High Performance and Distributed Computing

2020 ◽  
pp. 155-162
Author(s):  
Sebastiano Fabio Schifano ◽  
Eleonora Luppi ◽  
Didin Agustian Permadi ◽  
Thi Kim Oanh Nguyen ◽  
Nhat Ha Chi Nguyen ◽  
...  
Keyword(s):  
Distributed Computing ◽  
High Performance

Matlab and Parallel Computing

2012 ◽  
Vol 17 (4) ◽  
pp. 207-216 ◽  
Author(s):  
Magdalena Szymczyk ◽  
Piotr Szymczyk
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Parallel Computing ◽  
Distributed Computing ◽  
Control Systems ◽  
High Performance ◽  
Parallel Applications ◽  
Process Simulations ◽  
Key Features ◽  
Financial Process

Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB parallel applications focused on using GPU processors for image processing.

Concurrent Subspace Optimization Using Design Variable Sharing in a Distributed Computing Environment

1995 ◽  
Author(s):  
Brett A. Wujek ◽  
John E. Renaud ◽  
Stephen M. Batill ◽  
Jay B. Brockman
Keyword(s):  
Distributed Computing ◽  
Design Variable ◽  
High Performance ◽  
Low Cost ◽  
Multidisciplinary Design ◽  
Computing Environment ◽  
Subspace Optimization ◽  
System A ◽  
Implementation Studies ◽  
Concurrent Subspace Optimization

Abstract This paper reviews recent implementation advances and modifications in the continued development of a Concurrent Subspace Optimization (CSSO) algorithm for Multidisciplinary Design Optimization (MDO). The CSSO-MDO algorithm implemented in this research incorporates a Coordination Procedure of System Approximation (CP-SA) for design updates. Implementation studies detail the use of a new discipline based decomposition strategy which provides for design variable sharing across discipline design regimes (i.e., subspaces). The algorithm is implemented in a distributed computing environment, providing for concurrent discipline design. Implementation studies introduce a new multidisciplinary design test problem, the optimal design of a high performance, low cost structural system. A graphical user interface is developed which provides for menu driven execution and results display; this new programming environment highlights the modularity of the algorithm. Significant time savings are observed when using distributed computing for concurrent design across disciplines. The use of design variable sharing across disciplines does not introduce any difficulties in implementation as the design update in the CSSO-MDO algorithm is generated in the coordination procedure of system approximation (CP-SA).

A Review: Map Reduce Framework for Cloud Computing

2018 ◽  
Vol 7 (4.6) ◽  
pp. 13
Author(s):  
Mekala Sandhya ◽  
Ashish Ladda ◽  
Dr. Uma N Dulhare ◽  
. . ◽  
. .
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Distributed Computing ◽  
Data Storage ◽  
High Performance ◽  
Large Scale ◽  
Distributed Storage ◽  
Large Data ◽  
Mass Data ◽  
Internet Information

In this generation of Internet, information and data are growing continuously. Even though various Internet services and applications. The amount of information is increasing rapidly. Hundred billions even trillions of web indexes exist. Such large data brings people a mass of information and more difficulty discovering useful knowledge in these huge amounts of data at the same time. Cloud computing can provide infrastructure for large data. Cloud computing has two significant characteristics of distributed computing i.e. scalability, high availability. The scalability can seamlessly extend to large-scale clusters. Availability says that cloud computing can bear node errors. Node failures will not affect the program to run correctly. Cloud computing with data mining does significant data processing through high-performance machine. Mass data storage and distributed computing provide a new method for mass data mining and become an effective solution to the distributed storage and efficient computing in data mining. 

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.

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