FPT Approximation Algorithm for Scheduling with Memory Constraints

2016 ◽  
pp. 196-208
Author(s):  
Eric Angel ◽  
Cédric Chevalier ◽  
Franck Ledoux ◽  
Sébastien Morais ◽  
Damien Regnault
Keyword(s):  
Approximation Algorithm ◽  
With Memory ◽  
Memory Constraints

scholarly journals Proximal primal–dual best approximation algorithm with memory

2018 ◽  
Vol 71 (3) ◽  
pp. 767-794
Author(s):  
E. M. Bednarczuk ◽  
A. Jezierska ◽  
K. E. Rutkowski
Keyword(s):  
Approximation Algorithm ◽  
Best Approximation ◽  
Primal Dual ◽  
With Memory

On the Effectiveness of Hybrid Canopy with Hoeffding Adaptive Naive Bayes Trees

2017 ◽  
Vol 8 (2) ◽  
pp. 30-43
Author(s):  
Mrutyunjaya Panda
Keyword(s):  
Big Data ◽  
Clustering Analysis ◽  
Large Scale ◽  
Data Sets ◽  
Recent Past ◽  
Large Scale Data ◽  
Huge Data ◽  
With Memory ◽  
Memory Constraints ◽  
Scale Data

The Big Data, due to its complicated and diverse nature, poses a lot of challenges for extracting meaningful observations. This sought smart and efficient algorithms that can deal with computational complexity along with memory constraints out of their iterative behavior. This issue may be solved by using parallel computing techniques, where a single machine or a multiple machine can perform the work simultaneously, dividing the problem into sub problems and assigning some private memory to each sub problems. Clustering analysis are found to be useful in handling such a huge data in the recent past. Even though, there are many investigations in Big data analysis are on, still, to solve this issue, Canopy and K-Means++ clustering are used for processing the large-scale data in shorter amount of time with no memory constraints. In order to find the suitability of the approach, several data sets are considered ranging from small to very large ones having diverse filed of applications. The experimental results opine that the proposed approach is fast and accurate.

scholarly journals Data Partitioning for Multiprocessors with Memory Heterogeneity and Memory Constraints

2005 ◽  
Vol 13 (2) ◽  
pp. 93-112 ◽  
Author(s):  
Alexey Lastovetsky ◽  
Ravi Reddy
Keyword(s):  
Efficient Solution ◽  
Data Partitioning ◽  
Performance Model ◽  
Memory Structure ◽  
Problem Size ◽  
Heterogeneous Processors ◽  
Optimal Partitioning ◽  
With Memory ◽  
Memory Constraints ◽  
Element Set

The paper presents a performance model that can be used to optimally distribute computations over heterogeneous computers. This model is application-centric representing the speed of each computer by a function of the problem size. This way it takes into account the processor heterogeneity, the heterogeneity of memory structure, and the memory limitations at each level of memory hierarchy. A problem of optimal partitioning of ann-element set overpheterogeneous processors using this performance model is formulated, and its efficient solution of the complexity O(p3× log2n) is given.

scholarly journals Experimental study of scheduling with memory constraints using hybrid methods

2009 ◽  
Vol 232 (2) ◽  
pp. 638-654 ◽  
Author(s):  
J. Berlińska ◽  
M. Drozdowski ◽  
M. Lawenda
Keyword(s):  
Experimental Study ◽  
Hybrid Methods ◽  
With Memory ◽  
Memory Constraints

scholarly journals Non-probabilistic decision making with memory constraints

2012 ◽  
Vol 117 (1) ◽  
pp. 303-305
Author(s):  
Alexander Vostroknutov
Keyword(s):  
Decision Making ◽  
With Memory ◽  
Memory Constraints

Dealing with Memory Constraints in a Non-Dedicated Linux Cluster

2003 ◽  
Vol 17 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Francesc Giné ◽  
Francesc Solsona ◽  
Porfidio Hernández ◽  
Emilio Luque
Keyword(s):  
Linux Cluster ◽  
With Memory ◽  
Memory Constraints

scholarly journals Toward Bounds on Parallel Execution Times of Task Graphs on Multicores With Memory Constraints

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 52778-52789 ◽  
Author(s):  
Jiangong Song ◽  
Qinyong Li ◽  
Shilong Ma
Keyword(s):  
Parallel Execution ◽  
Task Graphs ◽  
Execution Times ◽  
With Memory ◽  
Memory Constraints
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