Back to Results
First PageMeta Content
Parallel computing / Data mining / Geostatistics / Medoid / K-medoids / K-means clustering / Hierarchical clustering / Computer cluster / Message Passing Interface / Statistics / Computing / Cluster analysis


Clustering Performance Data Efficiently at Massive Scales ∗ Todd Gamblin∗ , Bronis R. de Supinski∗ , Martin Schulz∗ , Rob Fowler† , and Daniel A. Reed‡
Add to Reading List

Document Date: 2010-04-20 19:38:23

Open Document

File Size: 680,07 KB

Share Result on Facebook

City

Seattle / Masstricht / Austin / Palo Alto / New York / Redmond / Madison / Washington / DC / Philadelphia / Ibaraki / Livermore / Miami / /

Company

IBM / Argonne National Laboratory / HP Laboratories / Reno NV / BOC / Lawrence Livermore National Laboratory / Addison-Wesley Longman Publishing Co. / Bell Laboratories / PAM / Microsoft / /

Continent

Europe / /

Country

Netherlands / Japan / United States / /

Currency

pence / USD / /

/

Event

Reorganization / /

Facility

Argonne Leadership Computing Facility / Analysis Consortium End Station / University of Illinois / USA Renaissance Computing Institute / M. Hall / University of North Carolina / Chapel Hill / /

IndustryTerm

extreme-scale systems / Parallel algorithms / on-line analysis / torus network / large-scale systems / on-line clusterings / exascale systems / much larger systems / parallel clustering algorithm / on-line clustering algorithm / performance tool / performance tools / performance-tuning tools / gas-dynamics code / similar algorithm / large systems / on-line analysis techniques / convergent algorithm / parallel applications / stack-walking tools / hypercube networks / parallel partitioning algorithm / large-scale parallel computing / on-line stratification / on-line use / parallel performance tools / on-line performance traces / compression algorithms / stratified sampling tool / large-scale torus network / software study / image-analysis applications / performance data mining framework / naive solution / larger systems / /

Organization

U.S. Department of Energy / U.S. Government / U.S. DOE / Society for Industrial and Applied Mathematics / USA Renaissance Computing Institute / office of Science / Association for Computing Machinery / University of Illinois / University of North Carolina / Chapel Hill / IEEE Computer Society / /

Person

Daniel A. Reed / Martin Schulz / D. A. Reed / V / Rob Fowler / Todd Gamblin / /

Position

editor / General / DynStackwalker Programmer / contractor / Effort Model of Gamblin / representative / /

Product

Pentax K-x Digital Camera / BlueGene/P / /

ProgrammingLanguage

C / K / DC / AMPL / C++ / /

ProvinceOrState

Texas / Wisconsin / Illinois / New York / Washington / Pennsylvania / Manitoba / Florida / North Carolina / California / /

PublishedMedium

Machine Learning / Mathematics of Computation / IEEE Transactions on Information Theory / /

Technology

Symmetric Multiprocessing / RAM / 3-D / 2.2 2.3 Partitioning Algorithms / compression algorithms / X-Means algorithm / 128 processors / clustering algorithm / parallel partitioning algorithm / convergent algorithm / classic CLARA algorithm / Machine Learning / CLUSTERING TECHNIQUES Clustering algorithms / parallel clustering algorithm / pdf / sampling algorithm / Hierarchical clustering algorithms / sampled K-Medoids algorithm / load balancing / ASCII / Multidimensional Clustering Algorithms / K-Means clustering algorithm / similar algorithm / sequential K-Means algorithm / on-line clustering algorithm / Data Mining / simulation / 32 processors / 48 processors / X-Means partitioning algorithm / /

URL

http /

SocialTag