XPS: FULL: Collaborative Research: Parallel and Distributed Circuit Programming for Structured Prediction

XPS：完整：协作研究：用于结构化预测的并行和分布式电路编程

• 批准号: 1629459
• 负责人: Vivek Sarkar
• 金额: $ 41万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629459&HistoricalAwards=false
• 关键词: XPS; FULL; Collaborative; Research; Parallel

This project develops a system for "circuit programming," which allows a programmer to focus on the high-level solution to a problem rather than on the details of how the computation is organized. Circuit programming consists of writing rules that describe how data items depend on one another. The intellectual merits lie in the design of a new programming language for specifying these rules, along with the algorithms whereby the computer automatically finds efficient strategies for managing the necessary computations on available parallel hardware. The project's broader significance and importance lie in its potential to streamline work in areas such as artificial intelligence and machine learning. With the growing complexity of systems in these areas and their need to process big data in depth, research and teaching typically get bogged down in programming details, especially for parallel platforms; this project aims to delegate those details to automatic methods.The research develops a programming system for Dyna, a circuit programming language that enables concise specification of large function graphs that may be cyclic and/or infinite. Dyna employs (1) a pattern-matching notation that augments pure Prolog with evaluation and aggregation and (2) an object-like mechanism for dynamically defining new sub-circuits as modifications of old ones. This project is building an adaptive system that can mix forward and backward chaining to seek a fixpoint of the circuit and to update this fixpoint as the inputs change. The system will perform compile-time and runtime analysis of the Dyna program and will map it to Habanero, a system for scheduling parallel computations on multicore processors, with extensions for task priorities, task cancellation, GPU execution, and distributed execution.

该项目开发了一个用于“电路编程”的系统，该系统允许程序员专注于问题的高级解决方案，而不是将计算方式的细节介绍。电路编程包括编写规则，这些规则描述了数据项如何相互依赖。智力优点在于设计新的编程语言的设计，用于指定这些规则，以及计算机自动找到有效的策略来管理可用并行硬件的必要计算的有效策略。 该项目的更广泛的意义和重要性在于其在人工智能和机器学习等领域中精简工作的潜力。 随着这些领域的系统的复杂性日益严重，并且需要深入处理大数据，研究和教学通常会陷入编程细节中，尤其是对于并行平台而言；该项目旨在将这些详细信息委派给自动方法。该研究开发了一种针对Dyna的编程系统，Dyna是一种巡回编程语言，可以简要规范可能是循环和/或无限的大函数图。 DYNA采用（1）一种图案匹配符号，通过评估和聚集来增强纯序，以及（2）一种类似对象的机制，用于动态定义新的子电路作为旧的修饰。 该项目正在建立一个自适应系统，该系统可以将前向和向后链式混合以寻求电路的固定位置，并随着输入的变化而更新此fixpoint。 该系统将对DYNA程序执行编译时间和运行时分析，并将其映射到Habanero，该系统用于在多项处理器上安排并行计算的系统，并扩展任务优先级，任务取消，GPU执行和分布式执行。