Exhaustive Community Enumeration in Parallel

2016 ◽  
Vol 26 (02) ◽  
pp. 1650006 ◽  
Author(s):  
Christian Trefftz ◽  
Hugh McGuire ◽  
Zachary Kurmas ◽  
Jerry Scripps
Keyword(s):  
Shared Memory ◽  
Graphical Processing Unit ◽  
Processing Unit ◽  
Well Performance ◽  
Cluster Of Workstations ◽  
Graphical Processing ◽  
Performance Results

An algorithm to evaluate/count all the possible communities of a graph is presented. An associated unrank function is described. An implementation of an existing algorithm to evaluate all the possible partitions of a graph, based on an unrank function, is presented as well. Performance results of the parallelizations of these algorithms obtained on a shared memory machine, a cluster of workstations and a Graphical Processing Unit (GPU) are included.

Mixed-mode database miner classifier: Parallel computation of graphical processing unit mining

2021 ◽  
pp. 002072092098849
Author(s):  
Soumya Ranjan Nayak ◽  
S Sivakumar ◽  
Akash Kumar Bhoi ◽  
Gyoo-Soo Chae ◽  
Pradeep Kumar Mallick
Keyword(s):  
Credit Card ◽  
Mixed Mode ◽  
Processing Time ◽  
Gpu Computing ◽  
Graphical Processing Unit ◽  
Computational Time ◽  
Processing Unit ◽  
Large Set ◽  
Minimal Processing ◽  
Graphical Processing

Graphical processing unit (GPU) has gained more popularity among researchers in the field of decision making and knowledge discovery systems. However, most of the earlier studies have GPU memory utilization, computational time, and accuracy limitations. The main contribution of this paper is to present a novel algorithm called the Mixed Mode Database Miner (MMDBM) classifier by implementing multithreading concepts on a large number of attributes. The proposed method use the quick sort algorithm in GPU parallel computing to overcome the state of the art limitations. This method applies the dynamic rule generation approach for constructing the decision tree based on the predicted rules. Moreover, the implementation results are compared with both SLIQ and MMDBM using Java and GPU with the computed acceleration ratio time using the BP dataset. The primary objective of this work is to improve the performance with less processing time. The results are also analyzed using various threads in GPU mining using eight different datasets of UCI Machine learning repository. The proposed MMDBM algorithm have been validated on these chosen eight different dataset with accuracy of 91.3% in diabetes, 89.1% in breast cancer, 96.6% in iris, 89.9% in labor, 95.4% in vote, 89.5% in credit card, 78.7% in supermarket and 78.7% in BP, and simultaneously, it also takes less computational time for given datasets. The outcome of this work will be beneficial for the research community to develop more effective multi thread based GPU solution in GPU mining to handle large set of data in minimal processing time. Therefore, this can be considered a more reliable and precise method for GPU computing.

Interactive, graphical processing unit-based evaluation of evacuation scenarios at the state scale

SIMULATION ◽  
2011 ◽  
Vol 88 (6) ◽  
pp. 746-761 ◽  
Author(s):  
Kalyan S Perumalla ◽  
Brandon G Aaby ◽  
Srikanth B Yoginath ◽  
Sudip K Seal
Keyword(s):  
The State ◽  
Graphical Processing Unit ◽  
Processing Unit ◽  
Graphical Processing ◽  
State Scale

scholarly journals Long Timestep Molecular Dynamics on the Graphical Processing Unit

2013 ◽  
Vol 9 (8) ◽  
pp. 3267-3281 ◽  
Author(s):  
James C. Sweet ◽  
Ronald J. Nowling ◽  
Trevor Cickovski ◽  
Christopher R. Sweet ◽  
Vijay S. Pande ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Graphical Processing Unit ◽  
Processing Unit ◽  
Graphical Processing
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