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.

Congratulations to Harita Dellaporta for receiving the Best Paper Award at the premier conference in Artificial Intelligence and Statistics (AISTATS) 2022 for her paper on Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap.

Members of the High-Performance and Scientific Computing Group (HPSC) at the department of Computer Science has won a best paper award at the 28th IEEE International Conference on High-Performance Computing, Data and Analytics held on the 17th-18th of December. The winning paper titled Predictive Analysis of Large-Scale Coupled CFD Simulations with the CPX Mini-App, develops a novel representative (mini-)application, specifically designed to model coupled execution of multi-physics numerical simulation codes from the CFD domain. The mini-coupler, CPX, is the first of its kind, combining multiple CFD mini-app instances to predict the run-time and scaling behaviour of large scale coupled CFD simulations, on modern multi-core and many-core clusters such as used for production turbomachinery design at Rolls-Royce plc. The work was carried out by PhD candidate, Archie Powell, in collaboration with Kabir Choudry, Arun Prabhakar, and Gihan Mudalige at the Department of CS Warwick, Dario Amirante (University of Surrey), Istvan Reguly (PPCU) and Stephen Jarvis (University of Birmingham).

The work was funded by the EPSRC Prosperity Partnership in Computational Science for Advanced Simulation and Modelling of Engineering Systems (AsiMoV) and Rolls-Royce plc.