SPX: Collaborative Research: Dependence Programming and Optimization of Scalable Irregular Numerical Applications

动态和不规则的应用程序，例如在多分辨率自适应科学模拟数值环境框架中，众所周知很难有效地实现，特别是在新兴的复杂和异质的高性能计算平台上。由于缺乏适当的编程模型来表达这些类型的应用程序，同时允许工具有效地将应用程序映射到各种硬件上，这进一步加剧了这一点。该项目的智力优势在于推动了基于依赖的编程模型、编译器技术和运行时技术的发展，以解决这些问题。该项目的更广泛的意义和重要性在于为非规则可伸缩算法的合成和优化奠定了智力基础，重点是具有挑战性和高度重要的空间树算法。该项目能够设计和实现高性能、可移植的不规则应用程序，以及培训在这些领域工作的公司和政府的未来员工。该项目通过使用新颖的编译器和运行时技术统一和扩展基于并发集合(CNC)依赖的编程模型，并将其应用于一类非常重要的动态、不规则数值计算，如上述模拟框架中的那些，从而重新定义了主流抽象概念。编程模型中的创新允许程序员将算法规范与如何有效地将应用程序映射到具有各种不同调优目标的各种不同平台上的规范分开。编译器的创新使得以前难以捉摸的不规则应用程序的优化成为可能，而运行时技术则能够在现代的、异类的和分布式的机器上高效执行。

Dynamic and irregular applications, such as in the Multiresolution Adaptive Numerical Environment for Scientific Simulation framework, are notoriously hard to implement efficiently, especially on emerging complex and heterogeneous high-performance computing platforms. This is further compounded by the lack of suitable programming models capable of expressing these kinds of applications, while at the same time allowing the tools to efficiently map the applications on a variety of hardware. The intellectual merits of this project are in advancing the state of the art in dependence-based programming models, compiler technologies and runtime techniques that address these issues. The project's broader significance and importance are in laying down the intellectual foundations for the composition and optimization of irregular scalable algorithms, focusing on challenging and highly-significant spatial-tree algorithms. This project enables design and implementation of high-performance, portable irregular applications, as well as training of the future employees of companies and government who work in these domains.This project redefines the prevailing abstractions by unifying and extending the Concurrent Collections (CnC) dependence-based programming model with novel compiler and runtime techniques, and applying these to a very important class of dynamic, irregular numerical computations such as the ones found in the above simulation framework. Innovations in the programming model allow the programmers to separate the specification of the algorithm from a specification of how to efficiently map the application on a variety of different platforms with a variety of different tuning goals. Compiler innovations enable previously elusive optimizations of irregular applications, while runtime techniques enable efficient execution on modern, heterogeneous and distributed machines.