This repository contains Supplementary Materials for the paper: Breitwieser et al., "High-Performance and Scalable Agent-Based Simulation with BioDynaMo" available at https://doi.org/10.1145/3572848.3577480.
Warning Due to Github's file size limitation, the files
SF2-code.tar.gzandSF3-bdm-publication-image.tar.gzare not included in this repository, but are available on Zenodo: https://doi.org/10.5281/zenodo.7544675.
If you find this repository useful, please cite the following works:
Lukas Breitwieser et al., High-Performance and Scalable Agent-Based Simulation with BioDynaMo, arXiv, 17 January 2023, https://doi.org/10.48550/arXiv.2301.06984
@article{breitwieser_biodynamo_2023,
title = {{High-Performance and Scalable Agent-Based Simulation with BioDynaMo}},
url = {https://doi.org/10.48550/arXiv.2301.06984},
journal = {arXiv},
author = {Breitwieser, Lukas and Hesam, Ahmad and Rademakers, Fons and Luna, Juan Gómez and Mutlu, Onur},
year = {2023},
doi = {10.48550/arXiv.2301.06984},
}Lukas Breitwieser et al., BioDynaMo: a modular platform for high-performance agent-based simulation, Bioinformatics, Volume 38, Issue 2, 15 January 2022, Pages 453–460, https://doi.org/10.1093/bioinformatics/btab649
@article{breitwieser_biodynamo_2022,
author = {Breitwieser, Lukas and Hesam, Ahmad and de Montigny, Jean and Vavourakis, Vasileios and Iosif, Alexandros and Jennings, Jack and Kaiser, Marcus and Manca, Marco and Di Meglio, Alberto and Al-Ars, Zaid and Rademakers, Fons and Mutlu, Onur and Bauer, Roman},
title = "{BioDynaMo: a modular platform for high-performance agent-based simulation}",
journal = {Bioinformatics},
volume = {38},
number = {2},
pages = {453-460},
year = {2021},
month = {09},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btab649},
url = {https://doi.org/10.1093/bioinformatics/btab649}
}Agent-based modeling plays an essential role in gaining insights into biology, sociology, economics, and other fields. However, many existing agent-based simulation platforms are not suitable for large-scale studies due to the low performance of the underlying simulation engines. To overcome this limitation, we present a novel high-performance simulation engine.
We identify three key challenges for which we present the following solutions. First, to maximize parallelization, we present an optimized grid to search for neighbors and parallelize the merging of thread-local results. Second, we reduce the memory access latency with a NUMA-aware agent iterator, agent sorting with a space-filling curve, and a custom heap memory allocator. Third, we present a mechanism to omit the collision force calculation under certain conditions.
Our evaluation shows an order of magnitude improvement over Biocellion, three orders of magnitude speedup over Cortex3D and NetLogo, and the ability to simulate 1.72 billion agents on a single server.
SF0-additional-evaluations.pdf: This document complements the main paper by providing additional performance evaluations.SF1-readme.pdf: This document provides detailed documentation, step-by-step instructions to reproduce the results, and ideas on how to reuse and repurpose this artifact.SF2-code.tar.gz: This archive contains all the necessary code to produce all results shown in the paper.SF3-bdm-publication-image.tar.gz: This archive contains a self-contained docker image to simplify executing our benchmarks and aid long-term reproducibility.SF4-raw-results.tar.gz: This archive contains the raw results we obtained when we executed the benchmarks on our systems.
License information for the code repositories in SF2-code.tar.gz can be found in the files:
biodynamo/LICENSEbdm-paper-examples/LICENSE
Setting up a new system requires four steps.
More details can be found in Section 2 in SF1-readme.pdf.
- Download and extract the code archive in Supplementary File SF2.
- Install the following software packages on the host machine:
Docker (version >= 19.0.3),
Intel Vtune sampling driver (version 2022.2.0),
and the Linux
screencommand. - Load the docker image provided by Supplementary File SF3.
- Verify the setup by executing the following command in the
bdm-paper-examplesdirectory:docker/run.sh ./run-functional-evaluation.sh
We separate the benchmarks into multiple scripts with different memory and disk space requirements to allow researchers with less powerful hardware to execute a subset of benchmarks.
To execute one of the scripts below, change into the bdm-paper-examples directory, and pass the script as a parameter to the command docker/run.sh.
More details can be found in Section 4 in SF1-readme.pdf.
-
run-main.sh: This script executes the majority of benchmarks described in the evaluation section of the paper and outputs Figure 5 (left) and Figures 9--12. -
run-comparison-with-others.sh: This script executes the comparison of BioDynaMo with Cortex3D and NetLogo and outputs Figure 8. -
run-runtime-complexity.sh: This script analyses the runtime and memory consumption of BioDynaMo, with the number of agents increasing from$10^3$ to$10^9$ , and generates Figure 6. -
run-profiling.sh: This script performs the microarchitecture analysis for all simulations in Table 1 and generates Figure 5 (right). -
run-biocellion-cmprsn-single-node.sh: This script executes the small-scale comparison with Biocellion and the optimization analysis in Figure 7b (right). -
run-biocellion-cmprsn-cluster.sh: This script executes the large-scale comparison with Biocellion, the optimization analysis in Figure 7b (left), and renders the visualization in Figure 7a.
Besides reproducing the results, researchers can build upon our artifact in numerous ways.
A non-exhaustive list of possibilities with detailed instructions is given in Section 5 in SF1-readme.pdf.
These possibilities include:
- Add additional benchmarks
- Evaluate the effectiveness of additional optimizations
- Evaluate BioDynaMo's performance for additional simulations
Please contact us with any feedback or if you need any help building on BioDynaMo. You can reach us at [email protected] and [email protected].