There is a growing number of problems where experiments are impossible, hazardous, or extremely expensive. Extreme-scale computing enables the solution of vastly more accurate predictive models and the analysis of massive quantities of data thanks to AI. Combining predictive modeling with data, coupled with machine learning and AI strategies, can create new opportunities in science. In particular, move from Human-in-the-Loop towards hybrid Human and Artificial Intelligence-driven design, discovery, or evaluation. However, various scientific and technical challenges need to be met to exploit exascale computing capabilities. These bottlenecks impact methods and algorithms in a profound way on all aspects of the simulation toolchain through (i) avoidance of communication, (ii) adaptive parallel grain and more compute-intensive at node level, (iii) handling of heterogeneous hardware and data representations and (iv) self-parametrization.

The Exa-MA project concentrates on the Exascale aspects of the numerical methods, ensuring their scalability to existing and forthcoming hardware. Furthermore, it is a transverse project, proposing methods and tools where modeling, data and AI, through algorithms, are central.

Though significant efforts have been devoted to the implementation and optimization of several crucial parts of a typical HPC software stack, most HPC experts agree that exascale supercomputers will raise new challenges, mostly because the trend in exascale compute-node hardware is toward heterogeneity and scalability: Compute nodes of future systems will have a combination of regular CPUs and accelerators (typically GPUs), along with a diversity of GPU architectures. Meeting the needs of complex parallel applications and the requirements of exascale architectures raises numerous challenges which are still left unaddressed.

As a result, several parts of the software stack must evolve to better support these architectures. More importantly, the links between these parts must be strengthened to form a coherent, tightly integrated software suite. Our project aims at consolidating the exascale software ecosystem by providing a coherent, exascale-ready software stack featuring breakthrough research advances enabled by multidisciplinary collaborations between researchers.

The advent of future Exascale supercomputers and the need to ensure that applications will fully exploit them raises multiple major data-related challenges. Beyond the need to advance data I/O, storage, processing and analytics to the next scale, several trends are driving the need for further specific progress in this area: the emergence of new storage technologies (e.g., first generation of commercialized NVM), which can help build faster and more scalable storage solutions, while creating a more complex storage hierarchy; the requirement to support the integration of traditional HPC simulations, Big Data, and AI in a single environment leads to the need to support new I/O schemes and data access patterns; the growing presence of accelerator-based technologies in HPC systems enabling faster, accelerator-based data analytics; the need to couple heterogeneous data from many different sources (HPC facilities, cloud-based repositories, IOT/edge-originated data from the real world). All these evolutions lead to major challenges for data transfer, I/O, storage, processing and analysis.

The emergence of exascale supercomputers for addressing scientific challenges (e.g. creating digital twins) is concomitant to a deluge of data from large scientific instruments, simulations, or IoT infrastructures. The new discovery process relies on combining HPDA and HPC in application workflows. These workflows must be deployed at a large scale, involving numerous multidisciplinary distributed infrastructures (HPC centers, Clouds, …) distributed over multiple geographical areas. This transversal axis aims at answering simple questions, typically raised by scientific users and application developers:

• How to allocate data and compute resources?
• How to ensure efficiency, productivity and reproducibility of executions?
• How to visualize results and exploit the application’s resulting data (in a FAIR spirit)?

While simple to express, current technologies and state-of-the-art systems architectures are making the task too complex for end-users (especially in a productive and efficient manner), and prevent a generalization of the use of HPC and HPDA. This project aims at defining a coherent architecture with all the tools needed to deploy scientific applications based on large-scale workflows.