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HPC Research Accepted for Publication at the ICS 2022 Conference

Two papers by researchers at the Department of Computer Science have been accepted to the 36th ACM International Conference on Supercomputing ICS 2022 to be held on the 28-30th of June this year. ICS is one of the most prominent and revered conferences in High Performance Computing, highly regarded by the HPC community for publishing leading-edge research in this area. The two papers accepted are:

  1. High Throughput Multidimensional Tridiagonal System Solvers on FPGAs (Preprint) by Kamalavasan Kamalakkannan and Gihan Mudalige at Warwick, together with Istvan Reguly (PPCU) and Suhaib Fahmy (KAUST).
  2. Clairvoyant: A Log-Based Transformer-Decoder for Failure Prediction in Large-Scale Systems by Khalid Alharthi and Arshad Jhumka at Warwick, together with Sheng Di, Franck Cappello at Argonne National Laboratory. Preprint. The ACM ICS2022 full program can be found here.
Fri 27 May 2022, 08:58 | Tags: Research Data Science Systems and Security

Warwick Computer Science ranked 4th in Research Excellence Framework 2021

The Research Excellence Framework (REF) is the UK’s system for assessing the quality of research in the country's higher education institutions.

The results of the 2021 REF rank Warwick Computer Science 4th out of 90 UK computing departments. This cements our position as one of the top Computer Science departments in the UK, a position we have held for some time under different assessment methodologies.

Research Excellence at Warwick

Fri 13 May 2022, 01:48 | Tags: Highlight Research

Winner of the Faculty of Science, Engineering and Medicine Post-Doctoral Research Prize 2022

Gunduz Vehbi Demirci has been awarded with the Faculty of Science, Engineering and Medicine Post-Doctoral Research Prize 2022 for his paper jointly with Prof. Hakan Ferhatosmanoglu, "Partitioning sparse deep neural networks for scalable training and inference", published in the Proceedings of the ACM International Conference on Supercomputing (ICS '21) (DOI: https://doi.org/10.1145/3447818.3460372).

Training large-scale deep learning models is notoriously difficult. Gunduz develops a highly parallel solution to scale training of sparse deep learning models, which is combined with a novel combinatorial optimisation built on a hypergraph partitioning model, reducing parallelisation overheads and achieving computational balance among processors. An end-to-end software solution is released, enabling competing with big tech companies that have access to large infrastructures and datasets.

The work is summarised in a paper accepted by the 2021 ACM International Conference on Supercomputing, which is a premier conference in high-end systems. The research output will have a great potential to bring significant practical impact in long term as developing such comprehensive solutions takes time and is typically achieved only within large groups.

Mon 09 May 2022, 09:08 | Tags: Research Data Science Systems and Security

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