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Reconfigurable Architectures for Scientific Computing

   Department of Computer Science

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  Dr Steven Wright  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Supercomputers play an important role in the field of computational science, and are used for a wide range of computationally intensive tasks in various fields across the engineering and sciences spectrum [1]. As the computational power of these systems continues to grow, there is typically a hard constraint on the maximum power that can be drawn by a system. It is therefore vitally important that new systems are procured with energy-efficiency in mind, and that applications are developed to achieve the maximum performance while making every effort to reduce their power requirements [2-4].

Although the theoretical peak performance of the fastest machines is rapidly approaching the era of Exascale computation, real applications are typically realising only a fraction of this – representing a significant waste of resources. One potential solution to this is the use of reconfigurable architectures — architectures that can be precisely configured at runtime to contain only the computational units required, in a configuration which is more tailored to the needs of the target application [5,6]. 

This research project seeks to investigate the applicability of reconfigurable architectures (e.g. field-programmable gate arrays [FPGAs]) to the computational sciences.

Specifically, the objectives of this project will be to (i) evaluate the programmability of FPGAs for applications with a heavy reliance on floating-point calculations; (ii) port a small number of proxy applications from the physics domain to FPGAs; and finally, (iii) analyse the performance of these applications in terms of their runtime and energy performance, in order to quantify the trade-offs that may be available.

Familiarity with parallel programming and programming FPGAs is desired.

Research areas: High-Performance Computing, Embedded Computing, FPGA, Energy-aware computing

For more details please contact Dr. Steven Wright

E-mail: [Email Address Removed]


[1] Wright, S.A. (2019). Performance Modeling, Benchmarking and Simulation of High Performance Computing Systems. Future Generation Computer Systems, 92, 900-902.
[2] Roberts, S.I., Wright, S.A., Fahmy, S.A., Jarvis, S.A. (2017) Metrics for Energy-Aware Software Optimisation. High Performance Computing. ISC 2017. Lecture Notes in Computer Science, 10266.
[3] Roberts, S.I., Wright, S.A., Fahmy, S.A., Jarvis, S.A. (2019) The Power-optimised Software Envelope. ACM Transactions on Architecture and Code Optimization, 16, 3, Article 21 (June 2019), 27 pages.
[4] Hackenberg, D., Ilsche T., Schuchart, J., Schöne, R., Nagel, W.E., Simon, M., Georgiou, Y. (2014) HDEEM: High Definition Energy Efficiency Monitoring. In Energy Efficient Supercomputing Workshop (E2SC). 1–10.
[5] Gray, I., Chan, Y., Audsley, N.C., Wellings A. (2014) Architecture-Awareness for Real-Time Big Data Systems. Proceedings of the 21st European MPI Users’ Group Meeting (EuroMPI/ASIA’14). 151–156.
[6] Nguyen, T., Williams, S., Siracusa, M., MacLean, C., Doerfler, D., Wright, N. J., (2020) The Performance and Energy Efficiency Potential of FPGAs in Scientific Computing. 2020 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS), 8-19.

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