External adjustment of runtime parameters in Time Warp synchronized parallel simulators (1997)
| Venue: | In 11th International Parallel Processing Symposium, (IPPS'97 |
| Citations: | 3 - 1 self |
BibTeX
@INPROCEEDINGS{Radhakrishnan97externaladjustment,
author = {Radharamanan Radhakrishnan and Lantz Moore and Philip A. Wilsey},
title = {External adjustment of runtime parameters in Time Warp synchronized parallel simulators},
booktitle = {In 11th International Parallel Processing Symposium, (IPPS'97},
year = {1997},
publisher = {IEEE Computer Society Press}
}
OpenURL
Abstract
Several optimizations to the Time Warp synchronization protocol for parallel discrete event simulation have been proposed and studied. Many of these optimizations have included some form of dynamic adjustment (or control) of the operating parametersof the simulation (e.g., checkpoint interval, cancellation strategy). Traditionally dynamic parameter adjustment has been performed at the simulation object level; each simulation object collects measures of its operating behaviors (e.g., rollback frequency, rollback length, etc) and uses them to adjust its operating parameters. The performance data collection functions and parameter adjustment are overhead costs that are incurred in the expectation of higher throughput. This paper presents a method of eliminating some of these overheads through the use of an external object to adjust the control parameters. That is, instead of inserting code for adjusting simulation parametersin the simulation object, an external control object is defined to periodically analyze each simulation object's performance data and revise that object's operating parameters. An implementation of an external control object in the WARPED Time Warp simulation kernel has been completed. The simulation parameters updated by the implemented control system are: checkpoint interval, and cancellation strategy (lazy or aggressive). A comparative analysis of three test cases shows that the external control mechanism provides speedups between 5%-17 % over the best performing embedded dynamic adjustment algorithms. 1







