Software Environment for Actionable & VVUQ-evaluated Exascale Applications (SEAVEA)
Lead Research Organisation:
University College London
Department Name: Chemistry
Abstract
Uncertainty quantification, verification and validation are crucial to establish the reliability and reproducibility of all forms of computer-based simulation. We propose to establish an open source and open development VVUQ toolkit optimised for efficient execution at current pre- and emerging exascale, which will raise new challenges and new opportunities for simulations in fields as diverse as fusion and climate modelling.
Computer simulation results are validated compared with experiment in several ways, ranging from qualitative to quantitative measures which apply a validation metric. Likewise, verification is concerned with confirmation that the mathematical model and corresponding algorithm have been coded correctly. Uncertainty quantification (UQ) is concerned with understanding the origins of and assessing the magnitudes of the errors which accompany computer simulations, whether epistemic or aleatoric.
VVUQ is necessary for any simulation that makes predictions in advance of an event to become actionable - that is, for its output to be useful in any form of decision-making process, from government interventions in pandemics to the choice of materials to combine for aircraft wing production. Here, exascale computing offers more opportunities to make actionable predictions.
Moreover, because VVUQ is intrinsically compute intensive due to its ensemble-based execution pattern, it too requires exascale resources, as well as advanced resource management strategies to efficiently manage the large numbers of concurrent runs necessary.
We propose to establish an open source and open development VVUQ toolkit optimised for efficient execution at current pre- and emerging exascale. This will include advanced approaches for surrogate modelling in order to minimise the expense and time needed to perform the most compute-intensive calculations and will demonstrate its efficiency gains for a diverse array of VVUQ workflows within multiple scientific applications, and on architecturally and geographically diverse emerging exascale environments.
The software developed, implemented and benchmarked in this project will become an open and invaluable asset to the UK ExCALIBUR community but also much more widely within UK and internationally as high-performance computing enters the exascale era.
The proposed exascale toolkit will be built on a combination of widely used tools and services which will be evolved to handle systems of increasing levels of complexity. These include components from the VECMA project (EasyVVUQ, FabSim3, QCG-PJ and EasySurrogate), as well as the UCL-Alan Turing Institute Multi-Output Gaussian Process Emulator (MOGP). We will apply these capabilities to several applications, including: (i) the UKAEA's tokamak fusion modelling use case for which a working software environment will be produced; (ii) weather and climate forecasting for the Met Office; (iii) turbulent flow simulation for environmental science; (iv) prediction of advanced materials properties of graphene-polymer based nanocomposites for aerospace applications; (v) high-fidelity patient-specific virtual human blood flow system for medical research; (vi) drug discovery; and (vii) human migration.
Computer simulation results are validated compared with experiment in several ways, ranging from qualitative to quantitative measures which apply a validation metric. Likewise, verification is concerned with confirmation that the mathematical model and corresponding algorithm have been coded correctly. Uncertainty quantification (UQ) is concerned with understanding the origins of and assessing the magnitudes of the errors which accompany computer simulations, whether epistemic or aleatoric.
VVUQ is necessary for any simulation that makes predictions in advance of an event to become actionable - that is, for its output to be useful in any form of decision-making process, from government interventions in pandemics to the choice of materials to combine for aircraft wing production. Here, exascale computing offers more opportunities to make actionable predictions.
Moreover, because VVUQ is intrinsically compute intensive due to its ensemble-based execution pattern, it too requires exascale resources, as well as advanced resource management strategies to efficiently manage the large numbers of concurrent runs necessary.
We propose to establish an open source and open development VVUQ toolkit optimised for efficient execution at current pre- and emerging exascale. This will include advanced approaches for surrogate modelling in order to minimise the expense and time needed to perform the most compute-intensive calculations and will demonstrate its efficiency gains for a diverse array of VVUQ workflows within multiple scientific applications, and on architecturally and geographically diverse emerging exascale environments.
The software developed, implemented and benchmarked in this project will become an open and invaluable asset to the UK ExCALIBUR community but also much more widely within UK and internationally as high-performance computing enters the exascale era.
The proposed exascale toolkit will be built on a combination of widely used tools and services which will be evolved to handle systems of increasing levels of complexity. These include components from the VECMA project (EasyVVUQ, FabSim3, QCG-PJ and EasySurrogate), as well as the UCL-Alan Turing Institute Multi-Output Gaussian Process Emulator (MOGP). We will apply these capabilities to several applications, including: (i) the UKAEA's tokamak fusion modelling use case for which a working software environment will be produced; (ii) weather and climate forecasting for the Met Office; (iii) turbulent flow simulation for environmental science; (iv) prediction of advanced materials properties of graphene-polymer based nanocomposites for aerospace applications; (v) high-fidelity patient-specific virtual human blood flow system for medical research; (vi) drug discovery; and (vii) human migration.
Organisations
- University College London (Lead Research Organisation)
- University of Cambridge (Project Partner)
- Argonne National Laboratory (Project Partner)
- Rutgers, The State University of New Jersey (Project Partner)
- Max Planck Institutes (Project Partner)
- RIKEN (Project Partner)
- Save the Children (Project Partner)
- Polish Academy of Sciences (Project Partner)
- Centrum Wiskunde & Informatica (Project Partner)
- Imperial College London (Project Partner)
- United Kingdom Atomic Energy Authority (Project Partner)
Publications
Ralli A
(2021)
Implementation of measurement reduction for the variational quantum eigensolver
in Physical Review Research
Li K
(2022)
Multilevel Bayesian Quadrature
Vassaux M
(2022)
Mechanically Stable Ultrathin Layered Graphene Nanocomposites Alleviate Residual Interfacial Stresses: Implications for Nanoelectromechanical Systems.
in ACS applied nano materials
Lo SCY
(2022)
Parametric analysis of an efficient boundary condition to control outlet flow rates in large arterial networks.
in Scientific reports
Ehara A
(2022)
Multi-level emulation of tsunami simulations over Cilacap, South Java, Indonesia
in Computational Geosciences
Bhati AP
(2022)
Large Scale Study of Ligand-Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols.
in Journal of chemical theory and computation
Bieniek MK
(2023)
TIES 2.0: A Dual-Topology Open Source Relative Binding Free Energy Builder with Web Portal.
in Journal of chemical information and modeling
Ehara Ayao
(2023)
AN ADAPTIVE STRATEGY FOR SEQUENTIAL DESIGNS OF MULTILEVEL COMPUTER EXPERIMENTS
in INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION
Groen D
(2023)
FabSim3: An automation toolkit for verified simulations using high performance computing
in Computer Physics Communications
Ehara A
(2023)
AN ADAPTIVE STRATEGY FOR SEQUENTIAL DESIGNS OF MULTILEVEL COMPUTER EXPERIMENTS
in International Journal for Uncertainty Quantification