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

目前，全球都在努力在2020年前实现百亿亿次计算，美国、中国和日本的努力力度尤其大。在美国，总统的美国创新战略（2009）明确列出了通过百亿亿次计算机大幅提高我们的模拟能力的目标之一。实现这一目标的挑战是前所未有的：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，以及系统中核心数量的不断增加。最重要的事情是开发可扩展的算法，以利用新系统的巨大并行性，并培养下一代计算科学家。第一个是这个CAREER项目的科学部分的核心。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相关的理想线性缩放，并以优异的性能映射到多核硬件（如gpu）。将进行算法改进，如结合三码元素和快速多极方法，通信和同步避免，动态误差控制和计算的自动调谐。该研究项目是跨学科的垂直整合，包括在流体动力学和生物系统的极端尺度的应用。该项目将产生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，交付的计算基础设施将提供最大的影响，超出应用领域的关注。能够扩展到数百万处理器的社区软件对于开发千兆级系统至关重要，而本项目旨在提供这一点。另一方面，这个项目的教育部分建立在PI在利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改善教育环境，既用于课程教学，又有助于提高国家的科学素养（通过开放课件）。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。

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.