A Scalable Trie Building Algorithm for High-Throughput Phyloanalysis of Wafer-Scale Digital Evolution Experiments

Agent-based simulation platforms play a key role in enabling fast-to-run evolution experiments that can be precisely controlled and observed in detail. Availability of high-resolution snapshots of lineage ancestries from digital experiments, in particular, is key to investigations of evolvability and open-ended evolution, as well as in providing a validation testbed for bioinformatics method development. Ongoing advances in AI/ML hardware accelerator devices, such as the 850,000-processor Cerebras Wafer-Scale Engine (WSE), are poised to broaden the scope of evolutionary questions that can be investigated in silico. However, constraints in memory capacity and locality characteristic of these systems introduce difficulties in exhaustively tracking phylogenies at runtime. To overcome these challenges, recent work on hereditary stratigraphy algorithms has developed space-efficient genetic markers to facilitate fully decentralized estimation of relatedness among digital organisms. However, in existing work, compute time to reconstruct phylogenies from these genetic markers has proven a limiting factor in achieving large-scale phyloanalyses. Here, we detail an improved trie-building algorithm designed to produce reconstructions equivalent to existing approaches. For modestly-sized 10,000-tip trees, the proposed approach achieves a 300-fold speedup versus existing state-of-the-art. Finally, using 1 billion genome datasets drawn from WSE simulations encompassing 954 trillion replication events, we report a pair of large-scale phylogeny reconstruction trials, achieving end-to-end reconstruction times of 2.6 and 2.9 hours. In substantially improving reconstruction scaling and throughput, presented work establishes a key foundation to enable powerful high-throughput phyloanalysis techniques in large-scale digital evolution experiments.

Extending a Phylogeny-based Method for Detecting Signatures of Multi-level Selection for Applications in Artificial Life

Multilevel selection occurs when short-term individual-level reproductive interests conflict with longer-term group-level fitness effects. Detecting and quantifying this phenomenon is key to understanding evolution of traits ranging from multicellularity to pathogen virulence. Multilevel selection is particularly important in artificial life research due to its connection to major evolutionary transitions, a hallmark of open-ended evolution. Bonetti Franceschi & Volz (2024) proposed to detect multilevel selection dynamics by screening for mutations that appear more often in a population than expected by chance (due to individual-level fitness benefits) but are ultimately associated with negative longer-term fitness outcomes (i.e., smaller, shorter-lived descendant clades). Here, we use agent-based modeling with known ground truth to assess the efficacy of this approach. To test these methods under challenging conditions broadly comparable to the original dataset explored by Bonetti Franceschi & Volz (2024), we use an epidemiological framework to model multilevel selection in trade-offs between within-host growth rate and between-host transmissibility. To achieve success on our in silico data, we develop an alternate normalization procedure for identifying clade-level fitness effects. We find the method to be sensitive in detecting genome sites under multilevel selection with 30% effect sizes on fitness, but do not see sensitivity to smaller 10% mutation effect sizes. To test the robustness of this methodology, we conduct additional experiments incorporating extrinsic, time-varying environmental changes and adaptive turnover in population compositions, and find that screen performance remains generally consistent with baseline conditions. This work represents a promising step towards rigorous generalizable quantification of multilevel selection effects.

A Guide to Tracking Phylogenies in Parallel and Distributed Agent-based Evolution Models

Computer simulations are an important tool for studying the mechanics of biological evolution. In particular, in silico work with agent-based models provides an opportunity to collect high-quality records of ancestry relationships among simulated agents. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Existing work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, direct tracking can be inefficient for large-scale, many-processor evolutionary simulations. An alternate approach to extracting phylogenetic information from simulation that scales more favorably is post hoc estimation, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recently introduced ``hereditary stratigraphy'' algorithms provide means for efficient inference of phylogenetic history from non-coding annotations on simulated organisms' genomes. A number of options exist in configuring hereditary stratigraphy methodology, but no work has yet tested how they impact reconstruction quality. To address this question, we surveyed reconstruction accuracy under alternate configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. We synthesize results from these experiments to suggest a prescriptive system of best practices for work with hereditary stratigraphy, ultimately guiding researchers in choosing appropriate instrumentation for large-scale simulation studies.

Trackable Island-model Genetic Algorithms at Wafer Scale

Emerging ML/AI hardware accelerators, like the 850,000 processor Cerebras Wafer-Scale Engine (WSE), hold great promise to scale up the capabilities of evolutionary computation. However, challenges remain in maintaining visibility into underlying evolutionary processes while efficiently utilizing these platforms' large processor counts. Here, we focus on the problem of extracting phylogenetic information from digital evolution on the WSE platform. We present a tracking-enabled asynchronous island-based genetic algorithm (GA) framework for WSE hardware. Emulated and on-hardware GA benchmarks with a simple tracking-enabled agent model clock upwards of 1 million generations a minute for population sizes reaching 16 million. This pace enables quadrillions of evaluations a day. We validate phylogenetic reconstructions from these trials and demonstrate their suitability for inference of underlying evolutionary conditions. In particular, we demonstrate extraction of clear phylometric signals that differentiate wafer-scale runs with adaptive dynamics enabled versus disabled. Together, these benchmark and validation trials reflect strong potential for highly scalable evolutionary computation that is both efficient and observable. Kernel code implementing the island-model GA supports drop-in customization to support any fixed-length genome content and fitness criteria, allowing it to be leveraged to advance research interests across the community.

Trackable Agent-based Evolution Models at Wafer Scale

Continuing improvements in computing hardware are poised to transform capabilities for in silico modeling of cross-scale phenomena underlying major open questions in evolutionary biology and artificial life, such as transitions in individuality, eco-evolutionary dynamics, and rare evolutionary events. Emerging ML/AI-oriented hardware accelerators, like the 850,000 processor Cerebras Wafer Scale Engine (WSE), hold particular promise. However, practical challenges remain in conducting informative evolution experiments that efficiently utilize these platforms' large processor counts. Here, we focus on the problem of extracting phylogenetic information from agent-based evolution on the WSE platform. This goal drove significant refinements to decentralized in silico phylogenetic tracking, reported here. These improvements yield order-of-magnitude performance improvements. We also present an asynchronous island-based genetic algorithm (GA) framework for WSE hardware. Emulated and on-hardware GA benchmarks with a simple tracking-enabled agent model clock upwards of 1 million generations a minute for population sizes reaching 16 million agents. We validate phylogenetic reconstructions from these trials and demonstrate their suitability for inference of underlying evolutionary conditions. In particular, we demonstrate extraction, from wafer-scale simulation, of clear phylometric signals that differentiate runs with adaptive dynamics enabled versus disabled. Together, these benchmark and validation trials reflect strong potential for highly scalable agent-based evolution simulation that is both efficient and observable. Developed capabilities will bring entirely new classes of previously intractable research questions within reach, benefiting further explorations within the evolutionary biology and artificial life communities across a variety of emerging high-performance computing platforms.