Efficient computation of sum-products on GPUs through software-managed cache
Proceedings of the 22nd annual international conference on Supercomputing
Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach
Computational Statistics & Data Analysis
GPU accelerated smith-waterman
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Hi-index | 0.03 |
The use of graphical processing unit (GPU) parallel processing is becoming a part of mainstream statistical practice. The reliance of Bayesian statistics on Markov Chain Monte Carlo (MCMC) methods makes the applicability of parallel processing not immediately obvious. It is illustrated that there are substantial gains in improved computational time for MCMC and other methods of evaluation by computing the likelihood using GPU parallel processing. Examples use data from the Global Terrorism Database to model terrorist activity in Colombia from 2000 through 2010 and a likelihood based on the explicit convolution of two negative-binomial processes. Results show decreases in computational time by a factor of over 200. Factors influencing these improvements and guidelines for programming parallel implementations of the likelihood are discussed.