School of Computing and Communications
My research focus is in the areas of parallel compilers, runtime systems and the application of machine learning to tackle the challenging optimisation problems within these areas. My research interests include:
- Compiler-based parallelism mapping: how to map a parallel program to the underlying hardware to achieve the best (energy-efficient) performance.
- Code generation and optimisation for heterogeneous many-cores: I am interested in how can compilers generate efficient code for the emerging heterogeneous many-core systems, such as a CPU-GPGPU system.
- Auto-parallelising compilers: I am currently investigating the use of dynamic analysis together with machine learning to develop a new approach that gives scalable performance for many-cores.
- Runtime scheduling: how to schedule multiple concurrently running tasks in a multi-tasking environment to maximise the system-level performance (e.g. throughput or energy-efficiency).
- Research into energy efficient computing through system software including just-in-time compilers and operating systems.
- The Royal Society International Collaboration Grant: Software Bug Detection and Fix Generation by Learning from Large Code Examples, PI, 03/2017 - 03/2019
- EPSRC iCASE Studentship with ARM Ltd: Energy and Performance Optimisation for Mobile Systems, PI, 02/2016 - 08/2019
- EPSRC SANDeRs: Smart, Adaptive Compilation for Dark Silicon (EP/M01567X/1), PI, 06/2015 - 05/2017
- CHIST-ERA (EPSRC), DIVIDEND: Distributed Heterogeneous Vertically IntegrateD ENergy Efficient Data centres (EP/M015793/1), PI, 01/2015 - 12/2016
- EPSRC ALEA: Abstraction-Level Energy Accounting for Many-core Programming Languages (EP/L000555/1), CoI, 12/2013 - 12/2016
- Lancaster FST Small Grant, PI
- Lancaster University Early Career Small Grant, PI