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AIR: Application-Level Interference Resilience for PDES on Multicore Systems

Parallel discrete event simulation (PDES) harnesses parallel processing to improve the performance and capacity of simulation, supporting bigger and more detailed models simulated for more scenarios. The presence of interference from other users can lead to dramatic slowdown in the performance of the simulation. Interference is typically managed using operating system scheduling support (e.g., gang scheduling), a heavyweight approach with some drawbacks. We propose an application-level approach to interference resilience through alternative simulation scheduling and mapping algorithms. More precisely, the most resilient simulators allow dynamic mapping of simulation event execution to processing resources (a work pool model). However, this model has significant scheduling overhead and poor cache locality. Thus, we investigate using application-level interference mitigation where the application detects the presence of interference and reacts by changing the thread task allocation. Specifically, we propose a locality-aware adaptive dynamic mapping (LADM) algorithm that adjusts the number of active threads on the fly by detecting the presence of interference. LADM avoids having the application stall when threads are inactive due to context switching. We investigate different mechanisms for monitoring the level of interference and different approaches for remapping tasks. We show that LADM can substantially reduce the impact of interference while maintaining memory locality.