Availability of adaptive potential shifts favor from nonmutators to mutators. Area plots show non-vs. mutator fixation probabilities. As available beneficial mutations are increased, mutators gain favor in progressively larger population sizes. Shaded bands show bootstrapped 95% confidence intervals.
throughput stable scaling
per-PE workload
1 2 4 6 8 10 12 14 16
Wafer-Scale Simulation of Mutator Allele Dynamics
in Large Asexual Populations
hypermutators
favored
normomutators favored
Matthew Andres
Moreno
(UMich)
Emily Dolson (MSU)
Luis
Zaman
(UMich)
morenoma@umich.edu
“normomutator”
“hypermutator”
Raynes et al., 2018:
🔎
Cerebras
CS-2
Wafer-Scale
Engine
NVIDIA A100 GPU
850k processors
MSU Computer Science & Engineering
Ecology, Evolution, & Behavior
hyper�mutator: 100x mut probs
parent
offspring
vs
fixation probability
Results
Introduction
Simulation Design
Discussion
Buitrago, P. A. & Nystrom, N. A. (2021). Neocortex and Bridges-2: A High Performance AI+HPC Ecosystem for Science, Discovery, and Societal Good, 205–219. Carla 2020. https://doi.org/10.1007/978-3-030-68035-0_15
Brashear, W., Chakravorty, D., He, Z., O’Connor, D., Siegmann, E., Buitrago, P. A., & Sanielevici, S. (2025). ByteBoost: An advanced cybertraining program designed to enhance research on testbed systems. In Practice and Experience in Advanced Research Computing 2025: The Power of Collaboration (pp. 1–5). PEARC ’25: Practice and Experience in Advanced Research Computing. ACM. https://doi.org/10.1145/3708035.3736082
John C. Gunther (2014). Algorithm 938: Compressing circular buffers. ACM Trans. Math. Softw. 40, 2, Article 17 (February 2014), 12 pages. https://doi.org/10.1145/2559995
Moreno M. A., Zaman L., & Dolson E. (2024). Structured Downsampling for Fast, Memory-efficient Curation of Online Data Streams. arXiv preprint arXiv:2409.06199. https://doi.org/10.48550/arXiv.2409.06199
Y. Raynes, C.S. Wylie, P.D. Sniegowski, & D.M. Weinreich (2018), Sign of selection on mutation rate modifiers depends on population size, Proc. Natl. Acad. Sci. USA 115 (13) 3422-3427, https://doi.org/10.1073/pnas.1715996115.
Olivier Tenaillon, Bruno Toupance, Hervé Le Nagard, François Taddei, Bernard Godelle (1999), Mutators, Population Size, Adaptive Landscape and the Adaptation of Asexual Populations of Bacteria, Genetics, Volume 152, Issue 2, 1 June, Pages 485–493, https://doi.org/10.1093/genetics/152.2.485
294× speedup on WSE vs GPU
Future Work
Performance Benchmark
References
UMich Ecology & Evolutionary Biology
Complex Systems
MIDAS
← poster abstract @ hopth.ru/fm
This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR), under Award Number DE-SC0025634. This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
This material is also based upon work supported by the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a Schmidt Sciences program. Computational resources were provided by MSU iCER and by Pittsburgh Supercomputing Center Neocortex under NSF ACCESS Innovative Projects Allocation BIO240102 (Buitrago & Nystrom, 2021). This work was made possible by the ByteBoost 2024 workshop (Brashear et al., 2025). Thank you also to M. Jacquelin and L. Wilson at Cerebras.
Acknowledgement
On-device Data Management
Cerebras Software Language (CSL) simulation code at https://github.com/mmore500/
(MIT License).
Spatiotemporal composition of simulated populations from Wafer-Scale Engine experiments. The snapshots depict populations of 191 million agents with a 2D spatial structure, modeled using a site-explicit genome framework configured with an adaptive potential of 14 beneficial mutations. Mutators are introduced de novo. Raster values are binary: white pixels represent sampled non-mutators, while black pixels represent sampled mutators. Panels show replicates in which mutators either fail to reach fixation (a) or succeed in reaching fixation (b).
Initial supply of mutators influences nonmutator resilience to available adaptive potential. When initially rare, substantially more adaptive potential is required to fix mutator alleles. Error bands provide bootstrapped 95% confidence intervals.
Restricted adaptive potential favors nonmutators in large populations. When adaptive potential is unlimited (left column), mutators are strongly favored in large populations. Right column shows fixation outcomes across population sizes with adaptive potential restricted to one beneficial mutation. Under the adaptation-restricted regime, nonmutators regain favor at very large population sizes. Top row shows results with 32 agents per PE (pop size size up to 23.9 million). Bottom row shows results with 2,048 agents per PE (population size up to 1.5 billion). Error bands indicate bootstrapped 95% confidence intervals.
256 agents per PE
2048 agents per PE
50/50 Treatment
hypermuts fix
–or–
normomuts fix
N
H
mix normo/hyper
H
N
H
N
N
H
H
H
H
H
H
H
H
H
H
H
N
N
N
N
N
N
N
N
N
N
N
De Novo Treatment
1 in 100k
all normomuts
N
H
N
N
N
N
N
N
–or–
normomuts fix
H
H
H
H
H
H
H
H
H
H
N
N
N
N
N
N
N
N
N
N
N
hypermuts fix
N