Collaborative Research: PPoSS: Large: A Full-stack Approach to Declarative Analytics at Scale

协作研究：PPoSS：大型：大规模声明性分析的全栈方法

• 批准号: 2316161
• 负责人: Swarat Chaudhuri
• 金额: $ 45万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316161&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Large; Full

The project investigates full-stack implementation methodologies for expressive programming systems that effectively bridge the gap between human-level specification and high-performance implementation of complex reasoning tasks at scale. Declarative languages permit a programmer to provide high-level rules and declarations that define some sought-after solution as a latent implication to be materialized automatically by the computer. The project's novelties are to scale this vision of high-performance declarative reasoning both to structured, higher-order, and probabilistic formulations and to the next generation of supercomputers and cloud-based clusters. The project's impacts are on application designers and programmers in key application areas, including precision medicine, stochastic modeling, software verification, graph analytics, and security. The project is developing open-source tools, programming languages, and frameworks capable of enabling truly scalable reasoning for users across disciplines.The complexities of next-generation exascale systems pose key challenges: managing increased parallelism, heterogeneity, graphic processing units (GPUs), deep memory hierarchies, and performance tuning across the full software stack. With this increasing complexity and diversity in the hardware configuration of upcoming high-performance computing systems, it becomes difficult to write maintainable and scalable applications by hand. Modern chain-forward reasoning systems are being extended with structured, higher-order data, probabilistic semantics, lattice orderings, recursive aggregation, and first-order theories, posing key implementation challenges - especially in a parallel setting. In this project, the investigators are developing a unified, and tunable, full-stack foundation for highly expressive chain-forward programming to be deployed at scale.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.

该项目研究了表达编程系统的全堆栈实施方法，这些方法有效地弥合了人级规范和高性能实施复杂推理任务之间的差距。声明性语言允许程序员提供高级规则和声明，这些规则和声明将某些受欢迎的解决方案定义为一种潜在的含义，以自动由计算机自动实现。该项目的新颖性是将高性能声明性推理的这种愿景扩展到结构化，高阶和概率的配方，以及下一代超级计算机和基于云的群集的愿景。该项目的影响是对关键应用领域的应用程序设计师和程序员的影响，包括精密医学，随机建模，软件验证，图形分析和安全性。该项目正在开发开源工具，编程语言和框架，能够为用户跨学科提供真正可扩展的推理。下一代Exascale系统的复杂性构成了关键挑战：管理增强的并行性，异质性，图形处理单元（GPU），深入的内存层次结构以及整个软件范围内的性能调节。随着即将到来的高性能计算系统的硬件配置的复杂性和多样性的增加，很难手工编写可维护和可扩展的应用程序。现代的链条推理系统正在扩展到结构化的，高阶的数据，概率语义，晶格有序，递归聚合和一阶理论，提出了关键的实施挑战 - 尤其是在平行环境中。在该项目中，调查人员正在开发一个统一，可调的，全堆栈的基础，用于将高度表达性的链式前提编程进行大规模部署。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来进行评估的。