article · 1989
Massively parallel switch-level simulation: a feasibility study
Saul Kravitz, Randal E. Bryant, Rob A. Rutenbar
Cited by 17Open access
Abstract
This work addresses the feasibility of mapping the COSMOS switch-level simulator onto a computer with thousands of simple processors. COSMOS preprocesses transistor networks into Boolean behavioral models, capturing the switch-level behavior of a circuit in a set of Boolean formulas. We describe a class of massively parallel computers and a mapping of COSMOS onto these computers. We discuss the factors affecting the performance of such a massively parallel simulator including: the amount of parallelism in the simulation model, performance measures for massively parallel machines, and the impact of event scheduling on simulator performance. We have developed compilation tools which automatically map a MOS circuit onto a massively parallel computer. Massively parallel switch-level simulation is illustrated by describing our pilot implementation on a 32k processor Thinking Machines Connection Machine System.
Cite this paper
Kravitz, S., Bryant, R. E., & Rutenbar, R. A. (1989). Massively parallel switch-level simulation: a feasibility study. https://doi.org/10.1145/74382.74399
Kravitz, Saul, et al. “Massively parallel switch-level simulation: a feasibility study.” 1989, pp. 91–97, https://doi.org/10.1145/74382.74399.
Kravitz, Saul, Randal E. Bryant, and Rob A. Rutenbar. 1989. “Massively parallel switch-level simulation: a feasibility study.” https://doi.org/10.1145/74382.74399.
Kravitz, S., Bryant, R.E. and Rutenbar, R.A. (1989) ‘Massively parallel switch-level simulation: a feasibility study’. Available at: https://doi.org/10.1145/74382.74399.
@article{kravitz1989massively,
title = {{Massively parallel switch-level simulation: a feasibility study}},
author = {Kravitz, Saul and Bryant, Randal E. and Rutenbar, Rob A.},
pages = {91--97},
year = {1989},
doi = {10.1145/74382.74399}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.