Open AccessJournal Article
How Are We Doing? A Self-Assessment of the Quality of Services and Systems at NERSC, 2005-2006
William Kramer,John Hules +1 more
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This is the sixth self-assessment of the systems and services provided by the U.S. Department of Energy's National Energy Research Scientific Computing Center, describing many of the efforts of the NERSC staff to support advanced computing for scientific discovery.Abstract:
This is the sixth self-assessment of the systems and services provided by the U.S. Department of Energy's National Energy Research Scientific Computing Center, describing many of the efforts of the NERSC staff to support advanced computing for scientific discovery. The report is organized along the 10 goals set by our staff and outlines how we are working to meet those goals. Our staff applies experience and expertise to provide world-class systems and unparalleled services for NERSC users. At the same time, members of our organization are leading contributors to advancing the field of high-performance computing through conference presentations, published papers, collaborations with scientific researchers and through regular meetings with members of similar institutions. In the fast-moving realm of high-performance computing, adopting the latest technology while reliably delivering critical resources can be a challenge, but we believe that this self-assessment demonstrates that NERSC continues to excel on both counts.read more
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Detecting Distributed Scans Using High-Performance Query-DrivenVisualization
TL;DR: In this paper, a set of parallel algorithms that demonstrate how an efficient selection mechanism, bitmap indexing, significantly speeds up a common analysist ask, namely, computing conditional histogram on very large datasets.
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Proceedings ArticleDOI
A min-max cut algorithm for graph partitioning and data clustering
TL;DR: This paper proposes a new algorithm for graph partitioning with an objective function that follows the min-max clustering principle, and demonstrates that a linearized search order based on linkage differential is better than that based on the Fiedler vector, providing another effective partitioning method.
Journal ArticleDOI
Multi-class protein fold recognition using support vector machines and neural networks.
Chris Ding,Inna Dubchak +1 more
TL;DR: This work investigated two new methods for protein fold prediction using the Support Vector Machine and the Neural Network learning methods as base classifiers, and examined many issues involved with large number of classes, including dependencies of prediction accuracy on the number of folds and on thenumber of representatives in a fold.
Proceedings Article
Spectral Relaxation for K-means Clustering
TL;DR: It is shown that a relaxed version of the trace maximization problem possesses global optimal solutions which can be obtained by Computing a partial eigendecomposition of the Gram matrix, and the cluster assignment for each data vectors can be found by computing a pivoted QR decomposition ofThe eigenvector matrix.
Book
High Performance Computing
TL;DR: This new edition of High Performance Computing gives a thorough overview of the latest workstation and PC architectures and the trends that will in?uence the next generation.
Journal ArticleDOI
Cosmology from MAXIMA-1, BOOMERANG, and COBE DMR cosmic microwave background observations.
Andrew H. Jaffe,Peter A. R. Ade,Amedeo Balbi,J. J. Bock,J. R. Bond,Julian Borrill,A. Boscaleri,K. Coble,B. P. Crill,P. de Bernardis,P. C. Farese,Pedro G. Ferreira,K. Ganga,M. Giacometti,Shaul Hanany,E. Hivon,Viktor Hristov,A. Iacoangeli,Andrew E. Lange,Adrian T. Lee,L. Martinis,Silvia Masi,Philip Daniel Mauskopf,Alessandro Melchiorri,T. E. Montroy,Calvin B. Netterfield,Sang-Yun Oh,Enzo Pascale,F. Piacentini,Dmitry Pogosyan,Simon Prunet,Bahman Rabii,S. Rao,Paul L. Richards,Giovanni Romeo,J. E. Ruhl,Francesco Scaramuzzi,D. Sforna,George F. Smoot,R. Stompor,C. D. Winant,Jiun-Huei Proty Wu +41 more
TL;DR: Results from BOOMERANG-98 and MAXIMA-1 provide consistent and high signal-to-noise measurements of the cosmic microwave background power spectrum at spherical harmonic multipole bands over 2