SHF: AF: Large: Collaborative Research: Parallelism without Concurrency

并行机（从多核到超级计算机）的广泛部署使得开发简单的编程方法变得至关重要。 通过开发编程模型来支持无并发的并行性，即不存在由通信进程的相对和不确定定时引起的程序逻辑的不确定性，在简化并行编程方面取得了重大进展。 然而，实践中大多数并行程序都是并发的，因此是不确定的，导致代码只能由专家编程和理解。 该研究项目旨在了解如何通过开发支持确定性并行计算的软件技术，使绝大多数程序员更容易使用并行计算机。该项目从编程语言学、软件系统、算法分析和绝对性能的关键角度全面看待问题。 它承认并行编程无法在每个抽象级别上完全确定的现实。 它追求处理并发性的三个关键策略：封装并发性，使其被适当抽象级别的分层抽象所隐藏，通过重组程序以采用确定性方法来避免并发性，以及在完全封装或完全避免并发性不切实际时管理并发性。正在研究的具体技术包括可交换构建块、确定性非关联减速器、确定性流水线并行性、确定性接口以及用于检测不变竞争的广义竞争检测。 该项目正在开发集成到多核软件平台中的开源库、工具和运行时扩展，以及用于比较方法的基于问题的基准套件。

The widespread deployment of parallel machines --- from multicores to supercomputers --- has made it critical to develop simple approaches to programming them. Significant progress has been made in simplifying parallel programming by developing programming models to support parallelism without concurrency, that is, without the nondeterminacies in the logic of programs caused by the relative and nondeterministic timing of communicating processes. Yet most parallel programs in practice are concurrent, and hence, nondeterministic, leading to code that can only be programmed and understood by experts. This research project aims to understand how parallel computers can be made easier to use by the vast majority of programmers by developing software technology that enables deterministic parallel computing.The project takes a holistic view of the problem from the key perspectives of programming linguistics, software systems, algorithmic analysis, and absolute performance. It acknowledges the reality that parallel programming cannot be fully deterministic at every level of abstraction. It is pursuing three key strategies for dealing with concurrency: encapsulating concurrency so that it is hidden by layered abstractions at appropriate abstraction levels, avoiding concurrency by restructuring programs to employ deterministic approaches, and managing concurrency when it is impractical to either encapsulate or avoid concurrency completely. Among the specific techniques being studied are commutative building blocks, deterministic nonassociative reducers, deterministic pipelined parallelism, deterministic interfaces, and generalized race detection for detecting invariant races. The project is developing open-source libraries, tools, and runtime extensions integrated into a multicore-software platform, as well as a problem-based benchmark suite to compare approaches.