular risk assessment

Paredes, S.; Rocha, Teresa; Carvalho, Paulo; Henriques, J.; Mendes, Diana; Cabete, Ricardo; Cabiddu, Ramona; Bianchi, Anna Maria

doi:10.1016/j.jocs.2015.04.025

Cited by 7 publications

(2 citation statements)

References 14 publications

(14 reference statements)

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“…Multiscale phenomena are everywhere around us [1][2][3][4][5][6][7]. If we study the origin and evolution of the Universe [8,9] or properties of materials [10][11][12][13][14], if we try to understand health and disease [3,[15][16][17][18][19][20][21][22] or develop fusion as a potential energy source of the future [23], in all these cases and many more we find that processes on quite disparate length and time scales interact in strong and nonlinear ways. In short, multiscale modelling is ubiquitous and progress in most of these cases is determined by our ability to design and implement multiscale models of the particular systems under study [1,6,24,25].…”

Section: Introductionmentioning

confidence: 99%

Multiscale computing for science and engineering in the era of exascale performance

Hoekstra

Chopard

Coster

et al. 2019

Phil. Trans. R. Soc. A.

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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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Section: Introductionmentioning

confidence: 99%

Multiscale computing for science and engineering in the era of exascale performance

Hoekstra

Chopard

Coster

et al. 2019

Phil. Trans. R. Soc. A.

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show abstract

“…Indeed, multiscale phenomena are everywhere around us [1][2][3][4][5][6][7]. If we study the origin and evolution of the universe [8] or properties of materials [9][10][11][12][13], if we try to understand health and disease [3,[14][15][16][17][18][19][20][21] or develop fusion as a potential energy source of the future [22], in all these cases and many more we find that processes on quite disparate length and time scales interact in strong and non-linear ways.…”

Section: Introductionmentioning

confidence: 99%

Multiscale computing in the exascale era

Alowayyed

Groen

Coveney

et al. 2017

Journal of Computational Science

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We expect that multiscale simulations will be one of the main high performance computing workloads in the exascale era. We propose multiscale computing patterns as a generic vehicle to realise load balanced, fault tolerant and energy aware high performance multiscale computing. Multiscale computing patterns should lead to a separation of concerns, whereby application developers can compose multiscale models and execute multiscale simulations, while pattern software realises optimized, fault tolerant and energy aware multiscale computing. We introduce three multiscale computing patterns, present an example of the extreme scaling pattern, and discuss our vision of how this may shape multiscale computing in the exascale era.

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Clinical Risk Prediction of Acute Coronary Syndrome Using Deep Learning Approach

Suchak¹,

Gogate

2020

Intelligent Computing and Networking

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The CardioRisk project: Improvement of cardiovascular risk assessment

Cited by 7 publications

References 14 publications

Multiscale computing for science and engineering in the era of exascale performance

Multiscale computing for science and engineering in the era of exascale performance

Multiscale computing in the exascale era

Clinical Risk Prediction of Acute Coronary Syndrome Using Deep Learning Approach

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