Focus
The group consists of two work groups that are headed by their respective group leaders. Their research is organized into the following key areas, which are reflected in our projects and publications.
- Parallel and Distributed Systems, Univ.-Prof. Dipl.-Ing. Dr. Siegfried Benkner
- Distributed Cloud and Edge Systems, Ass.-Prof. Dott. mag. Dr. Atakan Aral
Work group “Parallel and Distributed Systems”
The work group led by Univ.-Prof. Dipl.-Ing. Dr. Siegfried Benkner focuses on the following key areas:
Parallel Programming Systems
We design and develop innovative methods and tools for the efficient and cost-effective creation of high-performance parallel and distributed applications, with a strong emphasis on portability and sustainability. Our work spans next-generation programming paradigms, languages, compilers, and runtime systems, and focuses on performance optimization with particular attention to energy efficiency, as well as automatic performance tuning and adaptive, self-optimizing systems.
High-End Heterogeneous Computing
We pioneer techniques for optimizing and efficiently deploying complex applications across heterogeneous, large-scale computing infrastructures, bridging the gap between hardware diversity and application performance. Our research covers heterogeneous parallel systems, including GPUs and specialized accelerators, high-performance computing systems and leadership-class supercomputers, as well as cloud infrastructures and large-scale distributed environments.
Advanced Applications in HPC, AI, and ML
We drive research and innovation through the application of high-performance computing, machine learning, and artificial intelligence to solve complex real-world problems across disciplines. Our application domains include engineering, physics, and chemistry, as well as medicine and life sciences, and extend to finance and economics.
Work Group “Distributed Cloud and Edge Systems”
The work group led by Ass.-Prof. Dott. mag. Dr. Atakan Aral focuses on the following key areas:
Cloud, Edge, and Continuum Computing
We design distributed systems that span the cloud–edge continuum, from centralized cloud infrastructures to resource-constrained edge devices. Our research focuses on scalable architectures, orchestration, and scheduling across heterogeneous and geographically distributed environments. Particular emphasis is placed on supporting data-intensive and latency-sensitive applications that require coordinated processing across multiple layers of the continuum.
Edge AI and Intelligent Resource Management
We study how artificial intelligence can be efficiently deployed on edge infrastructures and how it can improve the operation of distributed systems. This includes lightweight inference, distributed and federated learning, and AI-driven optimization for dynamic and efficient resource management. We are particularly interested in adaptive methods that react to workload changes, failures, and varying network or energy conditions in real time.
Sustainable IoT and Cyber-Physical Systems
We develop energy-aware sensing, monitoring, and decision-making systems for real-world applications, including environmental monitoring and critical infrastructures. Our work explores brain-inspired and neuromorphic approaches, as well as event-driven and communication-efficient architectures, to enable sustainable and resilient operation in remote settings under tight network, power, and hardware constraints.
