Collaborative Research: SI2-SSI: Scalable Hierarchical Algorithms for Extreme Computing (SHARE)

合作研究：SI2-SSI：用于极限计算的可扩展分层算法（SHARE）

• 批准号: 1060067
• 负责人: Lincoln Greenhill
• 金额: $ 23.08万
• 依托单位: Harvard University
• 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: 2010-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1060067&HistoricalAwards=false
• 关键词: Collaborative; Research; SI2; SSI; Scalable

This award supports the development of software tools for advanced algorithms on a cluster of high performance graphics processing units(GPUs). The initial goal of this library will focus on a single driver application, the fundamental numerical study of Nuclear Forces (Lattice Quantum Chromodynamics: LQCD), and a second target application, the numerical simulation of the exciting nano-technology of graphene. These are both multi-fermion problems well suited to solution via multi-scale algorithms on many-core architectures. This pilot project draws on experience gained by the small team at Boston University and Harvard in developing Dirac solvers for lattice field theory. Two building blocks from prior research are (1) the construction of an adaptive multigrid (MG) solver for the Wilson Dirac operator of LQCD, which demonstrates a 20x speedup compared to the best Krylov solvers in production code and (2) a highly optimized Krylov solver for the same operator on GPUs (but without multigrid), realizing a 10x improvement in price/performance over traditional clusters. The library will unite these feature and generalize their domain of applicability. As an example of the broader impact, it is estimated that combining these two technologies (MG algorithms and GPU architectures) will yield a 100-fold improvement in price/performance for the most compute-intensive component of LQCD simulations. Such an advance would be truly transformative, making an immediate impact in nuclear and particle physics. At the same time, it will serve as a prototype of the more generic problem of mapping hierarchical algorithms onto heterogeneous architectures, a challenge of paramount importance on the path to the exascale. The software library will be designed to bring similar benefits to graphene technology and to evolve to accommodate additional target application and additional domain decomposition algorithm to mitigate the communication bottleneck of Exascale designs. The award will provide partial support for two postdoctoral scholars who play essential roles in this project.

该奖项支持在高性能图形处理单元（GPU）集群上开发高级算法的软件工具。 该库的最初目标将集中在一个单一的驱动程序应用程序，核力的基本数值研究（晶格量子色动力学：LQCD），以及第二个目标应用程序，石墨烯令人兴奋的纳米技术的数值模拟。 这些都是多费米子问题，非常适合通过多尺度算法在众核架构上解决。 这个试点项目借鉴了波士顿大学和哈佛的小团队在为格点场论开发狄拉克解算器方面所获得的经验。 来自先前研究的两个构建块是（1）为LQCD的Wilson Dirac算子构建自适应多重网格（MG）求解器，与生产代码中的最佳Krylov求解器相比，该求解器的速度提高了20倍;（2）在GPU上为相同算子（但没有多重网格）构建高度优化的Krylov求解器，与传统集群相比，实现了10倍的性价比提升。 该库将统一这些特性并概括其适用范围。 作为更广泛影响的一个例子，据估计，结合这两种技术（MG算法和GPU架构）将使LQCD模拟中计算密集度最高的组件的性价比提高100倍。 这样的进步将是真正的变革性的，对核物理和粒子物理产生直接影响。 与此同时，它将作为一个原型的更一般的问题，映射到异构体系结构的层次算法，一个挑战的最重要的路径上的exascale。 软件库将被设计为为石墨烯技术带来类似的好处，并发展以适应额外的目标应用和额外的区域分解算法，以减轻Exascale设计的通信瓶颈。 该奖项将为在该项目中发挥重要作用的两名博士后学者提供部分支持。