GcpSensor: a CPU Performance Tool for Grid Environments

Authors:
Dong Guo;Liang Hu;Meng Zhang;Zhuopeng Zhang
Affiliations:
Jilin University, Changchun, China;Jilin University, Changchun, China;Jilin University, Changchun, China;De Montfort University, Leicester, England
Venue:
QSIC '05 Proceedings of the Fifth International Conference on Quality Software
Year:
2005

Citing 4
Cited 0

LDAP: programming directory-enabled applications with lightweight directory access protocol

LDAP: programming directory-enabled applications with lightweight directory access protocol
The network weather service: a distributed resource performance forecasting service for metacomputing

Future Generation Computer Systems - Special issue on metacomputing
A Directory Service for Configuring High-Performance Distributed Computations

HPDC '97 Proceedings of the 6th IEEE International Symposium on High Performance Distributed Computing
A Portable Programming Interface for Performance Evaluation on Modern Processors

International Journal of High Performance Computing Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In grid computing environments, CPU performance discovery and allocation is a serious issue. Before they run their applications, grid users need to choose grid nodes based on available CPUs performance. Hence a tool is needed for measuring CPUs performance. To address this need, GcpSensor (Grid CPU performance Sensor) was developed. GcpSensor runs a typical benchmark program in every grid node to measure CPU performance and returns a metric named WMFLOPS which can be used to compare different CPUs performance directly. An algorithm, which can choose a moderate computation cost dynamically, is designed to ensure that every test is comprehensive and the added overhead is comparatively small. Our experiments have proved that GcpSensor can indicate the CPU performance sensitively and that users can use the GcpSensor data to compare different CPUs performance straightforward, thus the accuracy and efficiency for selecting computing resources are increased.