GPGPU : Metropolis hastings sampling on a cluster of GPUs

under guidance of Prof. Sharat Chandran

Recent advances in the speed and programmability of graphics hardware have made it a compelling platform for computationally demanding tasks in a wide variety of application domains. Many of the Bayesian machine learning algorithms rely on quantities estimated over samples and quite often sampling is the single most computationally demanding task in such learning methods. Fortunately, sampling techniques are fairly general and are easily extended to new models. Sampling methods like Gibbs and Metropolis-Hastings are easily parallelizable which make the sampling a perfect application for distributed computing. Furthermore, their numerical simplicity makes them an ideal candidate for implementation on GPUs. This project is about mapping Metropolis Hastings sampling algorithm to graphics hardware.

Project Report

Project Presentation

Seminar on GPGPU


Prekshu Ajmera, IIT Bombay