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

协作研究：PPoSS：大规模声明性分析的全栈方法

• 批准号: 2217036
• 负责人: Thomas Gilray
• 金额: $ 16.62万
• 依托单位: University of Alabama at Birmingham
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217036&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Full; stack

Declarative programming languages permit users to define a problem's rules and goals, and the structure of a valid solution, automating the mechanics of computation within their implementations. Such expressive programming systems provide an opportunity for non-experts to immediately scale their analytic tasks to massive datasets, leveraging AI-based programming. The project's novelties are a set of techniques, integrating novel optimizations across the full computation stack, that deliver orders-of-magnitude scalability enhancements to declarative programming. The project's impacts are centered on permitting non-specialists to scale sophisticated deductive inference algorithms to the next generation of cloud-based clusters and supercomputers.Linguistically, the project's approach is based on a key semantic extension to Datalog to support indexing for structured inductive data. While algebraic data is supported in currently existing Datalog engines (e.g., Souffle), the project’s novel approach also materializes indices for all such ADTs, enabling orders-of-magnitude algorithmic improvements in runtimes of queries over algebraic data. Operationally, the project advances state-of-the-art implementation strategies based on parallel relational algebra, which enables off-the-shelf data parallelism that rapidly scales to many-core clusters and supercomputers via MPI. The project seeks to integrate each of these technologies to scale key applications---including program analysis and security auditing---and demonstrate their application to large datasets enabled via the project’s unique synthesis of these technologies.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.

声明式编程语言允许用户定义问题的规则和目标，以及有效解决方案的结构，在其实现中自动化计算机制。这种表达性编程系统为非专家提供了一个机会，可以利用基于AI的编程，立即将其分析任务扩展到大规模数据集。该项目的新颖之处是一组技术，在整个计算堆栈中集成了新颖的优化，为声明式编程提供了数量级的可扩展性增强。该项目的影响主要集中在允许非专业人员将复杂的演绎推理算法扩展到下一代基于云的集群和超级计算机上。从语言上讲，该项目的方法基于对Datatron的关键语义扩展，以支持结构化归纳数据的索引。虽然在当前现有的数据库引擎中支持代数数据（例如，Souffle），该项目的新颖方法还实现了所有此类ADT的索引，从而在代数数据查询的运行时实现了数量级的算法改进。在操作上，该项目推进了基于并行关系代数的最先进的实现策略，这使得现成的数据并行性能够通过MPI快速扩展到众核集群和超级计算机。该项目旨在整合这些技术中的每一项，以扩展关键应用程序--包括程序分析和安全审计--并通过该项目独特的这些技术综合展示其在大型数据集上的应用。该奖项反映了NSF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。

项目成果

Towards Iterative Relational Algebra on the GPU

• 发表时间: 2023
• 作者: Ahmedur Rahman Shovon;Thomas Gilray;Kristopher K. Micinski;Sidharth Kumar

A Visual Guide to MPI All-to-all

MPI 全面可视化指南

• DOI: 10.1109/hipcw57629.2022.00008
• 发表时间: 2022
• 期刊: IEEE
• 作者: Netterville, Nick;Fan, Ke;Kumar, Sidharth;Gilray, Thomas
• 通讯作者: Gilray, Thomas