arXiv subjects

Santiago Rodriguez Papa

Publications and source records attributed to Santiago Rodriguez Papa.

6 recordsLinked to original sources

arXiv2026

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

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 ($2.1\times$ 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.

cs.NE↗

arXiv2024

Case Study of Novelty, Complexity, and Adaptation in a Multicellular System

Continuing generation of novelty, complexity, and adaptation are well-established as core aspects of open-ended evolution. However, it has yet to be firmly established to what extent these phenomena are coupled and by what means they interact. In this work, we track the co-evolution of novelty, complexity, and adaptation in a case study from the DISHTINY simulation system, which is designed to study the evolution of digital multicellularity. In this case study, we describe ten qualitatively distinct multicellular morphologies, several of which exhibit asymmetrical growth and distinct life stages. We contextualize the evolutionary history of these morphologies with measurements of complexity and adaptation. Our case study suggests a loose -- sometimes divergent -- relationship can exist among novelty, complexity, and adaptation.

cs.NE↗

arXiv2024

Analysis of Phylogeny Tracking Algorithms for Serial and Multiprocess Applications

Since the advent of modern bioinformatics, the challenging, multifaceted problem of reconstructing phylogenetic history from biological sequences has hatched perennial statistical and algorithmic innovation. Studies of the phylogenetic dynamics of digital, agent-based evolutionary models motivate a peculiar converse question: how to best engineer tracking to facilitate fast, accurate, and memory-efficient lineage reconstructions? Here, we formally describe procedures for phylogenetic analysis in both serial and distributed computing scenarios. With respect to the former, we demonstrate reference-counting-based pruning of extinct lineages. For the latter, we introduce a trie-based phylogenetic reconstruction approach for "hereditary stratigraphy" genome annotations. This process allows phylogenetic relationships between genomes to be inferred by comparing their similarities, akin to reconstruction of natural history from biological DNA sequences. Phylogenetic analysis capabilities significantly advance distributed agent-based simulations as a tool for evolutionary research, and also benefit application-oriented evolutionary computing. Such tracing could extend also to other digital artifacts that proliferate through replication, like digital media and computer viruses.

cs.DS↗

arXiv2024

Algorithms for Efficient, Compact Online Data Stream Curation

Data stream algorithms tackle operations on high-volume sequences of read-once data items. Data stream scenarios include inherently real-time systems like sensor networks and financial markets. They also arise in purely-computational scenarios like ordered traversal of big data or long-running iterative simulations. In this work, we develop methods to maintain running archives of stream data that are temporally representative, a task we call "stream curation." Our approach contributes to rich existing literature on data stream binning, which we extend by providing stateless (i.e., non-iterative) curation schemes that enable key optimizations to trim archive storage overhead and streamline processing of incoming observations. We also broaden support to cover new trade-offs between curated archive size and temporal coverage. We present a suite of five stream curation algorithms that span $\mathcal{O}(n)$, $\mathcal{O}(\log n)$, and $\mathcal{O}(1)$ orders of growth for retained data items. Within each order of growth, algorithms are provided to maintain even coverage across history or bias coverage toward more recent time points. More broadly, memory-efficient stream curation can boost the data stream mining capabilities of low-grade hardware in roles such as sensor nodes and data logging devices.

cs.DS↗

arXiv2021

SignalGP-Lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications

Event-driven genetic programming representations have been shown to outperform traditional imperative representations on interaction-intensive problems. The event-driven approach organizes genome content into modules that are triggered in response to environmental signals, simplifying simulation design and implementation. Existing work developing event-driven genetic programming methodology has largely used the SignalGP library, which caters to traditional program synthesis applications. The SignalGP-Lite library enables larger-scale artificial life experiments with streamlined agents by reducing control flow overhead and trading run-time flexibility for better performance due to compile-time configuration. Here, we report benchmarking experiments that show an 8x to 30x speedup. We also report solution quality equivalent to SignalGP on two benchmark problems originally developed to test the ability of evolved programs to respond to a large number of signals and to modulate signal response based on context.

cs.NE↗

arXiv2021

Conduit: A C++ Library for Best-effort High Performance Computing

Developing software to effectively take advantage of growth in parallel and distributed processing capacity poses significant challenges. Traditional programming techniques allow a user to assume that execution, message passing, and memory are always kept synchronized. However, maintaining this consistency becomes increasingly costly at scale. One proposed strategy is "best-effort computing", which relaxes synchronization and hardware reliability requirements, accepting nondeterminism in exchange for efficiency. Although many programming languages and frameworks aim to facilitate software development for high performance applications, existing tools do not directly provide a prepackaged best-effort interface. The Conduit C++ Library aims to provide such an interface for convenient implementation of software that uses best-effort inter-thread and inter-process communication. Here, we describe the motivation, objectives, design, and implementation of the library. Benchmarks on a communication-intensive graph coloring problem and a compute-intensive digital evolution simulation show that Conduit's best-effort model can improve scaling efficiency and solution quality, particularly in a distributed, multi-node context.

cs.DC↗