Nick is a data architect at EPCC and works on a variety of projects including leading a collaboration (PI) with RSI concerned with machine learning for optimising petrophysical workflows, and leads a work package on the VESTEC European FET project which aims to fuse HPC and real-time data for urgent decision making. I have previously worked on numerous collaborations, including working with the Met Office developing MONC, a next generation atmospheric model used by UK climate and weather scientists which enables the study of clouds and fog at scales before unobtainable (billion of grid points over 32,000+ CPU cores). As part of MONC we have researched numerous innovative aspects of HPC, such as in-situ data analytics to transform the terabytes of raw computational data into smaller amounts of higher level information on the fly. I am also interested in the acceleration of computationally intensive aspects of this model, previously investigating the use of GPGPUs and Xeon Phis, currently my focus has shifted a bit to the the role that FPGAs can play here. I have also worked with the British Geological Survey, modernising their MEME geomagnetic model to take advance of modern HPC machines to enable the study of the earth's geomagnetic field at resolutions far greater than previously obtainable.
AI/Machine Learning/Deep Learning
Performance Analysis and Optimization