By Topic

A scalable parallel workstation cluster system

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Chun-Lei Dong ; Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China ; Wei-Min Zheng ; Ding-Xing Wang ; Mei-Ming Sheng

In this paper, we argue that because of recent advance of network & CPU technologies, workstation clusters are poised to become the primary parallel computing infrastructure for science and engineering computing. After analyzing and comparing the communication performance of three popular networks: 10 Mbps Ethernet, 100 Mbps Ethernet and 640 Mbps Myrinet on an experimental workstation cluster, we point out that two main factors hinder the wider application of workstation cluster: low efficiency of communication system (both hardware and software) and lack of friendly parallel program development environment with accessory tools. For these two problem, we implemented two workstation cluster systems for different performance/price rate requirements: one is 8 PowerPCs with shared media network, another is 8 Sun Sparcstations with switch network. By using Reduced Communication Protocol (RCP), we dramatically improved the performance of communication system; by expanding the language support of PVM and adding several useful tools, we build a visual integrated parallel program development environment IPCE. On our platform, we also analyzed several massive applications, such as GRI benchmark, earthquake simulator, weather forecasting and some NAS benchmarks, and we get very good results for these coarse-grain to middle-grain applications. The speedup ranges from 5.83 to 7.98 and parallel efficiency reaches to 72.88%-99.7%

Published in:

Advances in Parallel and Distributed Computing, 1997. Proceedings

Date of Conference:

19-21 Mar 1997