信任，但要验证：严格剖析面向数字演化的尽力而为高性能计算
Trust, but Verify: Rigorously Profiling Best-Effort High-Performance Computing for Digital Evolution
- University of Michigan, Ann Arbor, MI, USA（密歇根大学）
- Michigan State University, East Lansing, MI, USA（密歇根州立大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
该研究开发了测量尽力而为代码运行时行为的框架，通过两个案例验证了面向数字演化的尽力而为高性能计算的可行性，为后确定性HPC范式发展提供了支撑。
中文摘要 AI 辅助
高性能计算（HPC）技术的发展持续大幅提升可用处理能力。在数字演化语境下，这种爆炸式增长为推进多尺度生物现象的假设驱动探索，以及针对难题领域的应用驱动演化优化提供了机遇。一个特定机遇来自新兴的下一代AI/ML硬件加速器平台，例如拥有880000个处理器的Cerebras晶圆级引擎（WSE）。然而，这类硬件受限于设备上的数据存储与移动，且大量设备组件易发生故障，进一步加剧了这一挑战。偏离传统确定性计算范式的尽力而为松弛方法，可帮助应对此类约束，但会使可复现性复杂化，并可能引入人为偏差。我们探究这些问题，开发了一个用于测量尽力而为代码运行时行为的框架，并研究数字演化项目中尽力而为计算的案例。第一个案例研究将尽力而为CPU集群多处理应用于多细胞演化模型，在64个进程下提供92%的扩展效率（2.1倍加速），即使在硬件异常下也表现出稳健的中位数服务质量。第二个案例研究考察基于WSE的模拟，展示了通过稀疏、异步设备到主机采样（可容忍硬件故障）来跟踪时空种群历史的尽力而为策略。总之，在尽力而为松弛的潜在形式与范围内，我们认为数字演化在开发后确定性HPC范式方面具有独特的贡献潜力。
英文摘要
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.
