Use Cases

Examples of the problems and topics on which ABACUS 2.0 is playing a key role




RNA 3D structures

Principal Investigator: Assistant Professor Jing Qin

Faculty of Science - Department of Mathematics and Computer Science (IMADA) - SDU

In this project we study the three-dimensional folding structure of RNA molecules. Understanding folding of RNA is important as wrongly folded molecules could lead to serious diseases. We use a computer simulation method called “molecular dynamics” for studying the physical movements of atoms and molecules. On ABACUS 2.0 molecules are represented as 3-dimensional geometrical data and a single simulation of one experiment requires about 7000 simulation steps. This method requires a tremendous amount of computer simulations and it can take many years on a normal computer to complete only one experiment.


Meeting current challenges in computational spectroscopy on a supercomputer

Principal Investigator: Professor Jacob Kongsted

Faculty of Science - Department of Physics, Chemistry and Pharmacy (FKF) - SDU

We develop and apply quantum chemical methods to study biological systems. We are especially interested in defining rational design strategies for development of novel functional optical biological materials relevant for example for light-harvesting and biological sensing. For our research, use of supercomputers like ABACUS 2.0 is of utmost importance – such computing facilities represent our laboratory and this is where we do our experiments using the computational strategies we develop.


Membrane Transport and Shape Remodeling Using True Multi-scale Simulations

Principal Investigator: Associate Professor Himanshu Khandelia

Faculty of Science - Department of Physics, Chemistry and Pharmacy (FKF) - SDU

We are interested in the molecular and biophysical processes occurring in proteins and membranes. We use highly parallel numerical integrators to study the self-assembly and dynamic behaviour of biological complexes that drive life. Such highly parallel computations are only possible on large scale HPC resources, because we study the dynamics of millions of particles, which cannot be performed on desktop computers. Using our computations, we have discovered new transport pathways across cellular membranes, have examined the dynamics and mechanical properties of the Zika virus, and describe the molecular processes involved in the formation of organelles that control fat metabolism.


Bond Repulsive models of stiff polymer gels

Principal Investigator: Carsten Svaneborg

Faculty of Science - Department of Physics, Chemistry and Pharmacy (FKF) - SDU

Gels made of stiff polymers are of interest for multiple reasons. Fundamentally, their topological entanglement properties are quite different from dens melts of flexible chains, secondly many biomaterials such as f-actin gels are composed of stiff biological polymers. Computationally, naively simulating stiff polymers with bead-spring models are expensive, since stiff chains require angular potentials with high spring strengths, and hence require small time steps, and many beads are required to decorate the chain. Recently, it became possible to add pair interactions not between beads, but between the bonds connecting beads. Such interactions allow for computationally much more effective models of stiff polymer gels, since much fewer beads are required to model a stiff polymer. Secondly, the bond pair interactions can be designed to ensure gel topology is conserved. In the present project, we investigate and benchmark how to apply these computational techniques and optionally how to map the gel models onto biological gel systems.


Chemically engineered nucleic acids and biogenesis of the bacterial cell wall

Principal Investigator: Michael Petersen

Faculty of Science - Department of Physics, Chemistry and Pharmacy (FKF) - SDU

Computational chemistry is emerging as an extremely powerful addition to the experimental tool box of chemistry and biology. Calculations performed on HPC systems have the potential to both explain and guide experiments. In this project, we use computational methods to study the structure and stability of chemically engineered nucleic acid duplexes as well as to study a key process in the synthesis of the bacterial cell wall. The projects will provide new insight and understanding at an atomic level.


Evaluation of Outlier Ensemble Methods

Principal Investigator: Arthur Zimek

Faculty of Science - Department of Mathematics and Computer Science (IMADA) - SDU

For an evaluation study on unsupervised outlier detection and ensemble techniques, we run various methods with various parameterizations on many datasets, resulting in ca. 4.5 mio. individual experiments which are to a certain extent independent of each other and try to see what the benefits of parallelization are on this effort.


Magnetic field sensing

Principal Investigator: Ilia Solov´yov

Faculty of Science - Department of Physics, Chemistry and Pharmacy (FKF) - SDU

Migratory songbirds can perceive the Earth’s magnetic field for the purpose of navigating their exceptional voyages or orienting in their local habitats. One particular modality of this sense, a magnetic inclination compass, is thought to rely on magnetically sensitive radical pairs formed photochemically in cryptochrome proteins in the animal's retinae. This process is a striking example of a truly quantum mechanical process in sensory biology. It necessitates long-lived electron spin coherences in the radical pair.

An important requirement of this hypothesis is that the electron spin relaxation is slow enough for the Earth’s magnetic field to have a significant effect on the coherent spin dynamics of the transient radical pair. This proposal aims to elucidate spin relaxation pathways based on a comprehensive analysis of the thermally induced motions in the radicals and their surroundings.




Understanding the fundamental structure of the Universe

Principal Investigator: Prof Claudio Pica

Faculty of Science - CP3-origins - Department of Mathematics and Computer Science (IMADA) - SDU

Researchers at the CP3-Origins centre of excellence use the ABACUS 2.0 supercomputer to study new theoretical models to explain how the Universe is made at its most fundamental level. Supercomputers help researchers where experiments and observation reach their limits.

With the Nobel-prize discovery of the Higgs boson in 2012 after decades of searches, the last missing piece of the Standard Model of particle physics was put in place. Nonetheless many are the mysteries that the Standard Model cannot explain: why is the Higgs so light? why is there more matter than anti-matter? what is dark matter and dark energy?

At the CP3-Origins centre for Cosmology and Particle Physics Phenomenology, researchers are exploring the idea that the Higgs particle is made up of smaller pieces, i.e. it is a composite particle that behaves almost identically to the Standard Model Higgs boson. The existence of these new smaller pieces could explain why the Higgs is so light and at the same time they could be re-arranged to form dark matter particles not present in the Standard Model.

Understanding the dynamics of these composite particles is no easy task, as the    theoretical models are so complex that no exact solutions exists. To get an accurate picture, researchers at CP3-Origins rely on the use of state-of-the-art supercomputers, such as ABACUS 2.0.



Statistical Mechanics, Theoretical and Computational Physics of Soft-Condensed Matter

Principal Investigator: Postdoc. Federica Lo Verso

Faculty of Science – Department of Physics, Chemistry and Pharmacy (FKF) – SDU

My research focuses on multi-scale modelling of complex systems with different architectures and microscopic interactions under various geometries and environmental conditions, in order to target new material properties and performance. The systems at hand are characterised by different length scales, and the system sampling usually involve a large number of particles (monomer/bead units) and of run necessary in order to get good statistics.

I was involved in three different projects running on ABACUS 2.0.

My main project involved the computational synthesis of microgels, obtained via inter-molecular cross-linking of polymers. We accurately characterised the morphology and structure as well as the kinetics of the cross-linking process. We also studied the swelling and deswelling behaviour.

One project-student I supervised performed simulation with LAMMPS in order to explore the mechanisms behind the anomalous diffusion of particles in a membrane from a numerical point of view in order to test theoretical previsions. This project was only preliminary, and gave insight in order to improve the properties of the model we used.

The second project-student I supervised simulated polymers in crowded environments. The student used an idealized model, and analysed the effects of crowding on polymers, characterising the response in terms of the dynamics, through the Mean Squared Displacement, and in terms of polymer structure via the calculation of asphericity, end-to-end distance and polymer pro lateness.



Theoretical Condensed Matter Physics

Principal Investigator: Professor Thomas G. Pedersen

Department of Physics and Nanotechnology - Aalborg University (AAU)

One of the research objectives our group is currently pursuing is to calculate the linear and non-linear optical response of certain low-dimensional semiconductor materials in external electro-magnetic fields. The optical response is important if such materials are to be used in opto-electronic devices such as photo-detectors or light-emitting diodes.

We use a number of numerical methods to calculate the band structure and the optical response of these semiconductor materials, including DFT calculations from software packages such as ABINIT and also our own code. Calculating the band-structures and optical response of materials in external fields is often computationally demanding but can parallelized and is highly suited for the use of HPCs.


Life Sciences and Precision Medicine



Epigenetic biomarkers as predictors of late-life mortality in the Elderly

Principal Investigator: Professor Qihua Tan

Faculty of Health Science - Epidemiology, Biostatistics and Biodemography - Human Genetics - SDU

The research is about genetic epidemiology and medical bioinformatics on human complex diseases, aging and development. HPC provides an irreplaceable platform for increasing computational load and demanding data storage capacity big data in medical genomics. The data run on ABACUS 2.0 is Genome-wide SNPs genotype data and whole genome DNA methylation sequencing data.

We performed a genome-wide association study on cognition and an article have been published. The methylation sequencing data is still running.



Brain Computer Interfaces

Principal Investigator: Assistant Professor Mads Jochumsen

Center for Sensory-Motor Interaction (SMI) - Department of Health Science and Technology - Aalborg University (AAU)

Our work concerns Brain Computer Interfaces, translating EEG into control signals for a variety of assistive devices. EEG is very dense data and can be analyzed in many ways. Thus, HPC is in high demand for explorative EEG studies. ABACUS 2.0 enabled us to extract and evaluate a large number features of the EEG recorded during internal speech, and thus reveal patterns that enabled a Random Forest algorithm to predict the internally spoken words. The preliminary results show a >70 % classification accuracy in a 6-class problem.


Whole genome sequencing analysis of hereditary breast cancer

Principal Investigator: Mads Thomassen

Faculty of Health Science - Department of Clinical Research - Human Genetics - SDU

Breast cancer is the most common cancer among women. Up to 10% of all cases are inherited. Since breast cancer is associated with high mortality patients with a strong family history of breast/ovarian cancer are referred to screening of the high-risk genes BRCA1 and BRCA2. In families where a mutation is identified, half of the daughters of a mutation carrier will not carry it and they can be spared prophylactic mastectomy and oophorectomy. Patients with BRCA1/2 mutations have been shown to respond better to certain types of chemotherapy and a new targeted treatment, PARP inhibitors. A major problem in clinical managing is that mutations in BRCA1/2 are only identified in app. 10% of referred families hampering counseling and treatment.

We will seek to improve treatment and counseling of hereditary breast cancer by identifying molecular subtypes and the missing causal genetic factors for non-BRCA1/2 familial breast cancers.



Genome-wide and epigenome-wide association study with sequencing and chip data

Principal Investigator: Qihua Tan

Faculty of Health Science - Epidemiology, Biostatistics and Biodemography - Human Genetics - SDU

Body mass index is an important indicator for determining obesity, which is highly correlated with cardiometabolic problem like coronary heart disease. It is contributed form multiple genetic and epigenetic factors. In this study, we focus on DNA methylation study and use twins discordant for BMI to control the genetic factors. Our results could benefit the public in the aspects of improve life quality and drug development. We have collected 30 twin pairs and measure their methylation level using reduced presentation bisulfite sequencing (RRBS) technology. RRBS is based on sequencing technology and it is a relatively new. The pipeline for processing such type of data is rather complex. Millions of reads need to be processed and mapped onto reference human genome, and the process need large amount of computational power.





Optimization of Deformable Object Manipulation Through Simulation

Principal Investigator: Professor Norbert Krüger

Faculty of Engineering - The Maersk Mc-Kinney Moller Institute - SDU Robotics - SDU

In this project, we study simulation and optimization for robotic manipulation of deformable objects (e.g. meat). Simulation of deformable objects requires a substantial amount of computation, and when optimization is placed on top of this hundreds of deformable object simulations are required. Furthermore, to analyze the optimization several optimization runs also have to be evaluated.

Robotic task tends to contain some control parameters, some objective and some uncertainties. Numeric optimization is a powerful tool to pick a set of control parameters that maximize the objective score. But to ensure the uncertainties doesn't affect the solutions some extra steps must be made. When considering the importance of minimizing the impact of uncertainties, function fitting optimization techniques in particular “RBFopt” are powerful optimization tools.



Monitoring of invasive species using drones

Principal Investigator: Assistant Professor Henrik Skov Midtiby

Faculty of Engineering - Mærsk Mc-Kinney Møller Institute - SDU Dronecenter - SDU

We work on recognizing objects of interest in images from natural environments. A case is to detect the presence of giant hogweed plants in an image. We use convolutional neural networks (CNNs) to detect these plants. The use of CNN requires access to suitable computing resources, especially graphics processing units GPU. We now have a CNN that reliably can detect giant hogweed in images, the next step is then to limit the number of false positive detections by the CNN.



THERMCYC – Advanced Thermodynamic Cycles

Principal Investigator: Associate Professor Kim Sørensen

Faculty of Engineering - Department of Energy Technology - AAU

We research the adhesive nature of small micron-sized particles in a turbulent flow. Due to the small size of the particles, inter-molecular forces such as van der Waals attraction becomes important. Due to the complex interaction between the adhesive particles and a turbulent flow, experimental studies are almost impossible to control to a satisfactory degree.

In our work, we therefore rely on numerical simulations using Large Eddy Simulations (LES) to the Discrete Element Method (DEM) to simulate how a high number of particles agglomerate in a turbulent flow. As particles collide over time intervals much smaller than the overall agglomeration process, these simulations tend to be computational expensive. Based on preliminary results published in Powder Technology , we have used ABACUS 2.0 to parametrically investigate how particle and fluid properties affect particle agglomeration.

As the computational time scales almost linearly with number of cores for our simulations, we have been able to run simulations that would not have been possible without ABACUS 2.0.



Data Analytics on Occupancy Data

Principal Investigator: Associate Professor Mikkel Baun Kjærgaard

Faculty of Engineering - The Mærsk Mc-Kinney Møller Institute - Center for Energy Informatics - SDU

My research is about Energy Informatics, Ubiquitous Computing and Data Science.HPC is important for my research because it allows us to scale our analysis to larger datasets and to explore the impact of more parameters. The data, I have run on ABACUS 2.0 is sensing data on the presence of people in buildings and associated energy data.

We have developed an algorithm to process sensing data about presence of people in buildings to estimate occupation properties. In our evaluations we have demonstrated that it can process sensor data to accurately estimate occupation properties. The estimated data can among others be used to analyse the energy consumption in buildings in relation to occupation patterns.



Light-matter interaction at the nanoscale

Principal Investigator: Professor Sergey I. Bozhevolnyi

Faculty of Engineering - The Mads Clausen Institute - SDU NanoOptics - SDU

At SDU Nano Optics, we utilize the strong interaction of light with nanostructured components to conduct research within the fields of quantum plasmonics and optical metasurfaces, where the accurate description of the systems requires a simultaneous knowledge of the distribution of the electromagnetic field at both the nano- and micrometer scale, which makes it a computationally demanding task. The calculations performed on ABACUS 2.0 are all depending on the finite element approach (FEA) implemented in the commercial software Comsol Multiphysics. Based on the simulation results, we have demonstrated various optical functionalities, such as focusing, beam deflection, integrated single-photon source and surface plasmon polariton couplers.



Fault Detection and Prediction in Offshore Wind Turbines

Principal Investigator: Associate Professor Esmaeil S. Nadimi

Faculty of Engineering - Maersk Mc-Kinney Moller Institute - SDU Embodied Systems for Robotics and Learning- SDU

The research efforts we address with ABACUS 2.0 are related to data volume. The variety of sensor installed in wind turbines offer the unique opportunity to use large scale data to predict events altering the wind turbines’ performance. The problem at hand is the limited abilities of handling those data by local workstations. ABACUS 2.0 offers the platform to handle large scale data and perform analysis within reasonable time compared to human efforts. With ABACUS 2.0 we are turning various data types (times-series, logs, and other sensory data) into valuable insight, such as the remaining lifetime of turbines. Recent results have shown that learning from large scale wind turbine data can provide prediction horizons of several month prior to a failure of a wind turbine. In the future we want to utilize the parallel computing power to decrease the dimension of wind turbine sensor data into more informative data series. Ultimately obtaining knowledge about the underlying root cause for failures in wind turbines.



Mathematical Modeling of ultrasound propagation in multi-phase flow

Principal Investigator: Associate Professor Jost Adam

Faculty of Engineering - The Mads Clausen Institute - SDU NanoSYD - SDU

Ultrasonic flow meters are used in a wide range of applications. This project specifically focuses on the particular problem of measuring flow in multi-phase flow condition, where the flow media consists of two or more substances. One industrially relevant example is the so-called bubbly flow scenario, where the flowing media is a gas/liquid mixture. In state-of-the art flow meters, this inhomogenity significantly affects the measuring accuracy, since ultrasonic signals get scattered from the second phase, adding distortions to the received signal. In practice, this leads to highly error-prone, unreliable measurement results. In this project in collaboration with Siemens Flow Instruments, we develop a numerical model that helps better understand and predict measurements in this screnario, with the ultimate goal of overcoming the described problem.

As it is nearly impossible to establish a measurement setup, where multiphase flow conditions would be sufficiently controlled to compare a single measurement to single simulation, we have to introduce a statistical modelling approach. Series of simulations hence need to performed for various phase distributions, flow speeds and flow profiles, in order to look for common received signal signatures. To this end, we implement our model in Matlab and run this vast number of necessary simulations in a highly parallel fashion on the ABACUS 2.0 supercomputer.



Evolutionary Robotics and Embodied Cognition

Principal Investigator: Assistant Professor Andrés Faíña

Robotics, Evolution, and Art Lab- Department of Computer Science - IT University of Copenhagen

We work on designing robotic morphologies and controllers for locomotion tasks by using evolutionary algorithms. Specifically, we employ modular robots, simple and autonomous devices that can be assembled together to achieve a functional robot. In ABACUS 2.0, we run physical simulations to evaluate the suitability of a robotic morphology and a controller. As these simulations are very time consuming, they are carried out in parallel in a cluster. Our results show that certain module´s geometries and generative encodings can greatly speed up the optimization process. Additionally, the best robots can be assembled and tested in reality, which give us a measurement of their performance. Comparing real and simulated results, we can shed light on how to reduce the reality gap.



Cyber security

Principal Investigator: Associate Professor Jens Myrup Pedersen

Department of Electronic Systems - Aalborg University (AAU)

My research field is cyber security, where we are mainly using machine learning algorithms to classify network traffic. Our aim is to be able to distinguish malicious traffic from benign traffic, thus allowing for detecting computers/devices infected with viruses, trojans, botnets, and other kind of malware.

The experiments where HPC helped us dealt with correlation of alarms from Intrusion Detection Systems using Neural Networks. Without HPC the training time was very high, which made it impossible for us to carry out the experiments - HPC reduced the training time by a factor of 35.

The aim of this particular work was to correlate alarms from IDS. The problem is that operators receive so many (false) alarms that it is hard to react timely and properly to true alarms. So these alarms and underlying features are what we have been running on ABACUS 2.0. The preliminary results indicate that the proposed method produces approximately 10 fold reduction in number of alerts, which significantly reduces the need for involvement of a human analysts.



Computational hydrodynamics for marine applications

Principal Investigator: Associate Professor Claes Eskilsson

Department of Civil Engineering, Aalborg University (AAU)

I work with computational hydrodynamics for marine applications and use ABACUS 2.0 to do CFD simulations of wave energy converters that can’t be properly simulated with standard linear diffraction/radiation models. These computations are very computationally expensive so access to HPC is a must. The idea is to use the results from the CFD simulations to build nonlinear static blocks to be added to the faster linear solver, in order to approximately incorporate the highly nonlinear wave overtopping in the linear models.

Photo: Simulation of the WaveDragon wave energy device using OpenFOAMs two-phase RANS solver coupled to an in-house mooring dynamics model.



An Effective Colorectal Cancer Screening Using Deep Learning

Principal Investigator: Associate Professor Esmaeil S. Nadimi

Faculty of Engineering - Maersk Mc-Kinney Moller Institute - SDU Embodied Systems for Robotics and Learning- SDU

The aim of this project is to rethink the current standard colorectal cancer (CRC) screening program and significantly improve the efficiency in terms of accuracy, acceptability, reduced complication rate and cost effectiveness.

An endoscopic camera pill produces up to 500,000 images per patient. All the recorded images are investigated manually by trained nurses which results in inefficient use of personnel. Each year, approximately 130,000 patients will go through screening. We are developing a new deep learning algorithm to fully automatise this process. ABACUS 2.0 offers the platform to handle large scale data and will be used for machine learning algorithms and big data analytics for detection of any cancer precursor, such as polyp detection, classification and characterization.


Quantum Plasmonics

Principal Investigator: N. Asger Mortensen

Faculty of Engineering - Center for Nano Optics - The Mads Clausen Institute - SDU

Plasmonics studies collective oscillations of free electrons, typically excited by light, in conducting media such as metals. While these interactions are commonly treated classically, we focus on situations where classical electrodynamics is interfacing regimes with quantum physics.





Simulating terrestrial carbon cycle processes

Principal Investigator: Associate Professor Guy Schurgers

Department of Geosciences and Natural Resource Management, University of Copenhagen (KU)

We do research on the interactions between terrestrial ecosystems and its physical and chemical environment, e.g. on the exchange of carbon dioxide and reactive trace gases between plants and the atmosphere, with a specific focus on high-latitude ecosystems. To do simulations at regional or global scale with these terrestrial ecosystem models, we use HPC systems such as ABACUS 2.0.

These results can help us to understand how future climate change will affect the ability of different ecosystems to take up carbon dioxide from the atmosphere. Because carbon dioxide is a potent greenhouse gas, understanding how much terrestrial ecosystems can take up and how much will remain in the atmosphere will help us when projecting future changes in the climate system.





Digital Research Infrastructures and eScience in Archaeology & Heritage

Principal Investigator: Associate Professor Jens-Bjørn Riis Andresen

School of Culture and Society - Department of Archeology and Heritage Studies – Aarhus University (AU)

Archaeological data, information, and knowledge is increasingly recorded, stored, managed, processed, and disseminated on digital media as its primary carrier. This development constitutes both a challenge and opens new avenues of possibilities for the discipline. Only through inter-disciplinary corporation satisfactory solutions can be accomplished. We participate in initiatives on both EU (; and national level (DIGHUMLAB), where research initiatives have been launched to facilitate seamless work-flows and long-term storage solutions for digital data. We focus on space and time modelling, dimensions which are of central importance in archaeological reasoning. Other key research areas are: 3D visualization & modelling, remote sensing, predictive modelling, digital heritage management, excavation recording, the semantic web, user / community participation. We have recently created a national section of the CAA and have organized the first Nordic conference in May (

HPC is important for our research because some of the applications are time-critical and one needs a computer with necessary power to get the job done in time. The data, we are running are primarily applications for digital field recording – computer vision applications.

Our results so far is described in the paper “Supercomputing at the trench edge”. The paper is accepted for the global CAA-proceedings Oslo 2016.