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.

该项目研究了表达性编程系统的全栈实现方法，该方法有效地缩小了人类水平规范与大规模复杂推理任务的高性能实现之间的差距。声明性语言允许程序员提供高级规则和声明，将一些受欢迎的解决方案定义为由计算机自动实现的潜在含义。该项目的新颖之处在于将高性能声明性推理的愿景扩展到结构化、高阶和概率公式以及下一代超级计算机和基于云的集群。该项目对关键应用领域的应用程序设计人员和程序员产生影响，包括精准医学、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，能够为跨学科的用户提供真正可扩展的推理。下一代百亿亿次系统的复杂性带来了关键挑战：管理增加的并行性、异构性、图形处理单元（GPU）、深层内存层次结构以及整个软件堆栈的性能调整。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不断增加，手工编写可维护和可扩展的应用程序变得困难。现代链式前向推理系统正在通过结构化、高阶数据、概率语义、格排序、递归聚合和一阶理论进行扩展，带来了关键的实施挑战——尤其是在并行环境中。在这个项目中，研究人员正在开发一个统一的、可调节的全栈基础，用于大规模部署的高度表现力的链前向编程。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，被认为值得支持。优点和更广泛的影响审查标准。