The Stanford Dash Multiprocessor
Computer
The SPLASH-2 programs: characterization and methodological considerations
ISCA '95 Proceedings of the 22nd annual international symposium on Computer architecture
Journal of Parallel and Distributed Computing - Special issue on parallel computing with optical interconnects
Computer Networks and Systems: Queueing Theory and Performance Evaluation
Computer Networks and Systems: Queueing Theory and Performance Evaluation
Prediction model for evaluation of reconfigurable interconnects in distributed shared-memory systems
Proceedings of the 2005 international workshop on System level interconnect prediction
Traffic Temporal Analysis for Reconfigurable Interconnects in Shared-Memory Systems
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 3 - Volume 04
Reconfigurable interconnects in DSM systems: a focus on context switch behavior
ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
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In this paper, we attempt to model congestion on a reconfigurable multi-processor communication network. This reconfigurable network adapts its topology at given intervals to the properties of the network traffic, which may alter over time. Using our congestion model, one can quickly estimate packet latency for a given set of network parameters. This allows a network designer to do design-space explorations without having to resort to detailed, slow simulations.The model is derived by viewing the network as an interconnected set of queues and servers. For each reconfiguration interval and each network link, we construct inter-arrival and service time distributions. Queuing theory can now give us the average waiting time on a single link. Combining waiting times per link over a network path yields the congestion experienced by each packet.After presenting the model itself, we show simulation results and determine the accuracy of our model. We analyze the assumptions made by the model, their effect on the accuracy, and propose some possible future improvements.