3 research outputs found
On the Efficient Evaluation of the Exchange Correlation Potential on Graphics Processing Unit Clusters
The predominance of Kohn-Sham density functional theory (KS-DFT) for the
theoretical treatment of large experimentally relevant systems in molecular
chemistry and materials science relies primarily on the existence of efficient
software implementations which are capable of leveraging the latest advances in
modern high performance computing (HPC). With recent trends in HPC leading
towards in increasing reliance on heterogeneous accelerator based architectures
such as graphics processing units (GPU), existing code bases must embrace these
architectural advances to maintain the high-levels of performance which have
come to be expected for these methods. In this work, we purpose a three-level
parallelism scheme for the distributed numerical integration of the
exchange-correlation (XC) potential in the Gaussian basis set discretization of
the Kohn-Sham equations on large computing clusters consisting of multiple GPUs
per compute node. In addition, we purpose and demonstrate the efficacy of the
use of batched kernels, including batched level-3 BLAS operations, in achieving
high-levels of performance on the GPU. We demonstrate the performance and
scalability of the implementation of the purposed method in the NWChemEx
software package by comparing to the existing scalable CPU XC integration in
NWChem.Comment: 26 pages, 9 figure
Unsupervised speech processing with applications to query-by-example spoken term detection
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 163-173).This thesis is motivated by the challenge of searching and extracting useful information from speech data in a completely unsupervised setting. In many real world speech processing problems, obtaining annotated data is not cost and time effective. We therefore ask how much can we learn from speech data without any transcription. To address this question, in this thesis, we chose the query-by-example spoken term detection as a specific scenario to demonstrate that this task can be done in the unsupervised setting without any annotations. To build the unsupervised spoken term detection framework, we contributed three main techniques to form a complete working flow. First, we present two posteriorgram-based speech representations which enable speaker-independent, and noisy spoken term matching. The feasibility and effectiveness of both posteriorgram features are demonstrated through a set of spoken term detection experiments on different datasets. Second, we show two lower-bounding based methods for Dynamic Time Warping (DTW) based pattern matching algorithms. Both algorithms greatly outperform the conventional DTW in a single-threaded computing environment. Third, we describe the parallel implementation of the lower-bounded DTW search algorithm. Experimental results indicate that the total running time of the entire spoken detection system grows linearly with corpus size. We also present the training of large Deep Belief Networks (DBNs) on Graphical Processing Units (GPUs). The phonetic classification experiment on the TIMIT corpus showed a speed-up of 36x for pre-training and 45x for back-propagation for a two-layer DBN trained on the GPU platform compared to the CPU platform.by Yaodong Zhang.Ph.D