A large-scale distributed parallel discrete event simulation engines based on Warped2 for Wargaming simulation

TL;DR

This paper introduces an optimized large-scale distributed PDES framework based on Warped2, incorporating asynchronous monitoring, load balancing, entity interaction management, and spatial hashing to significantly improve performance and scalability in wargaming simulations.

Contribution

The paper presents four novel enhancements to Warped2-based PDES, addressing synchronization latency, dynamic load balancing, entity conflicts, and search complexity, leading to substantial performance gains.

Findings

16x acceleration over baseline implementations

8x speedup over single-thread configurations

58.18% reduction in synchronization overhead

Abstract

Rising demand for complex simulations highlights conventional engines'scalability limits, spurring Parallel Discrete Event Simulation (PDES) adoption.Warped2, a PDES engine leveraging Time Warp synchronization with Pending Event Set optimization, delivers strong performance, it struggles with inherent wargaming limitations: inefficient LP resource allocation during synchronization and unaddressed complex entity interaction patterns. To address these challenges, we present an optimized framework featuring four synergistic improvements: (1) Asynchronous listener threads are introduced to address event monitoring latency in large-scale scenarios, instead of synchronous polling mechanisms, (2) METIS-based load rebalancing strategy is incorporated to address the issue of dynamic event allocation during real-world simulation, (3) Entity interaction solver with constraint satisfaction mechanisms is designed to mitigate state conflicts, and (4) Spatial hashing algorithm to overcome O(n^2) complexity bottlenecks in large-scale nearest-neighbor searches. Experimental validation through a GridWorld demo demonstrates significant enhancements in temporal fidelity and computational efficiency. Benchmark results show our framework achieves 16x acceleration over baseline implementations and maintains 8x speedup over 1-thread configuration across MPI and Pthreads implementations.The combined load balancing and LP migration strategy reduces synchronization overhead by 58.18%, with load balancing accounting for 57% of the total improvement as the dominant optimization factor. These improvements provide an enhanced solution for PDES implementation in large-scale simulation scenarios.