Center for Computing Research (CCR)
Center for Computing Research (CCR)

Publications

Results 7251–7300 of 12,511

Search results

Jump to search filters

Advances in Domain Mapping of Massively Parallel Scientific Computations

Leland, Robert W.; Hendrickson, Bruce A.

One of the most important concerns in parallel computing is the proper distribution of workload across processors. For most scientific applications on massively parallel machines, the best approach to this distribution is to employ data parallelism; that is, to break the datastructures supporting a computation into pieces and then to assign those pieces to different processors. Collectively, these partitioning and assignment tasks comprise the domain mapping problem.

More Details
TYPE Other Report YEAR 2015
DOIOSTI

Intro - High Performance Computing for 2015 HPC Annual Report

Klitsner, Tom

The recent Executive Order creating the National Strategic Computing Initiative (NSCI) recognizes the value of high performance computing for economic competitiveness and scientific discovery and commits to accelerate delivery of exascale computing. The HPC programs at Sandia –the NNSA ASC program and Sandia’s Institutional HPC Program– are focused on ensuring that Sandia has the resources necessary to deliver computation in the national interest.

More Details
TYPE Other Report YEAR 2015
DOIOSTI

Roadmap for Peridynamic Software Implementation

Littlewood, David J.

The application of peridynamics for engineering analysis requires an efficient and robust software implementation. Key elements include processing of the discretization, the proximity search for identification of pairwise interactions, evaluation of the con- stitutive model, application of a bond-damage law, and contact modeling. Additional requirements may arise from the choice of time integration scheme, for example esti- mation of the maximum stable time step for explicit schemes, and construction of the tangent stiffness matrix for many implicit approaches. This report summaries progress to date on the software implementation of the peridynamic theory of solid mechanics. Discussion is focused on parallel implementation of the meshfree discretization scheme of Silling and Askari [33] in three dimensions, although much of the discussion applies to computational peridynamics in general.

More Details
TYPE SAND Report YEAR 2015
DOIOSTI

Time Series Discord Detection in Medical Data using a Parallel Relational Database

Woodbridge, Diane M.; Bays, Nathan R.; Wilson, Andrew T.; Goldstein, Richard

Recent advances in sensor technology have made continuous real-time health monitoring available in both hospital and non-hospital settings. Since data collected from high frequency medical sensors includes a huge amount of data, storing and processing continuous medical data is an emerging big data area. Especially detecting anomaly in real time is important for patients’ emergency detection and prevention. A time series discord indicates a subsequence that has the maximum difference to the rest of the time series subsequences, meaning that it has abnormal or unusual data trends. In this study, we implemented two versions of time series discord detection algorithms on a high performance parallel database management system (DBMS) and applied them to 240 Hz waveform data collected from 9,723 patients. The initial brute force version of the discord detection algorithm takes each possible subsequence and calculates a distance to the nearest non-self match to find the biggest discords in time series. For the heuristic version of the algorithm, a combination of an array and a trie structure was applied to order time series data for enhancing time efficiency. The study results showed efficient data loading, decoding and discord searches in a large amount of data, benefiting from the time series discord detection algorithm and the architectural characteristics of the parallel DBMS including data compression, data pipe-lining, and task scheduling.

More Details
TYPE Conference Poster YEAR 2015
DOIOSTI

Active Learning in the Era of Big Data

Jamieson, Kevin; Davis, Warren L.

Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning. Because of this, real-world testing and comparing active learning algorithms requires collecting new datasets (adaptively), rather than simply applying algorithms to benchmark datasets, as is the norm in (passive) machine learning research. To facilitate the development, testing and deployment of active learning for real applications, we have built an open-source software system for large-scale active learning research and experimentation. The system, called NEXT, provides a unique platform for realworld, reproducible active learning research. This paper details the challenges of building the system and demonstrates its capabilities with several experiments. The results show how experimentation can help expose strengths and weaknesses of active learning algorithms, in sometimes unexpected and enlightening ways.

More Details
TYPE Other Report YEAR 2015
DOIOSTI
Results 7251–7300 of 12,511
Results 7251–7300 of 12,511
Top