Description
Partial wave analysis is a fundamental technique for extracting hadron spectra and hadron decay properties. It is widely employed in current experiments in particle physics, particularly at BES-III.
The analysis is typically performed using the event-by-event maximum likelihood method. For the BES-III experiment, fitting the accumulated data (about 1.225 billion J/psi decays) using currently employed software takes a long time, which significantly complicates and sometimes restricts data analysis. Fortunately, computing the likelihood function can be naturally parallelized. Thus the development of new multicore CPU's and GPU's makes using parallel programming technologies natural to decrease the data fitting time.
The talk will be about the development of highly scalable parallel framework for the partial wave analysis. The framework is intended to accelerate the calculations on modern multicore CPUs and CPU-like coprocessors by employing OpenMP parallel computing technology, high-performance computing optimizations like vectorization or aligned memory access, and offloading computationally intensive parts of the code to massively parallel co-processors.
Current results will be presented.
