SHF: Medium: Collaborative Research: Chorus: Dynamic Isolation in Shared-Memory Parallelism

SHF：媒介：协作研究：Chorus：共享内存并行中的动态隔离

• 批准号: 0964520
• 负责人: Vivek Sarkar
• 金额: $ 51.35万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0964520&HistoricalAwards=false
• 关键词: SHF; Medium; Collaborative; Research; Chorus

Expressing parallel computations over complex shared-memory data structures has always been a vexing issue in parallel programming. On one hand, popular task-based programming models do not provide first-class abstractions for isolation and locality. On the other, Actor-based programming naturally captures locality but is unsuitable for computations on large shared data structures. The present project partially bridges the gap between these two styles of parallelism through Chorus, a new programming model for parallel computations over unstructured, continually changing shared-memory data structures. The key abstraction of Chorus is an object assembly: a local, isolated region in the heap equipped with a thread of control. Assemblies can imperatively modify themselves, merge with other assemblies, and split into smaller assemblies?through these operations over assemblies, Chorus captures unpredictable, dynamic changes to parallelism. This makes Chorus an ideal programming model for many irregular data-parallel applications (e.g., meshing, clustering), which exhibit fine-grained data-parallelism in typical executions but no parallelism in the worst case, and whose parallelization remains an open and difficult challenge. The predicted outcomes of the project include new insights into the semantic foundations of Chorus and new language constructs integrating Chorus with existing abstractions for asynchronous task creation, directed synchronization, and locality. On the system-building end, the project will integrate Chorus with the Habanero Java parallel programming language, and implement a compiler and runtime for the resultant language. The performance and programmability of this language will be thoroughly evaluated using benchmarks largely consisting of emerging irregular workloads.

在复杂的共享内存数据结构上表达并行计算在并行编程中一直是一个令人沮丧的问题。一方面，流行的基于任务的编程模型不能为隔离和区域提供一流的抽象。另一方面，基于演员的编程自然会捕获本地性，但不适合对大型共享数据结构进行计算。本项目部分通过合唱来部分弥合了这两种并行性之间的差距，这是一种新的编程模型，用于对非结构化的，不断变化的共享记忆数据结构进行并行计算。合唱的关键抽象是一个对象组件：堆中的局部隔离区域，配备了一个控制线。组件可以迫切地修改自己，与其他组件合并，并分成较小的组件？通过对组件的这些操作，合唱会捕获对并行性的不可预测的动态变化。这使得合唱成为许多不规则数据并行应用（例如，网格划分，聚类）的理想编程模型，它们在典型的执行中表现出细粒度的数据并行性，但在最坏的情况下没有并行性，其并行化仍然是一个开放而困难的挑战。该项目的预测结果包括对合唱的语义基础和新语言构造的新见解，将合唱与现有的抽象集成为异步任务创建，定向同步和局部性。在系统构建端，该项目将将合唱与Habanero Java并行编程语言集成，并实施由此产生的语言的编译器和运行时。该语言的性能和可编程性将使用主要包括不规则的工作负载组成的基准进行彻底评估。