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2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation (1997)
Lockenhaus, AUSTRIA
June 10, 1997 to June 13, 1997
ISSN: 1087-4097
ISBN: 0-8186-7964-6
pp: 108
Kiran S. Panesar , Intel Corporation
Richard M. Fujimoto , Georgia Institute of Technology
ABSTRACT
It is well known that Time Warp may suffer from poor performance due to excessive rollbacks caused by overly optimistic execution. Here we present a simple flow control mechanism using only local information and GVT that limits the number of uncommitted messages generated by a processor, thus throttling overly optimistic TW execution. The flow control scheme is analogous to traditional networking flow control mechanisms. A ``window'' of messages defines the maximum number of uncommitted messages allowed to be scheduled by a process. Committing messages is analogous to acknowledgments in networking flow control. The initial size of the window is calculated using a simple analytical model that estimates the instantaneous number of messages that a process will eventually commit. This window is expanded so that the process may progress up to the next commit point (generally the next fossil collection), and to accommodate optimistic execution. The expansions to the window are based on monitoring TW performance statistics so the window size automatically adapts to changing program behaviors.The flow control technique presented here is simple and fully automatic. No global knowledge or synchronization (other than GVT) is required. We also develop an implementation of the flow control scheme for shared memory multiprocessors that uses dynamically sized pools of free message buffers. Experimental data indicates that the adaptive flow control scheme maintains high performance for "balanced workloads'', and achieves as much as a factor of 7 speedup over unthrottled TW for certain irregular workloads.
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CITATION
Kiran S. Panesar, Richard M. Fujimoto, "Adaptive Flow Control in Time Warp", 2012 ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation, vol. 00, no. , pp. 108, 1997, doi:10.1109/PADS.1997.594593
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