Motivation & Main Objectives

Our society faces a wide range of global challenges in different areas such as sociology, economy, ecology and technology. Solving the most vital problems in these fields often requires the analysis of a huge amount of data, and the utilization of appropriate methods and facilities for ample computations. In HiDALGO we develop a computational and data analytics environment, which enables systematic collaboration between scientists with different background and facilitates a profound understanding of several major global challenges.

Scientific Goals

In HiDALGO we conduct research and go beyond state-of-the-art in multiple areas. Our scientific goals drive the technology evolution targeted in our project and form the foundation of our business objectives. Specifically, we pursue the following goals:

Technology Evolution

In HiDALGO, we advance HPC, HPDA and AI technologies in order to improve data-centric computation in general. Although we mainly concentrate on three specific pilot applications, the technology baseline we develop in this project can also be extended to other application domains as well. The technology evolution in HiDALGO integrates our scientific objectives into a platform, which documents the success of the individual project developements and generates the required impact to establish a sustainable Centre of Excellence.

Hardware in various environments such as High Performance Computing, the Internet of Things and Embedded Systems has become heterogeneous in order to improve computational performance. Customising the hardware for particular application domains as well as the use of accelerators such as GPUs, TPUs, DSPs, FPGAs is attractive as it can lead to performance improvements of up to three orders of magnitude compared to general-purpose processors.

Assisting Decision Makers To Solve Global Challenges With HPC Applications – Covid-19 Modelling

CHALLENGE:​

The current pandemic situation has increased the need of supporting tools to detect, predict and even prevent the virus spread behaviour. Knowing in advance this information will support them to take the appropriate decisions while considering health and care capabilities. In addition, the advance warning of new pandemic waves (or when they may subside) can help health authorities to rescale the capacity for non-urgent care, and ensure the timely arrangement of surge intensive-care capacity.

Success Stories

Each of the success stories below is a summary of a successful work that has been conducted within one of our pilot applications and one or more partners from industry, society and / or science are involved. The summary focuses on the business benefits resulting from the work.

Assisting Decision Makers To Solve Global Challenges With HPC Applications – Migration Issues

CHALLENGE:​

At the same time of the current COVID-19 pandemic, other crises have not stopped like forced migration due to conflicts. In fact, the number of forcibly displaced people is still very high, with over 70 million persons being forced to leave their homes. Save the Children provides support in these countries and needs more accurate estimations on people flows and even destinations to send the appropriate amount of help to the right place.

Robert Elsässer from HiDALGO will give a guest lecture, titled "Spectral methods to compute "eigenvalue histograms" (estimates of eigenvalue frequencies) for graph Laplacians and other LA apps that rely on iterative solvers (e.g in PETCs).", during the Iterative Solvers for Linear Systems course at HLRS on March 8-10, 2021.

HIDALGO researchers presented findings of their studies on „Tackling global challenges with HPC High Performance Computing"

A group of scientists from the EU-funded project HIDALGO collaboratively organised a workshop to discuss pressing issues like the spread of diseases (Covid-19), climate change, air pollution, forced migration, or false and misleading information on twitter and other channels. The speakers showed that HPC, HPDA and Artificial Intelligence could form a pathway to accurately model, simulate, and also to provide forecasts (e.g.