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20250217150049.0 |
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231224s2014 xx ||||| 00| ||eng c |
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|a DE-627
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|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Yang, Luobin
|e verfasserin
|4 aut
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| 245 |
1 |
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|a High Performance Data Clustering
|b A Comparative Analysis of Performance for GPU, RASC, MPI, and OpenMP Implementations
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| 264 |
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|c 2014
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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| 338 |
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Revised 21.10.2021
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Compared to Beowulf clusters and shared-memory machines, GPU and FPGA are emerging alternative architectures that provide massive parallelism and great computational capabilities. These architectures can be utilized to run compute-intensive algorithms to analyze ever-enlarging datasets and provide scalability. In this paper, we present four implementations of K-means data clustering algorithm for different high performance computing platforms. These four implementations include a CUDA implementation for GPUs, a Mitrion C implementation for FPGAs, an MPI implementation for Beowulf compute clusters, and an OpenMP implementation for shared-memory machines. The comparative analyses of the cost of each platform, difficulty level of programming for each platform, and the performance of each implementation are presented
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a HPC
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| 650 |
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|a K-means Clustering
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| 650 |
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|a Parallel Data Clustering
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| 650 |
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|a Reconfigurable Computing
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| 650 |
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4 |
|a Scalability
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| 700 |
1 |
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|a Chiu, Steve C
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liao, Wei-Keng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Thomas, Michael A
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t The Journal of supercomputing
|d 1998
|g 70(2014), 1 vom: 01. Okt., Seite 284-300
|w (DE-627)NLM098252410
|x 0920-8542
|7 nnas
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| 773 |
1 |
8 |
|g volume:70
|g year:2014
|g number:1
|g day:01
|g month:10
|g pages:284-300
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|a AR
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| 952 |
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|d 70
|j 2014
|e 1
|b 01
|c 10
|h 284-300
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