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High Performance Computing

High-Performance Computing aims to use the hardware at hand in the most efficient way which is crucial for calculations that require large processing power and especially for applications where processing time is of vital importance. The changes in the structures of computer clusters and communication networks, multi-core processor architectures and additional accelerators such as graphics processing units and the performance variations due to these hardware complexity require the development of innovative parallel methods to be run on different hardware for high-performance big data analysis.

Funded Projects
  • 2015-2017, TÜBİTAK 2232 (No: 115C018): "High-Performance Big and Streaming Data Analysis", K. Kaya
  • 2018-2020, TÜBİTAK 3501 (No: 117E249): "High-Performance Computing Algorithms for the Hypergraph Fragmentation Problem", K. Kaya 
  • 2020-2023 (H2020-JTI-EuroHPC): ''SparCity: An Optimization and Co-design Framework for Sparse Computations'', K. Kaya
  • 2020-2022 (H2020-JTI-EuroHPC): ''EuroCC: "National Competence Centers in the Framework of EuroHPC", A. Demirelli, Ö. Taştan, O. Varol, H. Yenigün, B. Yanıkoğlu, K. Kaya

Faculty Members


Performance of a new parallel analysis method developed for finding important points and important people in social networks on a single Xeon Phi accelerator. GTEPS: number of relationships/friends analyzed per second (in billions).

Kamer Kaya

FENS Faculty Member

Research Areas

Parallel Algorithms; high performance computing; BigData analysis