David Beckingsale is a post doc in the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. His research interests include performance analysis, performance modelling, code optimization, mini-applications and parallel programming models. He is currently engaged in advanced research and development in the area of performance analysis and modeling of massively parallel simulations, and developing tools and techniques based on machine learning to better understand and predict the performance of large-scale, parallel, data-dependent, and adaptive applications. These techniques ares being used to build lightweight machine learning-based models for dynamic performance tuning of data-dependent applications.
David received his Ph.D. in Computer Science from the University of Warwick, UK in June 2015. His thesis investigated the use of mini-applications for investigating scalability and performance issues on future parallel computer systems, specifically focusing on hydrodynamics with adaptive mesh refinement. As part of his Ph.D. research, David developed CleverLeaf, a hydrodynamics mini-application with AMR using the SAMRAI toolkit developed at LLNL. David received his B.Sc. in Computer Science in 2011, also from the University of Warwick.