SHF: Small: Provably Efficient Dynamic Analysis Tools for Task Parallel Computations

多核平台的广泛部署-从个人计算机到移动设备再到超级计算机--使得开发支持编写能够高效地利用多核平台的正确和高效的并行软件变得至关重要。并行编程不再是只由专家执行的行业，也不再是科学计算等特定领域所要求的实践；现在，通用计算需要并行编程。然而，编写有效利用多核的正确并行代码仍然是出了名的困难。该项目旨在开发一套软件工程工具，以帮助编写高效的并行代码。该项目的影响包括简化编写正确和高效的并行代码的任务，从而提高程序员的生产力和现代多核硬件的有效利用。该项目专注于开发任务并行的工具，这是一种被现代并行软件平台广泛采用的编程模型，如OpenMP、英特尔线程构建块以及Cilk和Habanero的各种方言。通过任务并行性，语言结构表达了计算的逻辑并行性，并让底层运行时调度器自动执行任务，如负载平衡和子计算之间的协调。任务并行模型提供了许多好处：它允许程序员专注于实现应用程序的功能，而不必担心调度逻辑；编程模型是与处理器无关的，并且具有定义良好的成本模型；最后，调度器可以为资源使用提供可证明的保证。在这个项目中，研究人员研究和开发算法和软件支持工具，以调试非结构化和不确定的同步原语，分析不确定代码的可扩展性，并对抗任务并行计算中的工作膨胀。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The widespread deployment of multicore platforms --- from personal computers to mobile devices to supercomputers --- has made it critical to develop support for writing correct and efficient parallel software that can efficiently utilize the multicore platforms. Parallel programming is no longer a trade performed by only experts, or a practice required by a specific domain such as scientific computing; it is now required for general-purpose computing. Yet, writing correct parallel code that utilizes multicore efficiently is still notoriously difficult. This project aims to develop a suite of software engineering tools to aid the task of writing efficient parallel code. The project's impacts include simplifying the task of writing correct and efficient parallel code and thereby improving both the programmer's productivity and efficient utilization of modern multicore hardware. The project focuses on developing tools for task parallelism, a programming model widely employed by modern parallel software platforms, such as OpenMP, Intel Threading Building Blocks, and various dialects of Cilk and Habanero. With task parallelism, the language constructs express the logical parallelism of the computation, and let an underlying runtime scheduler automate tasks such as load balancing and coordinations among subcomputations. The task parallel model provides many benefits: it allows the programmer to focus on implementing the functionality of the application without worrying about scheduling logics; the programming model is processor-oblivious and has a well-defined cost model; finally, the scheduler can provide provable guarantees on resource usage. In this project, the investigators study and develop both algorithms and software support for tools to debug unstructured and nondeterministic synchronization primitives, to analyze scalability of nondeterministic code, and to combat work inflation in task parallel computations.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.