CAREER: Scalable Algorithms for Extreme Computing on Heterogeneous Hardware, with Applications in Fluids and Biology

职业：异构硬件上极限计算的可扩展算法，在流体和生物学中的应用

• 批准号: 1149784
• 负责人: Lorena Barba
• 金额: $ 55.06万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-03-01 至 2014-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149784&HistoricalAwards=false
• 关键词: CAREER; Scalable; Algorithms; Extreme; Computing

There is currently a world-wide quest to achieve exascale computing by the end of the decade, with vigorous efforts in the US as well as China and Japan, in particular. In the US, the President's Strategy for American Innovation (2009) explicitly lists among its goals to dramatically increase our simulations capacity via an exascale computer. The challenges to achieve this goal are unprecedented: power constraints, microchip fabrication reaching physical limits, the growing imbalance between compute capacity and interconnect bandwidth, and the ever increasing number of cores in a system.Among the matters of highest priority are development of scalable algorithms that can exploit the enormous parallelism of new systems, and educating the next generation of computational scientists. The first of these is at the center of the scientific part of this CAREER project. A potentially transformative combination is emerging where a class of hierarchical algorithms, offering ideal scaling linear with problem size, maps with excellent performance to many-core hardware (such as GPUs). Algorithmic improvements will be undertaken, such as combining elements of treecodes and fast multipole methods, communication and synchronization avoidance, dynamic error control and auto-tuning of the computation. The research program is vertically integrated across disciplines, including applications at extreme scales in fluid dynamics and biological systems.This project will produce highly scalable scientific software, reformulating the algorithms to achieve maximum performance in many-core hardware. Disseminated and curated via the open-source model, the computational infrastructure delivered will offer maximum impact, beyond the application areas of focus. Community software that is able to scale to millions of processors will be crucial to exploit post-petascale systems, and this project aims to provide that. The educational part of this program, on the other hand, builds on the PI's track record of success both in the use of technology to support learning, and in catalyzing international collaboration and outreach. The program includes enhancing educational environments using technology for both curricular instruction and contributing to the nation's science literacy (via open courseware). The goals of fostering the next generation of computational scientists will be pursued via extra-mural advanced training events, and online learning media.

目前，世界范围内都在寻求在本世纪末实现百亿亿次计算，特别是美国、中国和日本都在积极努力。在美国，总统的美国创新战略（2009）明确列出了通过百亿亿级计算机大幅提高我们的模拟能力的目标。实现这一目标面临着前所未有的挑战：功率限制、微芯片制造达到物理极限、计算能力和互连带宽之间日益不平衡，以及系统中内核数量的不断增加。其中最重要的是可扩展算法的开发可以利用新系统的巨大并行性，并教育下一代计算科学家。第一个是这个职业项目科学部分的核心。一种潜在的变革性组合正在出现，其中一类分层算法提供了与问题大小成线性关系的理想缩放，并以优异的性能映射到多核硬件（例如 GPU）。将进行算法改进，例如结合树代码和快速多极子方法的元素、通信和同步避免、动态错误控制和计算的自动调整。该研究项目跨学科垂直整合，包括流体动力学和生物系统中极端规模的应用。该项目将生产高度可扩展的科学软件，重新制定算法以在多核硬件中实现最大性能。通过开源模型传播和管理，所提供的计算基础设施将提供超出重点应用领域的最大影响。能够扩展到数百万个处理器的社区软件对于开发后千万亿级系统至关重要，该项目旨在提供这一点。另一方面，该计划的教育部分建立在 PI 在使用技术支持学习以及促进国际合作和推广方面的成功记录之上。该计划包括利用技术进行课程教学和提高国家科学素养（通过开放课件）来改善教育环境。培养下一代计算科学家的目标将通过校外高级培训活动和在线学习媒体来实现。