The purpose of this proposal is to enable a diverse set of multiscale, multiphysics applications -- from fusion and advanced materials through climate and migration, to drug discovery and the sharp end of clinical decision making in personalised medicine -- to run on current multi-petascale computers and emerging exascale environments with high fidelity such that their output is "actionable". That is, the calculations and simulations are certifiable as validated (V), verified (V) and equipped with uncertainty quantification (UQ) by tight error bars such that they may be relied upon for making important decisions in all the domains of concern. The central deliverable will be an open source toolkit for multiscale VVUQ based on generic multiscale VV and UQ primitives, to be released in stages over the lifetime of this project, fully tested and evaluated in emerging exascale environments, actively promoted over the lifetime of this project, and made widely available in European HPC centres. The project includes a fast track that will ensure applications are able to apply available multiscale VVUQ tools as soon as possible, while guiding the deep track development of new capabilities and their integration into a wider set of production applications by the end of the project. The deep track includes the development of more disruptive and automated algorithms, and their exascale-aware implementation in a more intrusive way with respect to the underlying and pre-existing multiscale modelling and simulation schemes. The potential impact of these certified multiscale simulations is enormous, and so we aim to promote the VVUQ toolkit across a wide range of scientific and social scientific domains, as well as within computational science more broadly. Scientific excellence and outreach will be overseen by a Scientific Advisory Board, while exploitation, including economic and societal impact, will be assisted by the project’s Innovation Advisory Board.

Multiscale phenomena are ubiquitous and they are the key to understanding the complexity of our world. Despite the significant progress achieved through computer simulations over the last decades, we are still limited in our capability to accurately and reliably simulate hierarchies of interacting multiscale physical processes that span a wide range of time and length scales, thus quickly reaching the limits of contemporary high performance computing at the tera- and petascale. Exascale supercomputers promise to lift this limitation, and in this project we will develop multiscale computing algorithms capable of producing high-fidelity scientific results and scalable to exascale computing systems. Our main objective is to develop generic and reusable High Performance Multiscale Computing algorithms that will address the exascale challenges posed by heterogeneous architectures and will enable us to run multiscale applications with extreme data requirements while achieving scalability, robustness, resiliency, and energy efficiency. Our approach is based on generic multiscale computing patterns that allow us to implement customized algorithms to optimise load balancing, data handling, fault tolerance and energy consumption under generic exascale application scenarios. We will realise an experimental execution environment on our pan-European facility, which will be used to measure performance characteristics and develop models that can provide reliable performance predictions for emerging and future exascale architectures. The viability of our approach will be demonstrated by implementing nine grand challenge applications which are exascale-ready and pave the road to unprecedented scientific discoveries. Our ambition is to establish new standards for multiscale computing at exascale, and provision a robust and reliable software technology stack that empowers multiscale modellers to transform computer simulations into predictive science.

This Centre of Excellence will advance the role of computationally based modelling and simulation within biomedicine. Three related user communities lie at the heart of the CoE: academic, industrial and clinical researchers who all wish to build, develop and extend such capabilities in line with the increasing power of high performance computers. Three distinct exemplar research areas will be pursued: cardiovascular, molecularly-based and neuro-musculoskeletal medicine. Predictive computational biomedicine involves applications that are comprised of multiple components, arranged as far as possible into automated workflows in which data is taken, from an individual patient, processed, and combined into a model which produces predicted health outcomes. Many of the models are multiscale, requiring the coupling of two or more high performance codes. Computational biomedicine holds out the prospect of predicting the effect of personalised medical treatments and interventions ahead of carrying them out, with all the ensuing benefits. Indeed, in some cases, it is already doing so today. The CoE presents a powerful consortium of partners and has an outward facing nature and will actively train, disseminate and engage with these user communities across Europe and beyond. Because this field is new and growing rapidly, it offers numerous innovative opportunities. There are three SMEs and three enterprises within the project, as well as eight associate partners drawn from across the biomedical sector, who are fully aware of the vast potential of HPC in this domain. We shall work with them to advance the exploitation of HPC and will engage closely with medical professionals through our partner hospitals in order to establish modeling and simulation as an integral part of clinical decision making. Our CoE is thus user-driven, integrated, multidisciplinary, and distributed; presenting a vision that is in line with the Work Programme.

CompBioMed2 is a proposal for the second phase of the Computational Biomedicine Centre of Excellence (CoE), CompBioMed, an outward facing CoE comprising members from academia, industry and the healthcare sector. CompBioMed has established itself as a hub for practitioners in the field, successfully nucleating a substantial body of research, education, training, innovation and outreach within the nascent field of Computational Biomedicine. Computational Biomedicine is an emergent technology that will enable clinicians to develop and refine personalised medicine strategies ahead of their clinical delivery to the patient. Medical regulatory authorities are currently embracing the prospect of using in silico methods in the area of clinical trials and we intend to be in the vanguard of this activity, laying the groundwork for the application of HPC-based Computational Biomedicine approaches to a greater number of therapeutic areas. The HPC requirements of our users are as diverse as the communities we represent. We support both monolithic codes, potentially scaling to the exascale, and complex workflows requiring support for advanced execution patterns. Understanding the complex outputs of such simulations requires both rigorous uncertainty quantification and the embrace of the convergence of HPC and high performance data analytics (HPDA). CompBioMed2 seeks to combine these approaches with the large, heterogeneous datasets from medical records and from the experimental laboratory to underpin clinical decision support systems. CompBioMed2 will continue to support, nurture and grow our community of practitioners, delivering incubator activities to prepare our most mature applications for wider usage, providing avenues that will sustain CompBioMed2 well-beyond the proposed funding period.