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

这个项目开发了一个“电路编程”系统，它允许程序员专注于问题的高级解决方案，而不是如何组织计算的细节。电路编程包括编写描述数据项如何相互依赖的规则。智力上的优点在于设计了一种新的编程语言来指定这些规则，以及计算机自动找到有效策略来管理可用并行硬件上必要的计算的算法。该项目更广泛的意义和重要性在于，它有可能简化人工智能和机器学习等领域的工作。随着这些领域系统的日益复杂，以及它们对深度处理大数据的需求，研究和教学通常会陷入编程细节的困境，尤其是在并行平台上；本项目旨在将这些细节委托给自动方法。该研究为Dyna开发了一个编程系统，Dyna是一种电路编程语言，可以对可能是循环和/或无限的大型函数图进行简洁的说明。Dyna采用(1)一个模式匹配符号，它用求值和聚合增强了纯Prolog；(2)一个类对象机制，它动态地将新的子电路定义为旧子电路的修改。这个项目是建立一个自适应系统，它可以混合向前和向后链来寻找电路的固定点，并随着输入的变化更新这个固定点。该系统将执行Dyna程序的编译时和运行时分析，并将其映射到Habanero，这是一个用于在多核处理器上调度并行计算的系统，具有任务优先级，任务取消，GPU执行和分布式执行的扩展。该奖项反映了美国国家科学基金会的法定使命，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。

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.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.