- Algorithms & methods for accelerating molecular dynamics
- Parallelization and acceleration of molecular dynamics on modern high performance computing architectures
- High performance computing, manycore and heterogeneous architectures, GPU computing
Molecular dynamics simulations (MD) offer a great advantage compared to laboratory experiments by giving unique insights into dynamic processes in biomolecules with an exceptional atomistic detail. However, one of the major bottlenecks in MD studies is sampling. In practice, this translates to computational experiments taking as long as weeks or months. My work aims to contribute to the field with methods and algorithms that improve the efficiency of biomolecular simulations by focusing on the following two aspects:
- Sampling the same amount in less time by speeding up simulations. We approach this computational problem by developing algorithms for modern parallel architectures. In our recent work we developed algorithms targeting modern processor architectures and an efficient parallelization scheme for emerging heterogeneous high performance computing platforms. Through this work we managed to substantially improve absolute performance and scalability of MD simulations using the state-of-the art GROMACS package. Our work enables thousands of users worldwide to achieve 2-5x higher absolute simulation performance.
- Sampling more in the same amount of time by improving ensemble methods. We worked on using parallel simulations which exchange information to increase the efficiency of calculating free energy differences.
The goal is to combine the developed methods and algorithms with the advantages of the distributed computing platform Copernicus developed in our group.
Education & Background
2011- PhD in computational biophysics at KTH Royal Institute of Technology: Parallel algorithms for molecular dynamics simulations.
2009-2011 researcher in computational biophysics at Stockholm University.
2008-2009 software engineer and member of the scientific staff at Software Competence Center Hagenberg
Worked as R&D software engineer on various problems in the field of computational intelligence related to GPU acceleration and machine learning.
2008 MSc in computer science from Johannes Kepler University, Linz, Austria
Master's thesis: “GPU Computing Approach for Parallelizing Support Vector Machine Classification”
2007 BSc in computer science from Babeș-Bolyai University, Cluj-Napoca, Romania
Diploma work: “Text Categorization-based Spam Filtering”