Parallel algorithms for singular value decomposition (SVD) have risen to prominence as an indispensable tool in high-performance numerical linear algebra.

Parallel tempering is a generic Markov chain Monte Carlo sampling method which allows good mixing with multimodal target distributions, where conventional Metropolis-Hastings algorithms often fail.

The team designed a fully dynamic APSP algorithm in the MPC model with low round complexity that is faster than all the existing static parallel APSP algorithms.

It is well known that traditional Markov chain Monte Carlo (MCMC) methods can fail to effectively explore the state space for multimodal problems. Parallel tempering is a well-established population ...

Students taking the course for COMP_SCI 368 credit will work on labs that utilize advanced parallel programming, data layout, and algorithm decomposition concepts. Students taking the course for ...