Supplementary Materials for "High-Performance and Scalable Agent-Based Simulation with BioDynaMo"
Bibliographic record
Abstract
This repository contains all Supplementary Materials for the paper "High-Performance and Scalable Agent-Based Simulation with BioDynaMo" and received the Best Artifact Award at PPoPP '23. The paper is available at https://doi.org/10.1145/3572848.3577480 and https://doi.org/10.48550/arXiv.2301.06984. We provide detailed instructions to reproduce all results of the paper in file: SF1-readme.pdf Citation: If you find this repository useful, please cite the following works: Lukas Breitwieser et al., High-Performance and Scalable Agent-Based Simulation with BioDynaMo. 2023, In Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (Montreal, QC, Canada) (PPoPP ’23). Association for Computing Machinery, New York, NY, USA, 174–188. https://doi.org/10.1145/3572848.3577480 arXiv:2301.06984 [cs.DC] @inproceedings{breitwieser_biodynamo_2023, author = {Breitwieser, Lukas and Hesam, Ahmad and Rademakers, Fons and Luna, Juan G\'{o}mez and Mutlu, Onur}, title = {High-Performance and Scalable Agent-Based Simulation with BioDynaMo}, year = {2023}, isbn = {9798400700156}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3572848.3577480}, doi = {10.1145/3572848.3577480}, booktitle = {Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming}, pages = {174–188}, numpages = {15}, keywords = {NUMA, HPC, performance evaluation, parallel computing, space-filling curve, high-performance simulation, performance optimization, agent-based modeling, memory layout optimization, memory allocation, scalability}, location = {Montreal, QC, Canada}, series = {PPoPP '23}, archivePrefix = "arXiv", eprint = "2301.06984", primaryClass = "cs.DC" } 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} } License License information for the code repositories in SF2-code.tar.gz can be found in the files: biodynamo/LICENSE bdm-paper-examples/LICENSE
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.797 | 0.456 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".