Adaptive and optimized agent placement scheme for parallel agent‐based simulation

Abstract

This study presents a noble scheme for distributed and parallel simulations with optimized agent placement for simulation instances. The traditional parallel simulation has some limitations in that it does not provide sufficient performance even though using multiple resources. The main reason for this discrepancy is that supporting parallelism inevitably requires additional costs in addition to the base simulation cost. We present a comprehensive study of parallel simulation architectures, execution flows, and characteristics. Then, we identify critical challenges for optimizing large simulations for parallel instances. Based on our cost–benefit analysis, we propose a novel approach to overcome the performance constraints of agent‐based parallel simulations. We also propose a solution for eliminating the synchronizing cost among local instances. Our method ensures balanced performance through optimal deployment of agents to local instances and an adaptive agent placement scheme according to the simulation load. Additionally, our empirical evaluation reveals that the proposed model achieves better performance than conventional methods under several conditions.

QPlayer: Lightweight, scalable, and fast quantum simulator

“…The Quantum Supremacy Circuit Simulation of Tsinghua University implemented a quantum circuit simulator in 16 384 nodes Sunway TaihuLight, whose results show that 49 qubits with depth 39 are reachable for current universal random circuits [33]. The common goal of these studies is to maximize distributed computing capabilities by improving parallelism and reducing communication overhead through OpenMP [34] and GPU [35]. However, these techniques only focus on utilizing substantial computing resources, not eliminating the fundamental problems of digital quantum simulation.…”