We propose Markov chain Monte Carlo sampling methods to address uncertainty estimation in disparity computation. We consider this problem at a postprocessing stage, i.e. once the d...
Program dynamic optimization, adaptive to runtime behavior changes, has become increasingly important for both performance and energy savings. However, most runtime optimizations o...
Abstract. Understanding and controlling program behavior is a challenging objective for the design of advanced compilers and critical system development. In this paper, we propose ...
We consider a parallel decomposition technique for solving the large quadratic programs arising in training the learning methodology Support Vector Machine. At each iteration of th...
In this paper we present the results of parallelizing two life sciences applications, Markov random fieldsbased (MRF) liver segmentation and HMMER’s Viterbi algorithm, using GP...
John Paul Walters, Vidyananth Balu, Suryaprakash K...