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Challenging, exciting, promising times

Giampietro Tecchiolli expects the techniques of high-performance computing to become more pervasive but

worries about shortage of skilled manpower C

omputational science is now commonly considered a third mode of science and engineering, complementing and adding to field/

experimentation/observation and theory. Computer models and computer simulations have become an important part of the research repertoire, supplementing (and in some cases replacing) experimentation. Scientific computing spans different

disciplines such as computer science, applied mathematics and science/ engineering. It is a complex and diverse area, requiring domain expertise, mathematical modelling, numerical analysis, algorithm development, soſtware implementation, program execution, analysis, validation, and visualisation of results. Te fundamental technological enabler

of scientific computing is high performance computing (HPC). Te linkage between scientific computing and HPC exists at different levels: some scientific and engineering problems need to scale over large computational resources. Others are suited to thousands of simulations running in parallel. A third group deals with large amounts of data. Tese three areas share the same need for

more computing. Te key point is exactly in those two words: ‘more computing’, because there lies the very essence of the challenges HPC is facing – challenges that will shape the future of HPC and scientific computing. Te first, and very considerable, challenge

is exascale, which in concrete terms is the ability to install and run a production supercomputer with a peak performance of at least 1000 PFlop/s and a power consumption limited to 20 MW. Another challenge is to take the

technologies for exascale to much smaller dimensions so that they become available to all users, bringing benefits such as fast


‘Human Brain’, in which Eurotech is taking part. With current computational resources, we can map and simulate 5-10 per cent of the human brain but not in real time. Extrapolation demonstrates that a real-time complete human brain simulation will need from 1 to 10 exaflop/s and 4 PB of memory. While theoretical projections suggest

Eurotech’s Tigon board shows the company’s cooling technology

and energy-efficient computation to many companies and institutions in a variety of markets. A third challenge is skills. With HPC

systems becoming larger and more pervasive and with the application of scientific computing in many additional areas, there is and there will continue to be a lack of specific competences, at least until proper education becomes more widespread and starts producing positive effects. Te future of scientific computing will

be very much conditioned by exascale and its derivations. Te current largest Top 500 supercomputers are petascale installations that already seem large enough in terms of energy consumption, operational cost, and space occupancy. But, they are not large enough for the challenges science is facing in many fields: from high energy physics to astronomy, climate modelling, chemistry, material science, biology, and others. Exascale supercomputers can enable

progress in ‘old’ science such as climate modelling, molecular dynamics, aerodynamic design, and cosmology. In other words, there is a part of science and engineering for which it is already known that more computing will lead to faster and better results. But, exascale could also open possibilities of ‘new’ science and engineering, so new applications that will lead to discoveries that we don’t yet imagine. Take, for example, a project like the

the possibility of having an exascale system by 2018, reality tells us that a usable supercomputer of that size will require at least few years into the next decade. Simply adopting the current approach – more of the same, but bigger and faster – will not work, due to constraints in power availability, power cost, reliability, and scalability of applications. Exascale poses new constraints that will

force the entire HPC community to think differently. Power (cost and availability) is the largest of these constraints. In the last decade, we realised a one-time gain in power efficiency by switching to accelerators/ manycore. Tis is not a sustainable trend, without any future technological discontinuity in combination with a new approach. New technologies such as silicon photonics, as well as improvements on existing technologies, such as low power processors, accelerators, liquid cooling and 3D Torus, will be combined with a new way of using systems and new programming models (so, in essence, a new scientific computing) that will not focus only on performance but also on energy efficiency. For instance, since the majority of energy consumed by today’s supercomputers is used to move data around the system, a lot of attention will be given to concurrency and locality. As a manufacturer, Eurotech believes it can

contribute to the exascale effort by working with research institutions (as we are doing in projects like QPACE2, DEEP, and Human Brain) and using technology advances in systems that are usable and affordable. What we envision is that the combination

of novel, extremely energy-efficient architectures and liquid cooling should provide the ground on which to build exascale systems. We recently presented a

@scwmagazine l

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