ACM Transactions on Modeling and Computer Simulation最新文献

This is Part 2 of a trio of works intended to provide a unifying framework in which conservative and optimistic synchronization for parallel discrete event simulations can be freely and transparently combined in the same logical process on an event-by-event basis. In this article, we continue the outline of an approach called Unified Virtual Time (UVT) that was introduced in Part 1, showing in detail via two extended examples how conservative synchronization can be refactored and combined with optimistic synchronization in the UVT framework. We describe UVT versions of both a basic time windowing algorithm called Unified Simple Time Windows and a refactored version of the Chandy-Misra-Bryant Null Message algorithm called Unified CMB.

Symbiotic simulation systems that incorporate data-driven methods (such as machine/deep learning) are effective and efficient tools for just-in-time (JIT) operational decision making. With the growing interest on Digital Twin City, such systems are ideal for real-time microscopic traffic simulation. However, learning-based models are heavily biased towards the training data and could produce physically inconsistent outputs. In terms of microscopic traffic simulation, this could lead to unsafe driving behaviours causing vehicle collisions in the simulation. As for symbiotic simulation, this could severely affect the performance of real-time base simulation models resulting in inaccurate or unrealistic forecasts, which could, in turn, mislead JIT what-if analyses. To overcome this issue, a physics-guided data-driven modelling paradigm should be adopted so that the resulting model could capture both accurate and safe driving behaviours. However, very few works exist in the development of such a car-following model that can balance between simulation accuracy and physical consistency. Therefore, in this paper, a new “jointly-trained physics-guided Long Short-Term Memory (JTPG-LSTM)” neural network, is proposed and integrated to a dynamic data-driven simulation system to capture dynamic car-following behaviours. An extensive set of experiments was conducted to demonstrate the advantages of the proposed model from both modelling and simulation perspectives.

Stable simulation of conservation laws, such as those used to model fluid dynamics and plasma physics applications, requires the satisfaction of the so-called Courant-Friedrichs-Lewy condition. By allowing regions of the mesh to advance with different timesteps that locally satisfy this stability constraint, significant work reduction can be attained when compared to a time integration scheme using a single timestep size. However, parallelizing this algorithm presents considerable difficulty. Since the stability condition depends on the state of the system, dependencies become dynamic and potentially non-local. In this article, we present an adaptive local timestepping algorithm using an optimistic (Timewarp-based) parallel discrete event simulation. We introduce waiting heuristics to limit misspeculation and a semi-static load balancing scheme to eliminate load imbalance as parts of the mesh require finer or coarser timesteps. Last, we outline an interface for separating the physics of the specific conservation law from the temporal integration allowing for productive adoption of our proposed algorithm. We present a misspeculation study for three conservation laws, demonstrating both the productivity of the local timestepping API, for which 74% of the lines of code are reused across different conservation laws, and the robustness of the waiting heuristics—at most 1.5% of element updates are rolled back. Our performance studies demonstrate up to a 2.8× speedup versus a baseline unoptimized local timestepping approach, a 4x improvement in per-node throughput compared to an MPI parallelization of synchronous timestepping, and scalability up to 3,072 cores on NERSC’s Cori Haswell partition.

In this article, we propose an efficient general simulation method for diffusions that are solutions to stochastic differential equations with discontinuous coefficients and local time terms. The proposed method is based on sampling from the corresponding continuous-time Markov chain approximation. In contrast to existing time discretization schemes, the Markov chain approximation method corresponds to a spatial discretization scheme and is demonstrated to be particularly suited for simulating diffusion processes with discontinuities in their state space. We establish the theoretical convergence order and also demonstrate the accuracy and robustness of the method in numerical examples by comparing it to the known benchmarks in terms of root mean squared error, runtime, and the parameter sensitivity.