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.