S. Lewis

Implementing data parallelisation in a Nested-Sampling Monte Carlo algorithm

In this paper we report our work on the parallelisation of a Nested Sampling Monte Carlo algorithm used in the nuclear physics field of hadron spectroscopy. The purpose of the application is to fit a set of parameters in a nuclear physics model based on the observations of the beam properties. We used both OpenCL and OpenMP to parallelise the existing code. Our aims were to achieve parallelisation with minimal changes to the original source code and to evaluate the performance of the parallel code on both a GPU and a multicore CPU. On the implementation side, we show that by using our OclWrapper abstraction over the OpenCL API, integration of OpenCL code into and existing C++ code base is much simplified, to the extent that integrating OpenCL is not considerably more effort than using OpenMP, as the main effort is in making the code suitable for parallel execution. Our evaluation shows that the best results depend strongly on the size of dataset. For large numbers of events (105), we achieved a best speed-up of 22 times using OpenCL on the CPU. For small numbers of events (103), we achieved a best speed-up of 4 times using OpenMP on the CPU. The best GPU speed-up was 7 times for 105 events. This is mainly a result of the longer data transfer time, which negates the improvement in computation time.

Development of Bayesian analysis program for extraction of polarisation observables at CLAS

At the mass scale of a proton, the strong force is not well understood. Various quark models exist, but it is important to determine which quark model(s) are most accurate. Experimentally, finding resonances predicted by some models and not others would give valuable insight into this fundamental interaction. Several labs around the world use photoproduction experiments to find these missing resonances. The aim of this work is to develop a robust Bayesian data analysis program for extracting polarisation observables from pseudoscalar meson photoproduction experiments using CLAS at Jefferson Lab. This method, known as nested sampling, has been compared to traditional methods and has incorporated data parallelisation and GPU programming. It involves an event-by-event likelihood function, which has no associated loss of information from histogram binning, and results can be easily constrained to the physical region. One of the most important advantages of the nested sampling approach is that data from different experiments can be combined and analysed simultaneously. Results on both simulated and previously analysed experimental data for the K+Λ channel will be discussed.