Learning not to share

J. Liu, D. Nicol

Research output: Contribution to conferencePaperpeer-review


Strong reasons exist for executing a large-scale discrete-event simulation on a cluster of processor nodes (each of which may be a shared-memory multiprocessor or a uniprocessor). This is the architecture of the largest scale parallel machines, and so the largest simulation problems can only be solved this way It is a common architecture even in less esoteric settings, and is suitable for memory-bound simulations. This paper describes our approach to porting the SSF simulation kernel to this architecture, using the Message Passing Interface (MPI) system. The notable feature of this transformation is to support an efficient two-level synchronization and communication scheme that addresses cost discrepancies between shared-memory and distributed memory In the initial implementation, we use a globally synchronous approach between distributed-memory nodes, and an asynchronous shared-memory approach within a SMP cluster. The SSF API reflects inherently shared-memory assumptions; we report therefore on our approach for porting an SSF kernel to a cluster of SMP nodes. Experimental results on two architectures are described, for a model of TCP/IP traffic flows over a hierarchical network. The performance on a distributed network of commodity SMPs connected through ethernet is seen to frequently exceed performance on a Sun shared-memory multiprocessor.

Original languageEnglish (US)
Number of pages10
StatePublished - 2001
Externally publishedYes
Event15th Workshop on Parallel and Distributed Simulation (PADS 2001) - Lake Arrowhead, CA, United States
Duration: May 15 2001May 18 2001


Other15th Workshop on Parallel and Distributed Simulation (PADS 2001)
Country/TerritoryUnited States
CityLake Arrowhead, CA

ASJC Scopus subject areas

  • General Engineering


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