Streaming multi-core sample-based Bayesian Analysis

The aim of this PhD is to develop strategies for implementing state-of-the-art Bayesian techniques in ways that fully exploit the computational power present in today's and tomorrow's (increasingly multi-core) computers. This will build on previous research related to high performance computing, Big Data and Bayesian statistics.
The PhD involves active engagement with Schlumberger. Schlumberger is a major supplier of a broad range of services to the oil and gas sector and, in fact, the specific applications that motivate the research are associated with the analysis of data related to oil exploration. Drilling provides access to reservoirs from which oil and gas can be extracted. Reaching such reservoirs is increasingly difficult physically and economically. The modern solutions that Schlumberger is developing in response to these difficulties involve analysing high volume streams of data from poorly sampled systems with large degrees of uncertainty. There is a need to extract information and identify events in real-time.
Complex (finite element) models exist that can predict what data will be observed were the model's parameters to take specific values and can also predict how the model's parameters change over time. This makes it possible to apply a specific state-of-the-art technique, a particle filter, to the data stream (particle filters are reminiscent of genetic algorithms but were developed by statisticians to solve streaming analytics problems). Historic research has demonstrated the utility of this approach, but real-time operation remains challenging.
Multi-core architectures are increasingly widespread (eg in desktop PCs' CPUs, GPUs, Xeon Phis and super-computing clusters). Given this, the specific tactic to be used to obtain real-time performance is to parallelise the operation of the particle filter and distribute the processing across each of a number of cores.
Particle filters use the diversity of a population of samples to convey uncertainty. For the majority of the operation of the particle filter, each particle is processed independently. This makes it trivial to parallelise the majority of the particle filter. However, at a specific point in the filter, it becomes necessary to perform a "resampling" step. A text-book implementation of this resampling step is impossible to parallelise. However, previous research has demonstrated that it is possible to describe the resampling operation using a divide-and-conquer strategy. In so doing, it becomes possible to parallelise the resampling step. More recent work has identified that, if using more cores if to result in faster operation, it is crucial that data locality and pipelining are explicitly considered in the implementation. There is a need for this research to be extended significantly and the potential advantages motivate this PhD.