scholarly journals Portable Library of Migratable Sockets

2001 ◽  
Vol 9 (4) ◽  
pp. 211-222 ◽  
Author(s):  
Marian Bubak ◽  
Dariusz Żbik ◽  
Dick van Albada ◽  
Kamil Iskra ◽  
Peter Sloot
Keyword(s):  
Parallel Computing ◽  
Distributed Computing ◽  
Load Balancing ◽  
High Performance ◽  
Internal Communication ◽  
Network Interface ◽  
Prototype Implementation ◽  
Computing Environments ◽  
User Space ◽  
Socket Interface

Efficient load balancing is essential for parallel distributed computing. Many parallel computing environments use TCP or UDP through the socket interface as a communication mechanism. This paper presents the design and development of a prototype implementation of a network interface that can preserve communication between processes during process migration. This new communication library is a substitution for the well-known socket interface. It is implemented in user — space; it is portable, and no modifications of user applications are required. TCP/IP is applied for internal communication, which guarantees relatively high performance and portability.

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.

scholarly journals Analysis of Effective Load Balancing Techniques in Distributed Environment

2020 ◽  
Author(s):  
Anju Shukla ◽  
Shishir Kumar ◽  
Harikesh Singh
Keyword(s):  
Distributed Computing ◽  
Load Balancing ◽  
Performance Optimization ◽  
Resource Selection ◽  
High Performance ◽  
Distributed Environment ◽  
Distributed Resources ◽  
Heterogeneous Services ◽  
Future Extension ◽  
Geographically Distributed

Computational approaches contribute a significance role in various fields such as medical applications, astronomy, and weather science, to perform complex calculations in speedy manner. Today, personal computers are very powerful but underutilized. Most of the computer resources are idle; 75% of the time and server are often unproductive. This brings the sense of distributed computing, in which the idea is to use the geographically distributed resources to meet the demand of high-performance computing. The Internet facilitates users to access heterogeneous services and run applications over a distributed environment. Due to openness and heterogeneous nature of distributed computing, the developer must deal with several issues like load balancing, interoperability, fault occurrence, resource selection, and task scheduling. Load balancing is the mechanism to distribute the load among resources optimally. The objective of this chapter is to discuss need and issues of load balancing that evolves the research scope. Various load balancing algorithms and scheduling methods are analyzed that are used for performance optimization of web resources. A systematic literature with their solutions and limitations has been presented. The chapter provides a concise narrative of the problems encountered and dimensions for future extension.

scholarly journals K-way Balanced Graph Partitioning for Parallel Computing

2021 ◽  
Vol 22 (4) ◽  
pp. 413-424
Author(s):  
Siddheshwar Vilas Patil ◽  
Dinesh B. Kulkarni
Keyword(s):  
Parallel Computing ◽  
Load Balancing ◽  
Graph Partitioning ◽  
High Performance ◽  
Ground Truth ◽  
Complex Problem ◽  
Maximum Throughput ◽  
Graph Partitioning Problem ◽  
Use Of Resources ◽  
Partitioning Problem

In modern computing, high-performance computing (HPC) and parallel computing require most of the decision-making in terms of distributing the payloads (input) uniformly across the available set of resources, majorly processors; the former deals with the hardware and its better utilization. In parallel computing, a larger, complex problem is broken down into multiple smaller calculations and executed simultaneously on several processors. The efficient use of resources (processors) plays a vital role in achieving the maximum throughput which necessitates uniform load distribution across available processors, i.e. load balancing. The load balancing in parallel computing is modeled as a graph partitioning problem. In the graph partitioning problem, the weighted nodes represent the computing cost at each node, and the weighted edges represent the communication cost between the connected nodes. The goal is to partition the graph G into k partitions such that: I) the sum of weights on the nodes is approximately equal for each partition, and, II) the sum of weights on the edges across different partitions is minimum.  In this paper, a novel node-weighted and edge-weighted k-way balanced graph partitioning (NWEWBGP) algorithm of  O(n x n)  is proposed. The algorithm works for all relevant values of k, meets or improves on earlier algorithms in terms of balanced partitioning and lowest edge-cut. For evaluation and validation, the outcome is compared with the ground truth benchmarks.

3D Visualization of Molecular Simulations in High-performance Parallel Computing Environments

2004 ◽  
Vol 30 (7) ◽  
pp. 469-477 ◽  
Author(s):  
Karsten Meier ◽  
Christopher Holzknecht ◽  
Stephan Kabelac ◽  
Stephan Olbrich ◽  
Karsten Chmielewski
Keyword(s):  
Parallel Computing ◽  
High Performance ◽  
3D Visualization ◽  
Molecular Simulations ◽  
Computing Environments
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