A Parallel Computing Component for Linux Cluster with Threads Binding Supports

2010 ◽  
Author(s):  
Hang Zhou ◽  
Xi-min Wang ◽  
Ying-Hua Guo
Keyword(s):  
Parallel Computing ◽  
Linux Cluster

Collective Relocation for Associative Distributed Collections of Objects

2017 ◽  
Vol 5 (2) ◽  
pp. 55-69
Author(s):  
Daisuke Fujishima ◽  
Tomio Kamada
Keyword(s):  
Parallel Computing ◽  
Performance Evaluation ◽  
Programming Languages ◽  
Parallel Programming ◽  
Programming Language ◽  
Data Structures ◽  
Object Oriented ◽  
Linux Cluster ◽  
Data Elements ◽  
Oriented Parallel

The field of parallel computing has experienced an increase in the number of computing nodes, allowing broader applications, including computations that have irregular features. Some parallel programming languages handle object data structures and offer marshaling/unmarshaling mechanisms to transpose them. To manage data elements across computing nodes, some research on distributed collections has been conducted. This study proposes a distributed collection library that can handle multiple collections of object elements and change their distributions while maintaining associativity between their elements. This library is implemented on an object-oriented parallel programming language, X10. The authors assume pairs of associative collections such as vehicles and streets in a traffic simulation. When many vehicles are concentrated on streets assigned to certain computing nodes, some of these streets should be moved to other nodes. The authors' library assists the programmer in easily distributing the associative collections over the computing nodes and collectively relocating elements while maintaining the data sharing relationship among associative elements. The programmer can describe the associativity between objects by using both declarative and procedural methods. They show a preliminary performance evaluation of their library on a Linux cluster and the K computer.

Parallel Computing in Optimization

1998 ◽  
Vol 49 (7) ◽  
pp. 770-771
Author(s):  
V J Rayward-Smith
Keyword(s):  
Parallel Computing

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.

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