CAREER: SMART: Scalable Adaptive Runtime Management Algorithms and Toolkit for Large-Scale Dynamic Scientific Applications

新兴的通用高性能计算和存储资源为解决科学和工程中的重大挑战问题提供了巨大的机遇。然而，由于缺乏应用意识，大多数系统没有足够的支持来满足一些超大规模动态科学应用的特殊要求。这些计算和数据密集型应用的目的是模拟和研究科学和工程中的高动态、有时急剧变化的现象，如相互作用的黑洞、全球和区域高分辨率天气预报、燃烧和爆炸模拟等。此外，对于科学家/工程师来说，从零开始开发大规模并行和分布式科学应用是具有挑战性和耗时的。该项目填补了这一空白，目标是(1)创建一个具有友好编程模型的可扩展的自适应运行时管理算法、库和工具包(称为SMART)的集成框架，以便科学家可以编写顺序程序来实现自动并行和高性能和吞吐量；(2)设计一套应用感知的自适应算法，以全面解决具有数千个处理器的系统(如集群、网格和云)中的计算、通信、数据和能源管理方面的各种问题；以及(3)通过额外的模拟和可视化工具实现高影响的真实世界大规模科学应用。

Emerging general-purpose high performance computing and storage resources provide great opportunities to tackle grand challenge problems in science and engineering. However, most systems do not have adequate support to meet special requirements of a number of ultra-scale dynamic scientific applications due to lack of application-awareness. These computation and data intensive applications are aimed to model and investigate highly dynamic and sometimes drastically changing phenomena in science and engineering, such as interacting black holes, global and regional high-resolution weather forecasting, combustion and detonation simulation, and many others. Furthermore, it is challenging and time-consuming for scientists/engineers to develop their large-scale parallel and distributed scientific applications from scratch.This project fills the gap and aims to (1) create an integrated framework of scalable adaptive runtime management algorithms, libraries, and toolkit (called SMART) with friendly programming models so that scientists can write sequential programs to achieve automatic parallelism and high performance and throughput; (2) design a suite of application-aware adaptive algorithms to holistically address various issues in computation, communication, data, and energy management in systems with thousands of processors (such as clusters, grids, and clouds); and (3) enable high-impact real-world large-scale scientific applications with additional tools for simulation and visualization.