ACM Home Page
Please provide us with feedback. Feedback
Massively parallel simulations of ATM systems
Full text PdfPdf (701 KB)
Source Workshop on Parallel and Distributed Simulation archive
Proceedings of the tenth workshop on Parallel and distributed simulation table of contents
Philadelphia, Pennsylvania, United States
Pages: 39 - 46  
Year of Publication: 1996
ISBN:0-8186-7539-X
Also published in ...
Authors
Krishnan Kumaran  Department of Physics, Rutgers University, Piscataway, NJ and AT&T Bell Labs, Murray Hill, NJ
Boris Lubachevsky  Lucent Technologies, Bell Labs Innovations, 600 Mountain Avenue, Murray Hill, NJ
Anwar Elwalid  Lucent Technologies, Bell Labs Innovations, 600 Mountain Avenue, Murray Hill, NJ
Sponsors
IEEE-CS\TCSIM : TC on Simulation
SIGSIM: ACM Special Interest Group on Simulation and Modeling
SCS : Society for Computer Simulation
Publisher
IEEE Computer Society  Washington, DC, USA
Bibliometrics
Downloads (6 Weeks): 2,   Downloads (12 Months): 9,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/238788.238810
What is a DOI?

ABSTRACT

We simulate models of ATM communication systems on a massively parallel SIMD computer. Fast simulations of ATM models are needed because the regimes of interest usually involve high volumes of traffic and low failure rates. Unexpected practical and theoretical difficulties, partly due to the massive parallelism and SIMD aspects, were encountered and we show how to cope with them. In a replica-parallel simulation of an ATM system, large variations in computed statistics are caused by small differences in the distribution of employed random number generators. A comparison of these distributions using a secondary statistical measure served to disambiguate the results. It was also found that time-parallel simulations of ATM systems with Markov sources can be efficiently performed using parallel prefix methods only when the sources have a small number of states, while more complex sources require end-state matching for efficient simulation. We discovered that, with the proper choice of initial state distributions and partial regeneration points, the time and memory requirements can be much improved. Our simulations were carried out on the MasPar MP-1216 system with 16,384 processors, which was compared against an SGI workstation. We achieved about 60%-70% efficiency (speed-up of approx 35 compared to the ideal of approx 51).


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
K. M. Chandy and R. Sherman. Spacetime and simulation. Proceedings of the 1989 SCS Multiconference on Distributed Simulation, pages 53-57, March 1989.
 
2
Anwar Elwalid, Debasis Mitra, and Robert H. Wentworth. A new approach for allocating buffers and bandwidth to heterogenous, regulated tramc in an atm node. IEEE Journal on Selected Areas in Communications, 13(6):1115-1127, Aug. 1995.
3
 
4
Richard M. Fujimoto, C. Anthony Cooper, and Ioanis Nikolaidis. Parallel simulation of statistical multiplexers. 32nd IEEE Conference on Decision and Control, 1993.
5
6
7

Collaborative Colleagues:
Krishnan Kumaran: colleagues
Boris Lubachevsky: colleagues
Anwar Elwalid: colleagues