Matthew Andres Moreno

Trust, but Verify: Rigorously Profiling Best-Effort High-Performance Computing for Digital Evolution

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Date August 25th, 2026
DOI 10.48550/arXiv.2608.23955
Venue arXiv
Abstract

Developments in high-performance computing (HPC) technology continue to drastically increase quantities of available processing power. In the context of digital evolution, this explosive growth offers opportunities to advance both hypothesis-driven explorations of multi-scale biological phenomena and application-driven evolutionary optimization targeting hard problem domains. A particular opportunity arises from emerging next-generation AI/ML hardware accelerator platforms, such as the 880,000-processor Cerebras Wafer-Scale Engine (WSE). Such hardware, however, constrains on-device data storage and movement — a challenge compounded by vulnerability to failures arising over numerous device components. Best-effort relaxations that depart from a traditional deterministic computing paradigm can help accommodate such constraints, but complicate reproducibility and risk introducing artifactual biases. We explore these concerns, developing a framework to measure runtime behavior of best-effort code and examining case studies of best-effort computing in digital evolution projects. The first case study applies best-effort CPU-cluster multiprocessing to a multicellularity evolution model, which provides 92% scaling efficiency at 64 processes (2.1× speedup) and exhibits robust median quality of service, even under hardware anomalies. The second case study examines WSE-based simulations, demonstrating best-effort strategies to track spatiotemporal population history — through sparse, asynchronous device-to-host sampling that tolerates hardware faults. In sum, across potential forms and scopes of best-effort relaxation, we argue that digital evolution is uniquely positioned to contribute in developing post-deterministic HPC paradigms.

BibTeX
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@misc{moreno2026trust,
      doi={10.48550/arXiv.2608.23955},
      url={https://arxiv.org/abs/2608.23955},
      title={Trust, but Verify: Rigorously Profiling Best-Effort High-Performance Computing for Digital Evolution},
      author={Matthew Andres Moreno and Santiago {Rodriguez Papa} and Charles Ofria and Luis Zaman and Emily Dolson},
      year={2026},
      eprint={2608.23955},
      archivePrefix={arXiv},
      primaryClass={cs.NE},
}
Citation
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Moreno M. A., Rodriguez Papa S., Ofria C., Zaman L., & Dolson E. (2026). Trust, but Verify: Rigorously Profiling Best-Effort High-Performance Computing for Digital Evolution. arXiv preprint arXiv:2608.23955. https://doi.org/10.48550/arXiv.2608.23955

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