Dreams come true...when robots learn to translate musical compositions into art paintings. Picture: IWR

research topics in this area one ear-ly example will be described here in more detail.

One of the key factors for impro-ving the performance of many

technical combustion devices is an optimal control of the ignition process ranging from the preven-tion of explosions and detonations to control of “knock” and complete ignition in automobile engines. To tackle this phenomena, an inten-sive dialogue between applied ma-thematics and physical chemistry was required. Hence, the resear-chers focused on a straight forward direct numerical simulation (DNS) of the technical ignition processes. This DNS required a solution of 103 to 104 nonlinear differential equa-tions for the chemical kinetics and coupled conservation equations for mass momentum and energy with a time resolution down to 10-7 se-conds which results in 1018 to 1020

For more than one million years fi re has been one of the most

successful technologies of man-kind, as it is still the most impor-tant way of providing energy supply in our modern society.

The construction of technical combustion devices has been an

empirical process based to a large degree on trial and error methods and global performance measu-rements for many centuries. With the foundation of the IWR a new concept became feasible due to the development of suitable numerical tools to solve large systems of dif-ferential equations to simulate the complex interaction of chemistry and physical transport processes in combustion devices. Fundamental questions in combustion research could be investigated by comparing results of mathematical modeling with data from quantitative analy-sis of technical combustion devices using non-intrusive laser methods with high temporal, spectral and spatial resolution. From the many

calculations. An amount still far too high for the capacity of modern parallel computers. In order to de-crease the numerical effort drasti-cally, model reduction was perfor-med by concerning the chemistry and the geometrical conditions and by investigating bench mark pro-blems like the explosion of ozone/oxygen mixtures.

Here are only two chemical re-actions of the explosion pro-

cess: Ozone decomposition by a CO2-laser: O3 -> O2+ O followed by the fast elementary reaction O + O3 -> 2O2 in cylindrical geometry.The IWR research teams showed by ex-periment and model that the fl ame propagation speed in axial direc-tion is fi ve times faster than in radi-al direction. This result was a very important step towards the under-standing of “knock”-phenomena in automobile engines where un-wanted self-ignition by fl ame front acceleration occurs in hot spots during the adiabatic compression phase. The local ignition produces shock waves resulting in tempera-tures jumps that accelerate the fl a-

IWR on fi re! By using powerful algorithms IWR researchers de-scribed the fundamental physico-chemical processes of combus-tion. Foto: Schönbucher

me propagation and lead to detonations.

This striking ex-ample shows that

the attack of challen-ging practical questi-ons by the IWR is an essential trigger in developing new ma-thematical methods and algorithms. 25 years ago the mana-gers of the automobi-le industry were qui-te sceptical about our concept for mathe-matical modeling the engine combustion. However, Ferdinand Piech from Volkswa-gen stated recently: “I have to admit that without mathema-tical modeling and laser diagnostics no modern direct injec-ting Diesel or Otto engine can be const-ructed, since to many parameters have to be considered.“

Fire in the Computer: Calculate Combustian Processes By JÜRGEN WOLFRUM

This statement of Walt Disney might not apply to science in

general. However, starting a center in a new scientifi c discipline you should have a dream and pursue a long-term vision, based on sound experience and expertise.

This was the case back in the 1980s at Heidelberg University,

when IWR was designed and imple-mented as a center for mathemati-cal modeling, simulation and optimization. Experts from Ma-thematics, Physics, Chemistry and

the Life Sciences were co-operating in SFB123 „Stochastic Mathemati-cal Models“ (1978-92), funded by the DFG. They had the joint goal to develop mathematical models for complex processes in the sciences and to develop systematic methods for computing them. Transported back in time, you will fi nd yourself in pioneering days: Computer tech-nology was starting to revolutionize science. Whereas mathematical mo-deling and simulations had already been established tools in physics and engineering sciences, other dis-

No 5 25th Anniversary of the Interdisciplinary Center for Scientifi c Computing (IWR) February 2013

ges and problems, which appeared to be unsolvable before. Luckily the DFG, the State of Baden Würt-temberg and Heidelberg University were determined in supporting this initiative by putting into perspective the potential of the new discipline and therby acted as a door opener for science and technology. Getting similar support for groundbreaking research has become increasingly diffi cult in the last decades. Back then, the partners of the young IWR had the courage to leave the beaten tracks and were co-operating wit-hout pursuing only their own profi t. Thus, it became possible to build up IWR to an internationally renow-ned top-center that aims at achie-ving crucial scientifi c progress and structural changes.

The synergy of the disciplines within IWR has led to new qua-

lities and potentials. The center ac-ted as a space maker for computer science within the University: It promoted the computational sci-ences in physics, chemistry and the environmental sciences. It also was one of the driving forces to establish the quantitative life sciences, in par-ticular by building up the BioQuant Center. By starting to push research

ciplines like chemistry or the life sciences had just started

to take profi t by them.

Complexity, non-linearities, insta-

bilities, multiple sca-les, high dimensions and uncertainties in the arising questi-ons urged the use of advanced com-putational hard- and software, to

adapt modeling to the potential of computers, and to develop effi cient and reliable algorithms. Combining mathema-

tical and computatio-nal methods and ma-

thematical modeling with advanced information technology opened up new perspectives in research. Inter-nationally leading scientists agreed that computational sciences as the “third branch of scientifi c me-thodology” - complementing the traditional branches theory and ex-periment - had to be created. It was a sheer stroke of luck, that a team of scientists in Heidelberg got exci-ted by the idea to establish a center for this new branch. Above all, they shared the dream of facing challen-

on computational humanities, the IWR is realizing the dream of intro-ducing mathematical and computa-tional methods into disciplines ran-ging from archaeology to sociology and economics. Medicine and Ma-terial Science shall be more in the focus of future research.

Within the last 25 years of co-operating with the faculties

the new areas introduced by the IWR could be established besides the basic disciplines of computer science: Computational mathema-tics, optimization, parallel algo-rithms, modelling in biosciences, computational chemistry, mole-cular dynamics, image processing, and visualization - these new re-search fi elds would not exist at Hei-delberg University without IWR, as it strengthened research and education oriented to algorithms and simulation. For IWR‘s future development you should take into account, that the center has been successful due to the fruitfull integ-ration of teams located in the center and in various faculties either. The entirety has to be given highest prio-rity. One should learn from history while catching a glimpse of what is coming next for IWR: A dream is essential, if you want to shape the future!

What makes IWR‘s success?

The intensive dialogue between disciplines

Spin-off : TECFLAM Research association „TEChnical FLAMes of the Universities Heidelberg, Karlsruhe, Stuttgart and the DLR

„IF YOU CAN DREAM IT, YOU CAN DO IT“Walt Disney

By WILLI JÄGER

How to understand explosions?

spiegelges and problems, which appeared on computational humanities, the

25YEARS

SIMULA

Page 2 spiegel | February 2013

tal contributions to theory. With his work on the coulomb explosion, his group has provided a rare examp-le of the prediction of an unknown effect, subsequently also shown by experiment. The IWR was strongly supporting the setting up of a new group in computational chemistry in the inorganic chemistry depart-ment in 1992. Peter Comba who took this position is involved in both experiments and theory and, with a DFG-funded graduate college (Molecular Modeling) has been able to strengthen the link between the chemical institutes and IWR. Jere-my Smith who had an IWR chair for

Computational chemistry: mode-ling and simulation in the central

science. What a combination! It is not unexpected that com-

putational chemistry plays a key role

at IWR since its foundation, and this will and must continue.

Computational che-mistry is the incorpo-

ration of theory in effi ci-ent computer programs with the aim of computing structures and properties of molecules and materials. This leads to a model based interpreta-tion of experimental data for the rational design and optimization of novel systems and for the predic-tion of so far unobserved phenome-na. It comprises the optimization of structures, the computation of

interaction energies, the prediction of reactivities and thermodynamic properties (stabilities, selectivities, redox potentials), and the compu-tation of electronic properties of ground and excited states. Static as well as dynamic phenomena are being investigated on various scales (atomistic resulution vs. nanosys-tems), requiering a range of me-thodologies from ab initio to fully empirical methods. It is in the deve-lopment of appropriate models and technologies, in the defi nition of new theory and its implementation in computer programs, where the cooperation of mathematics, phy-sics and chemistry is important – a

fundamental idea of the fathers of IWR, a model which has been successful for 25 years and an institution which has produced scientifi c success.

Anovel fi eld for methods of com-putational chemistry is the op-

timization of process control, a cen-tral area in the group

of Hans Georg Bock. Another

i m p o r t a n t fi eld, often not ap-p r e c i a t e d enough in the compu-

tational che-mistry commu-

nity, is modeling and simulation of

reactive fl ows. This was one of the main areas of Jürgen Warnatz, the IWR chair of chemistry from 1994 to 2007. In collaboration with Jür-gen Wolfrum – one of the found-ing fathers of IWR – as the expe-

rimentalist in the classical „IWR couple“, Warnatz is internationally recognized for his seminal work in combustion and also for providing the physical and technical basis for modeling and simulation in other areas, including heterogenous cata-lysis. Conceptually similar types of modeling are done in environmen-tal physics, e.g. in the group of Ul-rich Platt (chemistry of the atmos-phere), another member of IWR.

Lorenz Cederbaum, holding the Heidelberg chair of theoretical

chemistry since 1979, is internatio-nally recognized for his fundamen-

high perforance computer simula-tion of biological macromolecules from 1998 to 2006 completed the link between computation and mo-lecules at larger scales and with ap-plications in life sciences.

With the new IWR chair in chemistry since 2011, An-

dreas Dreuw, involved in method development as well as applications in various areas of chemistry – in particular also in materials science – the strength and importance of computational chemistry for both, IWR and the chemical institutes, is further established.

By PETER COMBA

The Secrets of PixelsThe Heidelberg Collaboratory for Image Processing (HCI)

At IWR Chemistry at the lab is accompanied by Modeling, Simulation and Optimization. Foto: IWR

Key players of computational chemistry The Crucial Role of Modeling, Simulation and Optimization in the Molecular Sciences

MaterialSciences

MaterialSciencesSIMMaterialIMMaterialIMSciencesIMSciencesIMMaterialSciences

Computational chemistry is computer modeling, simulation and optimization in molecular sciences.

Chemistry

Material Sciences

Bio- &MolecularSciences

Physics

Science has for ever been using visual representations to re-

cord its observations and fi ndings – in the early days in the form of drawings. The fi rst revolution was ushered in by the invention of photography, which permitted the exact documentation of pictorial information. However, painsta-king and time-consuming manual evaluation had been prohibitive in allowing a quantitative analysis of such image materials. Neverthel-ess, the new technology helped scientists to discover countless new phenomena.

Today, we are experiencing a second revolution in terms of

how we acquire and process ima-ges: modern digital cameras and computers record images and are able to evaluate them quantita-tively as well. Image processing has evolved into an indispensable tool for all quantitative research undertakings. It has become the means by which scientists docu-ment and analyze quantitatively complex processes with temporal and spatial variants. Images are

generated by using a vast range of different techniques in all sorts of dimensions: from satellites orbiting earth to a diverse range of micro-scopy techniques. Regardless of the specifi c recording technique, in the end all users face the same problem: they need suitable methods for the quantitative extraction of the infor-mation of interest from the images and for comparison with models and simulations.

The point of departure for re-search in image processing

at the IWR was the question how carbon dioxide released into the atmosphere by burning fossil fuels eventually ends up in our seas. The-re was a fundamental experimental problem inherent in our research: turbulence causes carbon dioxide to mix rapidly both in the atmos-phere and in the ocean. This turbu-lent mixing becomes less effective the closer it occurs to the oceans surface. Indeed, at some point this effect becomes so weak that the mo-lecules are moving solely due their own thermal motion. This process is called molecular diffusion. In

water, it is four orders of magnitu-de slower than in the air. Thus the bottle neck of transporting CO2 into the oceans lies in a thin, just 30 to 300 μm wide boundary layer of wa-ter at the ocean’s surface. Here, the concentration gradients are the gre-atest and here, the minute residual turbulence must be measured.

How is it possible to obtain re-liable measurements in such a

thin layer, particularly while wind-generated waves cause the layer to undulate constantly? Non-contact techniques are the only possible so-lution - and image processing tech-niques fi t this premise perfectly.Firstly, visualization methods need to be developed that transform the parameters of interest into pictures. For the fi eld of small-scale air-sea interaction four measurement para-meters are of primary importance:

Continued on page 3

By BERND JÄHNE

Image processing addresses a vital bottleneck in the area of „connectomics“. Th e aim of the latter is to experimentally decipher the wiring diagram of the brain. Th e picture shows the partitioning of a high-resolution electron microscopic volume image into single neurons (data courtesy of Winfried Denk, MPI for Medical Research). Only a small fraction of all dendrites are rendered for improved visibility; the actual tissue is densely packed with such processes! Automated image analysis is a prerequisite to understanding the fundamental computational units in a brain and, hopefully in the long run, of the entire brain.

MODLINGIt‘s All About Optimization How to improve control and operation of complex processes?

Page 3 spiegel | February 2013

in gas exchange. So the concentra-tion of bubbles (number of bubbles per radius and volume) needs to be gauged and how long the bubbles remain in the water.3. The shear flow above and be-neath the water surface: This is particularly difficult, as it requires volumetric measurements. The three velocity components of the flow field must be measured in de-pendence of the distance to the wa-ter surface.4. Concentration fields of dissolved

in this instance is that no defined measuring volume exists and that sharply imaged and blurry cells appear side by side. This same problem arose when we measured the size and concentration of gas bubbles generated in the water by breaking waves. We had develo-ped a technique that could quan-titatively determine the degree of “blurriness” which allowed us to measure the distance from the cell to the focal plane and thus the actu-al diameter and size of the particles. This crucial experience resulted in a continuous extension of industrial and scientific applications for image processing. As a consequence, bet-ween 1994 and 2008, the image processing groups at the IWR grew from a single C3 professorship in 1994 (B. Jähne) finally into the Hei-delberg Collaboratory for Imaging Processing (HCI) with three full research groups (F. Hamprecht, B. Jähne, C. Schnörr) within the excel-lence initiative in 2008. In coope-ration with Robert Bosch GmbH, Heidelberger Druckmaschinen AG, Heidelberg Engineering GmbH, Silicon Software GmbH and PCO AG the HCI works towards solving essential image analysis problems. 5.6 mm

1.7 s

Penetration of oxygen into water depleted by oxygen at a wavy wind-driven water surface in a small wind-wave tunnel. Oxygen con-centrations are made visible by a fluorescent dye. The fluorescent intensity depends on the concentration of oxygen dissolved in water (fluorescence quenching). The oxygen concentration is shown in a pseudo color range. (Graph: Friedl)

1. Shape of the water surface, i.e. the water surface waves: Wind transfers energy into the waves, which is then distributed among the waves by nonlinear wave-to-wave interaction processes and finally dissipated into turbulence. This improves the mixing effect and pro-motes gas exchange.2. Air bubbles generated in the water by way of breaking waves: bubbles provide an additional ex-change surface, which also permits a potentially very strong increase

The wide range of applications such as industry quality control, indus-trial metrology, driver assistance systems, consumer applications, medicine, biology and environmen-tal sciences achieves high synergy and cooperations prove to be very successfull. With Sony Deutschland GmbH and Carl Zeiss AG two strong global player became HCI members in the 2ed phase of the excellence initiative and B. Ommer became the forth HCI director.

gases in the boundary layer: This also requires measurements with three dimensions in space and one in time.

It goes without saying that it would be very difficult indeed

to implement the aforementioned measuring techniques in situ. For this reason the ocean has to be si-mulated in the lab. To this end, a unique research facility is available at the Institute for Environmental Physics of Heidelberg Universi-ty, the Aeolotron, named after the Greek god Aeolus

One question of particular inte-rest for the IWR while develo-

ping image analysis methods, like those mentioned above, was: will it be possible to apply them to other application areas?

It is astonishing to see how many industrial applications simply

came about in the wake of envi-ronmental physics applications. For instance, industry approached us about the feasibility of counting cells directly in a bioreactor, i.e. with a microscope mounted direct-ly on the bioreactor. The problem

Today’s systems and processes considered in modeling and si-

mulation, e.g. in industry, are very complex and offer lots of possibili-ties to operate them. To find a good way of operation is already difficult. To find a truly optimal solution is practically impossible by intuition or trial-and-error - but optimization methods developed at IWR can.

They help determine fastest mo-vement of industrial robots,

operate subways with minimal energy consumption, drive heavy duty trucks automatically with in-telligent energy optimal cruise con-trol, design optimal running biped humanoid robots, operate chemical engineering process energy optimal or rediscover satellites after faulty launches.

The mathematics behind the me-thods allows to fully exploit the

potential of existing systems - and to design and operate new systems in the best possible way. Three prin-cipal steps of this approach are the modeling of the system by mathe-matical equations that represent all the physical and technical know-ledge about it, the simulation of the process under different conditions to validate the model and then to use mathematical optimization al-gorithms to compute the desired best operation. An additional dif-ficulty is the optimization of proces-ses with disturbances or changing scenarios. Here, real-time optimi-zation algorithms were developed to optimally react. And, of course, it is of utmost importance that optimal

solutions always satisfy all technical restrictions, such as speed limits for trucks or limited motor power in walking robots.

The construction of detailed ma-thematical models that precise-

ly incorporate all features of a sys-tem is a sophisticated optimization problem of its own. Certain charac-teristics of the models (so-called parameters) can typically only be determined using measurements of the real process under different con-ditions. Optimal parameters have to be found, for which the model mat-ches the measurements best.

An important further optimi-zation problem concerns the

way the necessary experiments are designed. By optimizing the experi-mental design it is often possible to determine parameters with a dras-tically reduced experimental effort. Optimal experimental design me-thods for the modeling of chemical reaction systems were developed in cooperation with chemical experts. BASF reports savings of 80% in ex-perimental effort and routinely ap-plies these algorithms.

Heavy duty trucks play a very important role for transporta-

tion of goods. For environmental and economic reasons a reduction of their energy consumption is high-ly desirable. IWR has developed a real-time optimization methods to

realize an automatic foreword-loo-king way of driving that takes into account road geometry and speed limits. These algorithms are able to solve so-called mixed-integer opti-mal control problems and select the optimal gear shift patterns out of billions of possible ones in a small fraction of a second on on-board-processors in a truck. The new real-time algorithms outperform earlier approaches by a factor of more than

10.000. Experimental evaluation of the performance of these methods led to fuel savings of 3-5% compa-red to experienced drivers!

At IWR optimization methods are steadily enhanced to meet

future challenges in diverse fields like the biped walking and running

in order to support handicapped people or the energy revolution to set up a reliable and affordable energy system.

By HANS GEORG BOCK & JOHANNES SCHLÖDER

Modeling, simulation and optimiza-tion of dynamic processes.

(Graph: IWR)

Each issue needs it‘s optimal method...

An important research theme of the HCI is the generation

of 3-D information from 2-D images. This image shows

a depth map in pseudo-color as an

overlay on a regular 2-D image observed

from a camera in a driver assistance

system through the front window of a car as an example of such a technique (here TV-

regulated structure from motion).

Future challenges

ned with a node while executing the user‘s job. If a node crashes this is obvious to the user. But if a memory error occurs, the user has to be infor-med, so he can decide if he may trust the results of this job or not.

Hermann Lauer: Beeing open to the rapid development in the large world wide community. He should always look out for hardware and software advances which fi t into the applica-tion area of the IWR. At least, never forget that this is a scientifi c envi-ronment with its special needs. You should always remember this when comparing the IWR computing facili-ties with that of other centers. Most of these computing units have diff e-rent goals which yield to completely other technical solutions in hardware and software.

tions is the real art done here at the IWR.The most important jobs for our su-percluster here are the ones testing new algorithms for scalablity. These jobs need the complete machine! But typically only for a rather short time. The second class of jobs, we are dealing with, do need cpu usage hours that are much larger although the jobs are smaller. These jobs deal with calculations around molecules, what is done by biocomputing and chemistry research.

Hermann Lauer: It‘s not our duty to run HELICS without any errors. In-deed such a task is nowadays nearly impossible - even on the world‘s most expensive systems. The important thing for us is to inform the user or scientist if something had happe-

Page 4 spiegel | February 2013

Science 3.0 - High Performance Computing at IWR

redaktion im dialog„ „

By PETER BASTIAN, HERMANN LAUER, STEFAN FRIEDEL, MARKUS RIDINGER & MARTIN NEISEN

The Parallel Gallery

Hermann Lauer and Helics IIIa in 2012. (Photo: IWR)

Th e fi gure shows a parallel simulation of density driven fl ow in porous medium and is a result of high performance computing in the IWR research group of Prof. Bastian.

The Real Story About Computing

IWR-Spiegel What are the major di� erences between an ordinary computer and a high performance machine?

To keep parallel computing systems running is not an easy job. Anybody who has ever had problems with the personal computer at home knows ex-hausting situations when the machine is not working the way it should. We talked to Dr. Hermann Lauer, head of the central ad-ministration group at IWR.

Hermann Lauer: On the hardware side it‘s mainly the interconnect. The old marketing slogan „the network is the computer“ is not completely true but a low latency myrinet or infi niband network moves a pile of common ser-vers to a supercomputer. On the software side - if you are using already open source, it‘s the paral-lel programming (mostly message based) plus the job scheduling. Wri-ting and debugging parallel applica-

Bernhard Przywara (IWR), Mahmoud Chatah (Parstec company) and the Parsytec GC. (Photo: private)

Bernhard Przywara and Willi Jäger with the new Parsytec PowerGC in 1995. (Photo: private)

Stefan Friedel and Martin Neisen in front of Helics in 2002. (Photo: Uni)

In 1965 co-founder of Intel corporation Gordon E. Moo-

re published an article that pre-dicted the exponential growth of the number of transistors one can put on a silicon chip at optimal cost. The time for dou-bling the transistor count, up to the present day, is very close to two years, a fact known as Moore‘s law. As a consequence of this, compute power and storage capacity grew with the same rate during the last 50 ye-ars. This phenomenal achieve-ment of the computer industry has a had a tremendous impact on society in general and on the fi eld of scientifi c compu-ting in particular.

The speed of supercomputers, the fastest computers in the world

at their time, grew at an even faster rate than that of normal computers. Supercomputer speed is measured in „fl oating point operations per second“ or FLOPS. The fi rst compu-ter to achieve one thousand billion FLOPS (or one TFLOPS) was ASCI Red in June 1997. Eleven years later, in June 2008, a computer named Roadrunner achieved one million billion FLOPS (or one PFLOPS). Since November 2012 the fastest computer in the world is Titan at Oak Ridge National Laboratory in the USA with 17.5 PFLOPS. This amounts to almost one doubling of compute speed per year. What is also interesting is that today‘s desk-

top and laptop computers are the supercomputers from yesterday, e.g. even a tablet computer today is as powerful as the Cray 2 supercom-puter, the fastest computer in 1985 which achieved two GFLOPS, cost US$ 17 million and needed a 195 kW power supply!

As an important characteristic today‘s computers - from lap-

top to supercomputer - contain se-veral processors that work in paral-lel. In fact, the only way to make a program execute faster today is by subdividing the task at hand into several subtasks that are then exe-cuted in parallel, a process called „parallelization“. This is a relatively new development in every day com-puting as until around 2004 pro-grams just became faster by putting them on a new computer. However, in the fi eld of high-performance computing the shift from sequenti-al processing to parallel processing happened more than twenty ye-ars ago, the Cray 2 supercomputer mentioned above had already four processors (same as in a laptop or smartphone today). Since the early nineties the fastest computer in the world has always been a massively parallel computer employing at least 1000 processors.

The IWR was an early adopter of massively parallel computing

technology and played an important role in the development of effi cient parallel algorithms for scientifi c computing applications since its foundation. Behind that commit-ment to high-performance com-puting stands the observation that latest computer hardware needs to be combined with new methods in mathematical modeling, simu-lation and optimization to achieve the overall best solution. There are many examples, where the impro-vement of the algorithm used to solve a problem provided at least as much reduction in computing time as Moore‘s law. IWR‘s fi rst parallel computer system was installed in 1989 and parallel computer systems have been operated since then (see Table). The fi rst system was built by German company Parsytec and was

based on the INMOS transputer, a chip combining good fl oating point

performance with an on-chip net-work that allowed it to communi-cate directly with four neighboring transputers. The second system was also built by Parsytec based on Pow-erPC processors for computing and transputers for communication. While the fi rst two parallel compu-ters were designed with the direct support of IWR, Helics the third machine, was a cluster system con-sisting of „commercial off the shelf“ components. 256 standard desktop computers fi lled 22 racks in the computing center‘s machine room. Helics was equipped with a myrinet 2G low latency network. This ma-chine even achieved number 35 in June 2002 of the „TOP500 list“, a regularly updated list of the 500 fas-test computers in the world. Clus-ter systems consisting of standard components are now a commodity. The current systems Helics2 and Helics3a employ multicore proces-sors as well as fast communication networks which are necessary for many codes to parallelize effi cient-ly. For 2013 it is planned to acquire the next generation parallel compu-ter system.

Parallel computing is now ubi-quitous in scientifi c computing

and continues to play an important role on IWR‘s research agenda, e.g. starting in 2013, two new projects coordinated by IWR researchers within the DFG priority program 1648 „Software for Exascale Com-puting“ will develop algorithms and software for the next generation of supercomputers capable of execu-ting one billion billion FLOPS. The-se systems, anticipated to go into operation in 2018, are expected to be able to execute 1 billion tasks in parallel. A tremendous challenge for algorithm development.

A� er 25 years research with supercompu-ters... what is the most im-portant skill a supercomputer administrator should come along?

IWR-Spiegel

Your team has to make sure that the HELICS systems runs without any er-rors. What would happen if the system fails and how could you recognize that?

IWR-Spiegel

Rolf Rannacher and Georg Bock with Helics II in 2007. (Foto: RNZ)

Hermann Lauer and Helics IIIa in 2012. (Photo: IWR)Hermann Lauer and Helics IIIa in 2012. (Photo: IWR)Impressum:

Interdisziplinäres Zentrum Für Wissenschaftliches Rechnen- Public Relations -

Im Neuenheimer Feld 368D-69120 HeidelbergSabine Kluge (Red.)Tel.: 06221 54 8854

The Parallel GalleryThe Parallel Gallery