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» Clustering performance data efficiently at massive scales
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2010
Tsinghua U.
13 years 7 months ago
Clustering performance data efficiently at massive scales
Existing supercomputers have hundreds of thousands of processor cores, and future systems may have hundreds of millions. Developers need detailed performance measurements to tune ...
Todd Gamblin, Bronis R. de Supinski, Martin Schulz...
KDD
2006
ACM
156views Data Mining» more  KDD 2006»
14 years 4 months ago
Discovering significant OPSM subspace clusters in massive gene expression data
Order-preserving submatrixes (OPSMs) have been accepted as a biologically meaningful subspace cluster model, capturing the general tendency of gene expressions across a subset of ...
Byron J. Gao, Obi L. Griffith, Martin Ester, Steve...
PVLDB
2010
150views more  PVLDB 2010»
13 years 2 months ago
DataGarage: Warehousing Massive Performance Data on Commodity Servers
Contemporary datacenters house tens of thousands of servers. The servers are closely monitored for operating conditions and utilizations by collecting their performance data (e.g....
Charles Loboz, Slawek Smyl, Suman Nath
PVLDB
2008
182views more  PVLDB 2008»
13 years 3 months ago
SCOPE: easy and efficient parallel processing of massive data sets
Companies providing cloud-scale services have an increasing need to store and analyze massive data sets such as search logs and click streams. For cost and performance reasons, pr...
Ronnie Chaiken, Bob Jenkins, Per-Åke Larson,...
ICPP
2000
IEEE
13 years 8 months ago
A Scalable Parallel Subspace Clustering Algorithm for Massive Data Sets
Clustering is a data mining problem which finds dense regions in a sparse multi-dimensional data set. The attribute values and ranges of these regions characterize the clusters. ...
Harsha S. Nagesh, Sanjay Goil, Alok N. Choudhary