Summary

Simulation alongside theory and experiment is nowadays considered an integral part of scientific discovery. As computation speeds up and new technologies and instruments improve, data generation in all fields of science is rapidly increasing. As a consequence, researchers face new challenges: Data collection exceeds by far the capacity to validate, analyze, visualize, store, and curate the information contained. Additionally, traditional, single-scale, macroscopic models are becoming inadequate for the accuracy requirements of modern physical, biological and engineering applications that involve multiscale phenomena occurring over vastly different scales. Scientific and engineering disciplines have been at the forefront of applying simulation to study complex phenomena that in many cases involve multiple scales, such as turbulence and neurogenerative diseases. Physicists, biologists and engineers constantly need innovative techniques to handle efficiently large-scale simulations and the exponentially increasing data sets. Many researchers, in fact, are talking of a new research paradigm that uses big data in a non-hierarchical manner to achieve scientific breakthroughs. Whether or not there is agreement on the so called fourth paradigm of big data science, it is clear that we need to have the tools and competencies to enable computational scientists to efficiently manage and explore extreme scale data. In this project, we tackle applications that can greatly benefit from new simulation and data approaches. These range from exascale simulations in lattice QCD, where high-throughput reduction and analysis capabilities of the large volumes of data generated is needed for extracting information, to simulations at the molecular and neuronal networks level of the neuronal dysfunction associated with Parkinson’s disease (PD), which is the second most common, fatal neurodegenerative disease, affecting about 1.5 million people worldwide. Adopting a unified approach to solve the challenges posed by extreme computing and data across disciplines has been recognized as the appropriate methodology and new initiatives for centers of excellence and research institutes following this underlying principle are taking place world-wide. A crucial component of enabling the next generation of scientific breakthroughs identified by these initiatives is the education of computational scientists trained in both exascale and data-intensive computing within their domain of specialization. This requires a highly interdisciplinary environment and goes beyond what is customarily delivered by traditional departments. The ambition of this European Joint Doctorate (EJD) program SimulaTIon in MUltiscaLe physicAl and biological sysTEms (STIMULATE) is to deliver such an interdisciplinary educational program with research projects that address exascale simulation and data challenges.