Interference resilient PDES on multi-core systems: towards proportional slowdown

  • Authors:

  • Jingjing Wang;Nael Abu-Ghazaleh;Dmitry Ponomarev

  • Affiliations:

  • Binghamton University, Binghamton, NY, USA;Binghamton University, Binghamton, NY, USA;State University of New York at Binghamton, Binghamton, NY, USA

  • Venue:
  • Proceedings of the 2013 ACM SIGSIM conference on Principles of advanced discrete simulation
  • Year:
  • 2013

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Abstract

Parallel Discrete Event Simulation (PDES) harnesses the power of parallel processing to improve the performance and capacity of simulation, supporting bigger models, in more details and for more scenarios. PDES engines are typically designed and evaluated assuming a homogeneous parallel computing system that is dedicated to the simulation application. In this paper, we first show that the presence of interference from other users, even a single process in an arbitrarily large parallel environment, can lead to dramatic slowdown in the performance of the simulation. We define a new metric, which we call proportional slowdown, that represents the idealized target for graceful slowdown in the presence of interference. We identify some of the reasons why simulators fall far short of proportional slowdown. Based on these observations, we design alternative simulation scheduling and mapping algorithms that are better able to tolerate interference. More precisely, the most resilient simulators will allow dynamic mapping of simulation event execution to processing resources (a work pool model). However, this model has significant overhead and can substantially impact locality. Thus, we propose a locality-aware adaptive dynamic-mapping (LADM) algorithm for PDES on multi-core systems. LADM reduces the number of active threads in the presence of interference, avoiding having threads disabled due to context switching. We show that LADM can substantially reduce the impact of interference while maintaining memory locality reducing the gap with proportional slowdown. LADM and similar techniques can also help in situations where there is load imbalance or processor heterogeneity.