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

职业：SMART：用于大规模动态科学应用的可扩展自适应运行时管理算法和工具包

基本信息

批准号：
1128805
负责人：
Xiaolin Li
金额：
$ 40.65万
依托单位：
University of Florida
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-01 至 2017-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1128805&HistoricalAwards=false
关键词：
CAREER SMART Scalable Adaptive Runtime

项目摘要

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.

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会。然而，大多数系统没有足够的支持，以满足一些超大规模的动态科学应用的特殊要求，由于缺乏应用意识。这些计算和数据密集型应用旨在模拟和研究科学和工程中高度动态和有时急剧变化的现象，例如相互作用的黑洞，全球和区域高分辨率天气预报，燃烧和爆炸模拟等。此外，科学家/工程师从头开始开发大规模并行和分布式科学应用程序是具有挑战性和耗时的，本项目填补了差距，旨在（1）创建一个集成的框架，可扩展的自适应运行时管理算法，库，和工具包（称为SMART）具有友好的编程模型，以便科学家可以编写顺序程序，以实现自动并行性以及高性能和吞吐量;（2）设计一套应用感知自适应算法，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，数据和能源管理中的各种问题;（3）通过额外的仿真和可视化工具实现高影响力的现实世界大规模科学应用。