CRII: CSR: Enhancing Eventual Data Consistency in Multidimensional Scientific Computing through Lightweight In-Memory Distributed Ledger System.

在科学计算领域，确保数据随时间的一致性对于促进协作和推进研究至关重要，特别是在处理复杂的多维数据时。许多科学研究都涉及相互关联的组件交换关键信息，如气候模型模拟和DNA测序。维护最终一致性的能力允许各个组件独立操作（实现更高的效率），同时确保它们的数据更新最终一致，即使在潜在的中断中也是如此。但是，在管理大量数据时实现这种级别的一致性会带来重大挑战，包括更高的延迟、复杂的数据同步和可伸缩性问题。当前的解决方案不足以为大规模系统提供轻量级的内存最终一致性。该项目旨在通过为极端规模系统量身定制一个健壮的最终一致性模型来弥合这一差距。提出的模型包括两个核心模块——一个致力于改进数据建模策略，以降低复杂性和优化工作负载分布，另一个旨在建立可扩展的内存分布式分类账，以增强缓存和处理效率。拟议的研究有望改善数据管理实践，特别是对于复杂的数据集，从而促进科学研究的进步。这些创新通过在极端规模系统中引入大型复杂数据集管理方面的进步，将使科学界受益。通过将轻量级最终一致性集成到这些系统中，该项目设想了对数据处理技术的重大影响。这个跨学科的计划，跨越分布式数据库、系统和高性能计算，将促进跨不同科学领域的跨学科创新。此外，从这项研究中得到的系统将在推进科学研究和使研究人员和科学家之间更有效地合作方面发挥作用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In the realm of scientific computing, ensuring the consistency of data over time is essential for facilitating collaboration and advancing research, especially when dealing with complex multidimensional data. Numerous scientific endeavors involve interconnected components exchanging critical information, such as climate modeling simulations and DNA sequencing. The ability to maintain eventual consistency allows individual components to operate independently (achieving improved efficiency), yet ensures their data updates eventually align, even amidst potential disruptions. However, achieving this level of consistency when managing vast volumes of data poses significant challenges, including higher latencies, complex data synchronization, and scalability issues. Current solutions are inadequate in providing lightweight in-memory eventual consistency for large-scale systems. This project aims to bridge this gap by crafting a robust eventual consistency model tailored for extreme-scale systems. The proposed model comprises two core modules -- one dedicated to refining data modeling strategies to reduce complexity and optimize workload distribution, and the other aimed at establishing a scalable in-memory distributed ledger for enhanced caching and processing efficiency.The proposed research promises to improve data management practices, particularly for intricate datasets, thereby fostering advancements in scientific research. These innovations stand to benefit the scientific community by introducing advancements in the management of large, complex datasets within extreme-scale systems. Through the integration of lightweight eventual consistency into these systems, the project envisions a substantial impact on data handling techniques. This interdisciplinary initiative, spanning distributed databases, systems, and high-performance computing, will foster cross-disciplinary innovation across diverse scientific domains. Further, the resulting systems derived from this research will play a role in advancing scientific research and enabling more efficient collaboration among researchers and scientists.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.