High-Performance Computing Implementations of Agent-Based Economic Models for Realizing 1:1 Scale Simulations of Large Economies

Gill, A., Lalith, M., Poledna, S., Hori, M., Fujita, K., & Ichimura, T. (2021). High-Performance Computing Implementations of Agent-Based Economic Models for Realizing 1:1 Scale Simulations of Large Economies. IEEE Transactions on Parallel and Distributed Systems 32 (8) 2101-2114. 10.1109/TPDS.2021.3060462.

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Abstract

We present a scalable high-performance computing implementation of an agent-based economic model using distributed + shared-memory hybrid parallelization paradigms, capable of simulating 1:1 scale models of large economies like the eurozone. Agent-based economic models consist of millions of agents interacting over several graphs, which are either centralized or scale-free in nature. While most of the interactions are bi-directional, the interaction graphs are dense and random and keep evolving as the simulation progresses. These characteristics cause a very large and unknown number of random communications among MPI processes, posing challenges to developing scalable parallel extensions. Further, random access to large volume of data makes the algorithms highly memory-bound, severely degrading computational performance. Adopting various strategies inspired by the real-world functioning of economies, we reduce the large unknown number of communications to a known handful number. Memory-intensive algorithms are improved to make these cache-efficient, and advanced MPI functions are used to minimize communication overhead, thereby attaining higher performance and scalability. Further, an MPI + OpenMP hybrid model is developed to best utilize modern many-core computing nodes with low per-core memory capacity. It is demonstrated that our implementation can simulate a full fledged economic model with 331 million agents within 108 seconds using 128 CPU cores attaining 70 percent strong scalability.

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