Distributed systems Seminar 2020/21 spring

List of possible topics

1. Parallel Scientific Applications and Concurrent Computing (Eero Vainikko)

• Parallel implementation of Domain Decomposition Methods; wave propagation problems (Eero Vainikko, Mohammad Anagreh)
  • GPU-accelerated Krylov methods - starting with implementation of Conjugate Gradient solver - a universal solver for systems with symmetric (or Hermitian) matrices, suitable for a class of Helmholtz problems, in particular; MINRES method, block MINRES or GMRES method. But these can be used in an acceleration of many other applications. The focus will be on creating automatic parameter choice strategies for a given problem.
• Ńumerical methods utilizing mixed precision arithmetic
  • Abdelfattah, A., H. Anzt, E. Boman, E. Carson, T. Cojean, J. Dongarra et al., A Survey of Numerical Methods Utilizing Mixed Precision Arithmetic, Innovative Computing Laboratory University of Tennessee, Tech. Report, 2020.
• Multiprecision arithmetics:
  • Multiprecision arithmetics in preconditioning techniques in iterative solvers
  • Multiprecision arithmetics for power-saving purpose on different devices
  • Posit Arithmetic vs Interval Arithmetic - Gustafson and Yonemoto, "Beating Floating Point at its Own Game: Posit Arithmetic" http://dx.doi.org/10.14529/jsfi170206 (+ other papers)
• Parallel programming environments, languages and programming practices
  • Parallel profiling tools and best practices
  • Recent hot topics in Distributed Systems development
  • Parallel performance profiling
  • etc.

2. Distributed Systems, Network Applications (Artjom Lind)

Covering the topics related to development and applied research of distributed computing and network protocols.

• Example topics:
  • SUMO Simulator: add support for concurrent execution
    • Simulator of Urban Mobility (SUMO) is an open source, highly portable, microscopic and continuous multi-modal traffic simulation package designed to handle large networks. Current implementation performs all the road network related routines (simulation, calibration etc.) using single thread hence under-utilizing the multi-core CPU. The objective is to achieve better CPU utilization by allowing multiple concurrent threads within one simulation.
  • DASK: Scalable analytics in Python
    • Evaluate DASK distributed computing framework in respect to various scientific computing tasks.
• Individual topic -> Contact me!

3. Applied Computer Vision (CV) (Artjom Lind)

Mostly the topics related to the application of the latest results in CV. In this area, we mostly use OpenCV library, which is recommended but not obligatory. The several topics we can focus on:

• Structure from motion
• Object detection/classification
• Object tracking
• Optical Character Recognition (OCR)
• Augmented Reality
• Example topics:
  • State-full Masking of Dynamic Objects for Visual Simultaneous Localization and Mapping
    • Advancing in the direction of reducing the time complexity of masking the moving objects Visual SLAM input. It was proven in previous research MaskRCNN is accurate but can hardly achieve 10FPS. Objective is to employ state estimation techniques to track the moving objects and update the masking information faster then actual detection rate.
• Individual topic -> Contact me!

4. Parallel Machine learning algorithms (Artjom Lind, Amnir Hadachi)

• Optical Character Recognition (OCR) algorithms for Estonian and non-latin scripts such as Arabic / Cyrillic / Chinese / Farsi / Hebrew / Hindi / Japanese / Korean
• Road type recognition and detection
• Object detection and recognition

5. Modelling and analyzing semantic trajectories (Amnir Hadachi)

• Trajectory filtering
• Map-matching
• Movement episode detection
• Conceptual modelling
• Semantic modelling

6. Mobility data modelling and representation (Amnir Hadachi)

• Trajectories and their representation
• Trajectory collection and reconstruction
• Uncertainty in mobility data
• Data mining and human mobility behaviour
• Visual analytics of mobility

7. GPGPU (Mohammad Anagreh)

• vs

8. Autonomous Driving (Naveed Muhammad)

• Behaviour modelling and prediction in autonomous driving
  • Safe and efficient entry into a roundabout
  • Human vs. machine behaviour prediction in a roundabout scenario