Gaussian Mixture Models (GMMs) are widely used among scientists e.g. in statistics toolkits and data mining procedures. In order to estimate parameters of a GMM the Maximum Likelihood (ML) training is often utilized, more precisely the Expectation-Maximization (EM) algorithm. Nowadays, a lot of tasks works with huge datasets, what makes the estimation process time consuming […]

October 2, 2014 by Vanekyj

The graphics processing unit (GPU) has emerged as a powerful and cost effective processor for high performance computing. GPUs are capable of an order of magnitude more floating point operations per second as compared to modern central processing units (CPUs), and thus provide a great deal of promise for computationally intensive statistical applications (Brodtkorb et […]

November 26, 2011 by hgpu

Partial wave analysis is a core tool in hadron spectroscopy. With the high statistics data available at facilities such as the Beijing Spectrometer III, this procedure becomes computationally very expensive. We have successfully implemented a framework for performing partial wave analysis on graphics processors. We discuss the implementation, the parallel computing frameworks employed and the […]

August 31, 2011 by hgpu

Partial wave analysis is a key technique in hadron spectroscopy. The use of unbinned likelihood fits on large statistics data samples and ever more complex physics models makes this analysis technique computationally very expensive. Parallel computing techniques, in particular the use of graphics processing units, are a powerful means to speed up analyses; in the […]

August 31, 2011 by hgpu

Scientific data sets continue to increase in both size and complexity. In the past, dedicated graphics systems at supercomputing centers were required to visualize large data sets, but as the price of commodity graphics hardware has dropped and its capability has increased, it is now possible, in principle, to view large complex data sets on […]

November 9, 2010 by hgpu