Professor of Scientific Computing - Mathematical Institute
As well as being an Associate Director of OeRC, Mike Giles is Professor of Scientific Computing in the Mathematical Institute, and a member of the Oxford-Man Institute of Quantitative Finance.
He read mathematics at Cambridge before completing a PhD in Aeronautical Engineering at the Massachusetts Institute of Technology (MIT). He was an Associate Professor at MIT before moving to Oxford in 1992 to join the Computing Laboratory. After working closely with Rolls-Royce for many years developing computational fluid dynamics techniques, he moved into the development of Monte Carlo and finite difference methods in computational finance, which led to his transfer to the Mathematical Institute in 2008. In 2007 he was named ‘Quant of the Year’ by Risk magazine, together with Paul Glasserman of Columbia Business School, for their joint work on the use of adjoints for the efficient calculation of Monte Carlo sensitivities.
He has always been interested in high performance computing, working within the Oxford Parallel centre in 1992-96, and setting up the Oxford Supercomputing Centre in 1997. In the last few years, he has been one of the leaders within the UK in exploiting the computational power of GPUs (graphics processors) and has been named an NVIDIA CUDA Fellow in Computational Finance in recognition of his work in this area within OeRC and the Oxford-Man Institute of Quantitative Finance. In 2012 he led the design of the Emerald GPU supercomputer, and the establishment of the Oxford CUDA Centre of Excellence.
To learn more about his research on Monte Carlo methods within the Mathematical Institute, please see his homepage.
GPU Computing publications
- , M.B. Giles, A. Doucet, C.C. Holmes. 'On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods'. Journal of Computational and Graphical Statistics, 19(4): 769-789, 2010. (PDF)A. Lee, C. Yau
- T. Bradley, J. du Toit, M.B. Giles, R. Tong, P. Woodhams. 'Parallelisation techniques for random number generators'. In GPU Computing Gems, volume 1, Morgan Kaufmann, 2011. (PDF)
- M.B. Giles. 'Approximating the erfinv function'. In GPU Computing Gems, volume 2, Morgan Kaufmann, 2011. (PDF)
- G. Klingbeil, R. Erban, M. Giles, P.K. Maini. 'Fat vs. thin threading approach on GPUs: application to stochastic simulation of chemical reactions'. IEEE Transactions on Parallel and Distributed Systems, May 2011. (PDF)
- G. Klingbeil, R. Erban, M. Giles, P.K. Maini. 'STOCHSIMGPU: Parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB'. Bioinformatics, 27(8):1170-71, 2011. (PDF)
- M.B. Giles, G.R. Mudalige, Z. Sharif, G. Markall, P.H.J. Kelly. 'Performance Analysis of the OP2 Framework on Many-core Architectures', ACM SIGMETRICS Performance Evaluation Review, 38(4):9-15, 2011 (PDF)
- C. Bertolli, A. Betts, G.R. Mudalige, M.B. Giles, P.H.J. Kelly. `Design and performance of the OP2 library for unstructured mesh applications', Euro-Par 2001 Parallel Processing Workshops, Lecture Notes in Computer Science, Springer, 2011. (PDF)
- G.R. Mudalige, M.B. Giles, C. Bertolli, P.H.J. Kelly. 'Predictive modeling and analysis of OP2 on distributed memory GPU clusters'. PMBS '11 Proceedings of the second international workshop on Performance modeling, benchmarking and simulation of high performance computing systems, 2011, ACM SIGMETRICS Performance Evaluation Review, 40(2):61-67, 2012 (PDF)
- M.B. Giles, G.R. Mudalige, Z. Sharif, G. Markall, P.H.J. Kelly, 'Performance analysis and optimisation of the OP2 framework on many-core architectures', Computer Journal, 55(2):168-180, 2012 (PDF)
- G. Klingbeil, R. Erban, M. Giles, P.K. Maini. 'Fat vs. thin threading approach on GPUs: application to stochastic simulation of chemical reactions'. IEEE Transactions on Parallel and Distributed Systems, 23(2):280-287, 2012.(PDF)
- G.R. Mudalige, M.B. Giles, B. Spencer, C. Bertolli, I. Reguly. 'Designing OP2 for GPU architectures', to appear inJournal of Parallel and Distributed Computing, 2012. (PDF)