Unlocking Faster Graph Analysis with Parallel Computing

During his PhD studies, Cheng Huang researched how large and complex graph problems can be solved efficiently on modern parallel hardware. Cheng Huang designed new parallel algorithms for graph analysis and mining, with a particular focus on multi-core CPUs, GPUs and IPUs. His research improved computational efficiency, scalability and memory usage for several graph analysis and mining tasks.

The new research findings contribute to faster analysis of large-scale graph data and provide practical methods for making better use of modern computing hardware.

The PhD study was completed at Department of Computer Science, Faculty of Natural Sciences, Aarhus University.

This summary was prepared by the PhD student.

Time: Monday, 28 September 2026 at 13:00
Place: Building 5342, room 333, Lecture Theatre 5342-333, Department of Computer Science, Aarhus University, Åbogade 34, Aarhus 8200
Title of PhD thesis: Scalable Graph Analytics on Modern Parallel Architectures
Contact information: Cheng Huang, e-mail: cheng@cs.au.dk, tel.: +45 55255746
Members of the assessment committee:
Professor Fragkiskos Malliaros, CentraleSupélec, Université Paris-Saclay, France
Associate Professor Pinar Tözun, Data, Systems and Robotics Section, IT University of Copenhagen, Denmark
Associate Professor Sophia Yakoubov (chair), Department of Computer Science, Aarhus University, Denmark
Main supervisor: Associate Professor Davide Mottin, Department of Computer Science, Aarhus University, Denmark
Co-supervisor: Professor Ira Assent, Department of Computer Science, Aarhus University, Denmark
Language: The PhD dissertation will be defended in English

The defence is public.
The PhD thesis is available for reading at the Graduate School of Natural Sciences/GSNS, Ny Munkegade 120, building 1521, 8000 Aarhus C