Supplementary Materials for "High-Performance and Scalable Agent-Based Simulation with BioDynaMo"
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This repository contains all Supplementary Materials for the paper "High-Performance and Scalable Agent-Based Simulation with BioDynaMo" and <strong>received the Best Artifact Award at PPoPP '23</strong>. 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 <strong>Citation</strong>: If you find this repository useful, please cite the following works: Lukas Breitwieser et al., <strong>High-Performance and Scalable Agent-Based Simulation with BioDynaMo.</strong> 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] <pre><code>@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" }</code></pre> Lukas Breitwieser et al., <strong>BioDynaMo: a modular platform for high-performance agent-based simulation</strong>, Bioinformatics, Volume 38, Issue 2, 15 January 2022, Pages 453–460, https://doi.org/10.1093/bioinformatics/btab649 <pre><code>@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} }</code></pre> <strong>License</strong> 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.042 | 0.000 |
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 it