Mathematical Model for Approximation the Efficiency of Parallel Computing on Single Board Cluster with Least-squares Approximation

2020 ◽  
Vol 12 (7) ◽  
Author(s):  
Chotitham Thanrak ◽  
◽  
Sasalak Tongkaw ◽  
Keyword(s):  
Mathematical Model ◽  
Parallel Processing ◽  
Least Squares ◽  
High Performance ◽  
Computing System ◽  
Raspberry Pi ◽  
Processing Efficiency ◽  
High Performance Computing System ◽  
The Mathematical Model ◽  
The Relationship

This research aims to study the relationship between parallel processing efficiency and several nodes on a single board cluster using a mathematical model, approximating least squares. This research tested on the Raspberry Pi single-board in the form of a high-performance computing system. It divided the tasks that need to be processed in each particular part and sent it to each unit to process simultaneously via the MPI (Messaging Passing Interface). This process is the standard division of work with communication between processors in the form of messages on the cluster system. It consists of eight nodes of Raspberry Pi. It measures the instruction set's ability to perform decimal operations per second or Floating-point Operation Per Second (FLOPS) with High-Performance Linpack Benchmarks (HPL). As a result, the efficiency of the ability to process instruction set in decimal per second increases the performance continuously when increasing the number of the node on the cluster. Which corresponds to the mathematical model obtained f(x) = 1.0684x^(0.8256).It shows a relationship between parallel processing performance values and the number of nodes on the cluster and can be estimated with the mathematical model above.

A High Performance Model for Task Allocation in Distributed Computing System Using K-Means Clustering Technique

2018 ◽  
Vol 9 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Harendra Kumar ◽  
Nutan Kumari Chauhan ◽  
Pradeep Kumar Yadav
Keyword(s):  
Mathematical Model ◽  
Distributed Computing ◽  
High Performance ◽  
Computing System ◽  
Performance Model ◽  
Communication Costs ◽  
Clustering Technique ◽  
Proposed Model ◽  
Two Stages ◽  
The Mathematical Model

Tasks allocation is an important step for obtaining high performance in distributed computing system (DCS). This article attempts to develop a mathematical model for allocating the tasks to the processors in order to achieve optimal cost and optimal reliability of the system. The proposed model has been divided into two stages. Stage-I, makes the ‘n' clusters of set of ‘m' tasks by using k-means clustering technique. To use the k-means clustering techniques, the inter-task communication costs have been modified in such a way that highly communicated tasks are clustered together to minimize the communication costs between tasks. Stage-II, allocates the ‘n' clusters of tasks onto ‘n' processors to minimize the system cost. To design the mathematical model, executions costs and inter tasks communication costs have been taken in the form of matrices. To test the performance of the proposed model, many examples are considered from different research papers and results of examples have compared with some existing models.

A High Performance Model for Task Allocation in Distributed Computing System Using K-Means Clustering Technique

2021 ◽  
pp. 1244-1268
Author(s):  
Harendra Kumar ◽  
Nutan Kumari Chauhan ◽  
Pradeep Kumar Yadav
Keyword(s):  
Mathematical Model ◽  
Distributed Computing ◽  
High Performance ◽  
Computing System ◽  
Performance Model ◽  
Communication Costs ◽  
Clustering Technique ◽  
Proposed Model ◽  
Two Stages ◽  
The Mathematical Model

Tasks allocation is an important step for obtaining high performance in distributed computing system (DCS). This article attempts to develop a mathematical model for allocating the tasks to the processors in order to achieve optimal cost and optimal reliability of the system. The proposed model has been divided into two stages. Stage-I, makes the ‘n' clusters of set of ‘m' tasks by using k-means clustering technique. To use the k-means clustering techniques, the inter-task communication costs have been modified in such a way that highly communicated tasks are clustered together to minimize the communication costs between tasks. Stage-II, allocates the ‘n' clusters of tasks onto ‘n' processors to minimize the system cost. To design the mathematical model, executions costs and inter tasks communication costs have been taken in the form of matrices. To test the performance of the proposed model, many examples are considered from different research papers and results of examples have compared with some existing models.

Construction of high performance computing system for fusion research using cluster technology

2009 ◽  
Vol 29 (8) ◽  
pp. 2132-2135
Author(s):  
Wei PAN ◽  
Liao-yuan CHEN ◽  
Yong-ge LI ◽  
Jin-hua ZHANG ◽  
Li PAN ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Computing System ◽  
Fusion Research ◽  
High Performance Computing System ◽  
Performance Computing ◽  
Cluster Technology

Temperature Influence on Performance of Oxidation Ponds

1991 ◽  
Vol 24 (5) ◽  
pp. 85-96 ◽  
Author(s):  
Qingliang Zhao ◽  
Zijie Zhang
Keyword(s):  
Mathematical Model ◽  
Temperature Influence ◽  
Field Scale ◽  
Experiment Data ◽  
Computer Technique ◽  
Oxidation Pond ◽  
Optimum Model ◽  
Oxidation Ponds ◽  
The Mathematical Model ◽  
The Relationship

By means of simulated tests of a laboratory–scale oxidation pond model, the relationship between BOD5 and temperature fluctuation was researched. Mathematical modelling for the pond's performance and K1determination were systematically described. The calculation of T–K1–CeCe/Ci) was complex but the problem was solved by utilizing computer technique in the paper, and the mathematical model which could best simulate experiment data was developed. On the basis of experiment results,the concept of plug–ratio–coefficient is also presented. Finally the optimum model recommended here was verified with the field–scale pond data.

Influence of Tooth Errors and Truing Principles of Grinding Wheel Abrasion for Machining Arc Enveloping Worm

2013 ◽  
Vol 652-654 ◽  
pp. 2153-2158
Author(s):  
Wu Ji Jiang ◽  
Jing Wei
Keyword(s):  
Mathematical Model ◽  
Case Studies ◽  
High Precision ◽  
High Performance ◽  
New Method ◽  
Grinding Wheel ◽  
Grinding Process ◽  
The Mathematical Model

Controlling the tooth errors induced by the variation of diameter of grinding wheel is the key problem in the process of ZC1 worm grinding. In this paper, the influence of tooth errors by d1, m and z1 as the grinding wheel diameter changes are analyzed based on the mathematical model of the grinding process. A new mathematical model and truing principle for the grinding wheel of ZC1 worm is presented. The shape grinding wheel truing of ZC1 worm is carried out according to the model. The validity and feasibility of the mathematical model is proved by case studies. The mathematical model presented in this paper provides a new method for reducing the tooth errors of ZC1 worm and it can meet the high-performance and high-precision requirements of ZC1 worm grinding.

A High-Performance Computing System for Probabilistic Weather Forecasts

2019 ◽  
Author(s):  
Weiming Hu ◽  
Guido Cervone ◽  
Vivek Balasubramanian ◽  
Matteo Turilli ◽  
Shantenu Jha
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Computing System ◽  
Weather Forecasts ◽  
High Performance Computing System ◽  
Performance Computing

scholarly journals Taxonomic assignment for large-scale metagenomic data on high-perfomance systems

2017 ◽  
Vol 33 (2) ◽  
pp. 119-130
Author(s):  
Vinh Van Le ◽  
Hoai Van Tran ◽  
Hieu Ngoc Duong ◽  
Giang Xuan Bui ◽  
Lang Van Tran
Keyword(s):  
High Performance Computing ◽  
Assignment Problem ◽  
High Performance ◽  
Large Scale ◽  
Computing System ◽  
Metagenomic Data ◽  
Taxonomic Assignment ◽  
High Performance Computing System ◽  
Powerful Approach ◽  
Performance Computing

Metagenomics is a powerful approach to study environment samples which do not require the isolation and cultivation of individual organisms. One of the essential tasks in a metagenomic project is to identify the origin of reads, referred to as taxonomic assignment. Due to the fact that each metagenomic project has to analyze large-scale datasets, the metatenomic assignment is very much computation intensive. This study proposes a parallel algorithm for the taxonomic assignment problem, called SeMetaPL, which aims to deal with the computational challenge. The proposed algorithm is evaluated with both simulated and real datasets on a high performance computing system. Experimental results demonstrate that the algorithm is able to achieve good performance and utilize resources of the system efficiently. The software implementing the algorithm and all test datasets can be downloaded at http://it.hcmute.edu.vn/bioinfo/metapro/SeMetaPL.html.

Sign in / Sign up

Export Citation Format

Share Document