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: 2217037
• 负责人: Kristopher Micinski
• 金额: $ 8.38万
• 依托单位: Syracuse University
• 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=2217037&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.

声明性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，在其实现中自动执行计算机制，这种表达性编程系统为非专家提供了立即扩展其分析任务的机会。大规模数据集，利用基于人工智能的编程。该项目的新颖之处在于一系列技术，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。该项目的影响集中在允许非专家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。在语言上，该项目的方法基于 Datalog 的关键语义扩展，以支持结构化归纳数据的索引。虽然当前现有的 Datalog 引擎（例如 Souffle）支持代数数据，但该项目的新颖方法还为所有此类 ADT 实现了索引，从而实现了数量级代数数据查询运行时的算法改进在操作上，该项目提出了基于并行关系代数的最先进的实施策略，这使得现成的数据并行性能够通过快速扩展到多核集群和超级计算机。 MPI。该项目旨在整合这些技术以扩展关键应用程序（包括程序分析和安全审计），并通过该项目对这些技术的独特综合来展示它们在大型数据集上的应用。该奖项反映了通过使用基金会的智力价值和更广泛的影响审查标准进行评估，NSF 的法定使命被认为值得支持。