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

动态和不规则的应用，如科学仿真框架中的多分辨率自适应数值环境，很难有效地实现，特别是在新兴的复杂和异构的高性能计算平台上。由于缺乏能够表达这类应用程序的合适编程模型，同时又允许工具有效地将应用程序映射到各种硬件上，这进一步加剧了这种情况。这个项目的智力优势在于推进了基于依赖的编程模型、编译器技术和解决这些问题的运行时技术的最新发展。该项目更广泛的意义和重要性在于为不规则可扩展算法的组成和优化奠定了智力基础，重点关注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、便携的不规则应用程序，并培训在这些领域工作的公司和政府的未来雇员。该项目通过使用新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，重新定义了流行的抽象，并将这些应用于非常重要的一类动态，不规则数值计算，例如在上述仿真框架中发现的那些。编程模型中的创新允许程序员将算法规范与如何在具有各种不同调优目标的各种不同平台上有效地映射应用程序的规范分离开来。编译器的创新使以前难以捉摸的不规则应用程序的优化成为可能，而运行时技术使现代、异构和分布式机器上的高效执行成为可能。

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.