Pan-European supercomputer comes a step closer to reality

European researchers will soon have access to a world-class high performance computing infrastructure, thanks to the new Partnership for Advanced Computing in Europe (PACE) initiative.

PACE brings together the supercomputing centres of 15 European countries, with the goal of strengthening European science, engineering and supercomputer technologies. At a ceremony in Berlin on 17 April, the partners signed a Memorandum of Understanding for the new initiative.

"I am very pleased that, together with our European partners, we have created the conditions for the start of PACE during the German presidency of the EU," said German Research Minister Annette Schavan at the ceremony. "With PACE, scientific computing with supercomputers gains a European dimension."

"Science and the economy need computing power of the highest level," added Achim Bachem of German partner the Gauss Centre for Supercomputing. "Supercomputers have become an essential tool in all of the natural sciences. The great leaps of knowledge of the future will only be made with the help of complex simulations."

PACE was one of the European infrastructure projects highlighted in last October's report from the European Strategy Forum for Research Infrastructures (ESFRI). The central idea behind the new European supercomputer is the joint usage of the capacities of several supercomputers. "It will be a common network with different locations, which will be linked to one another by the most modern networks," explained Dr Schavan.

According to the terms of the Memorandum of Understanding, the partners will spend the next two years putting together concrete proposals on how to best combine their equipment and expertise to implement the project. In the preparation phase, which will run up until 2010, the necessary organisational structures will be created, and clear guidelines will be drawn up concerning the hardware needed at the different locations. When the infrastructure is up and running, a peer review process will ensure that only scientifically excellent projects gain access to the supercomputer.

The start-up costs of the project have been calculated at €400 million, while annual running costs are likely to be around €100 million. Most of these costs will be covered by the Member States involved in the project, with additional funds coming from the EU's Seventh Framework Programme.

The countries involved in the project are Austria, Finland, France, Germany, Greece, Italy, Norway, Poland, Portugal, Spain, Sweden, Switzerland, the Netherlands, Turkey and the UK.

For further information about FP7 funding for research infrastructures, please visit:
http://cordis.europa.eu/fp7/capacities/research-infrastructures_en.html

Related news article:

Copyright ©European Communities, 2007
Neither the Office for Official Publications of the European Communities, nor any person acting on its behalf, is responsible for the use, which might be made of the attached information. The attached information is drawn from the Community R&D Information Service (CORDIS). The CORDIS services are carried on the CORDIS Host in Luxembourg - http://cordis.europa.eu. Access to CORDIS is currently available free-of-charge.

Most Popular Now

AI also Assesses Dutch Mammograms Better…

AI is detecting tumors more often and earlier in the Dutch breast cancer screening program. Those tumors can then be treated at an earlier stage. This has been demonstrated by...

Unlocking the 10 Year Health Plan

The government's plan for the NHS is a huge document. Jane Stephenson, chief executive of SPARK TSL, argues the key to unlocking its digital ambitions is to consider what it...

AI can Find Cancer Pathologists Miss

Men assessed as healthy after a pathologist analyses their tissue sample may still have an early form of prostate cancer. Using AI, researchers at Uppsala University have been able to...

Alcidion Grows Top Talent in the UK, wit…

Alcidion has today announced the addition of three new appointments to their UK-based team, with one internal promotion and two external recruits. Dr Paul Deffley has been announced as the...

How AI could Speed the Development of RN…

Using artificial intelligence (AI), MIT researchers have come up with a new way to design nanoparticles that can more efficiently deliver RNA vaccines and other types of RNA therapies. After training...

AI, Full Automation could Expand Artific…

Automated insulin delivery (AID) systems such as the UVA Health-developed artificial pancreas could help more type 1 diabetes patients if the devices become fully automated, according to a new review...

MIT Researchers Use Generative AI to Des…

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA). Using generative AI algorithms, the research...

Penn Developed AI Tools and Datasets Hel…

Doctors treating kidney disease have long depended on trial-and-error to find the best therapies for individual patients. Now, new artificial intelligence (AI) tools developed by researchers in the Perelman School...

AI Hybrid Strategy Improves Mammogram In…

A hybrid reading strategy for screening mammography, developed by Dutch researchers and deployed retrospectively to more than 40,000 exams, reduced radiologist workload by 38% without changing recall or cancer detection...

New Training Year Starts at Siemens Heal…

In September, 197 school graduates will start their vocational training or dual studies in Germany at Siemens Healthineers. 117 apprentices and 80 dual students will begin their careers at Siemens...

Routine AI Assistance may Lead to Loss o…

The introduction of artificial intelligence (AI) to assist colonoscopies is linked to a reduction in the ability of endoscopists (health professionals who perform colonoscopies) to detect precancerous growths (adenomas) in...

New AI Tool Addresses Accuracy and Fairn…

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms...