SI2-SSI: Collaborative Research: Sustained Innovation for Linear Algebra Software (SILAS)

SI2-SSI：协作研究：线性代数软件 (SILAS) 的持续创新

• 批准号: 1339797
• 负责人: Julien Langou
• 金额: $ 30万
• 依托单位: University of Colorado at Denver-Downtown Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1339797&HistoricalAwards=false
• 关键词: SI2; SSI; Collaborative; Research; Sustained

As the era of computer architectures dominated by serial processors comes to a close, the convergence of several unprecedented changes in processor design has produced a broad consensus that much of the essential software infrastructure of computational science and engineering is utterly obsolete. Math libraries have historically been in the vanguard of software that must be quickly adapted to such design revolutions because they are the common, low-level software workhorses that do all the most basic mathematical calculations for many different types of applications. The Sustained Innovation for Linear Algebra Software (SILAS) project updates two of the most widely used numerical libraries in the history of Computational Science and Engineering---LAPACK and ScaLAPACK, (abbreviated Sca/LAPACK)---enhancing and hardening them for this ongoing revolution in processor architecture and system design. SILAS creates a layered package of software components, capable of running at every level of the platform deployment pyramid, from the desktop to the largest supercomputers in the world. It achieves three complementary objectives: 1) Wherever possible, SILAS delivers seamless access to the most up-to-date algorithms, numerical implementations, and performance, by way of Sca/LAPACK programming interfaces that are familiar to many computational scientists; 2) Wherever necessary, SILAS makes advanced algorithms, numerical implementations and performance capabilities available through new interface extensions; and 3) SILAS provides a well engineered conduit through which new discoveries at the frontiers of research in these areas can be channeled as quickly as possible to all the application communities that depend on high performance linear algebra. The improvements and innovations included in SILAS derive from a variety of sources. They represent the results (including designs and well tested prototypes) of the PIs' own algorithmic and software research agenda, which has targeted multicore, hybrid and extreme scale system architectures. They are an outcome of extensive and on-going interactions with users, vendors, and the management of large NSF and DOE supercomputing facilities. They flow from cross-disciplinary engagement with other areas of computer science and engineering, anticipating the demands and opportunities of new architectures and programming models. And finally, they come from the enthusiastic participation of the research community in developing and offering enhanced versions of existing Sca/LAPACK codes.The primary impact of SILAS is a direct function of the importance of the Sca/LAPACK libraries to many branches of computational science. The Sca/LAPACK libraries are the community standard for dense linear algebra and have been adopted and/or supported by a large community of users, computing centers, and HPC vendors. Learning to use them is a basic part of the education of a computational scientist or engineer in many fields and at many academic institutions. Application domains where Sca/LAPACK have historically been heavily used include (among a host of other examples) airplane wing design, radar cross-section studies, flow around ships and other off-shore constructions, diffusion of solid bodies in a liquid, noise reduction, and diffusion of light through small particles. Moreover, the list of application partners working with SILAS to enhance and transform these libraries for next generation platforms expands this traditional list to include quantum chemistry, adaptive mesh refinement schemes, computational materials science, geophysical flows, stochastic simulation and database research for "big data". No other numerical library can claim this breadth of integration with the community. Thus, there is every reason to believe that enhancing these libraries with state of the art methods and algorithms and adapting them for new and emerging platforms (reaching up to extreme scale), will have a correspondingly large impact on the research and education community, government laboratories, and private industry.

随着由串行处理器主导的计算机体系结构时代的结束，处理器设计中几个前所未有的变化的汇合产生了一个广泛的共识，即计算科学和工程的许多基本软件基础设施已经完全过时了。数学库在历史上一直是软件的先锋，必须快速适应这种设计革命，因为它们是为许多不同类型的应用程序执行所有最基本数学计算的常见的低级软件主力。线性代数软件的持续创新（SILAS）项目更新了计算科学与工程历史上使用最广泛的两个数字库——LAPACK和ScaLAPACK，（缩写为Sca/LAPACK）——为处理器架构和系统设计的持续革命增强和强化了它们。SILAS创建了一个分层的软件组件包，能够在平台部署金字塔的每个层次上运行，从桌面到世界上最大的超级计算机。它实现了三个互补的目标：1)只要有可能，SILAS通过许多计算科学家熟悉的Sca/LAPACK编程接口，提供对最新算法、数值实现和性能的无缝访问；2)在必要时，SILAS通过新的接口扩展提供先进的算法、数值实现和性能能力；3) SILAS提供了一个精心设计的管道，通过该管道，这些领域研究前沿的新发现可以尽可能快地传递给依赖高性能线性代数的所有应用社区。SILAS所包含的改进和创新来自各种来源。它们代表了pi自己的算法和软件研究议程的结果（包括设计和经过良好测试的原型），目标是多核、混合和极端规模的系统架构。它们是与用户、供应商以及大型NSF和DOE超级计算设施管理人员进行广泛和持续互动的结果。他们从计算机科学和工程其他领域的跨学科参与中流动，预测新的体系结构和编程模型的需求和机会。最后，它们来自于研究社区在开发和提供现有Sca/LAPACK代码的增强版本方面的热情参与。SILAS的主要影响是Sca/LAPACK库对许多计算科学分支的重要性的直接作用。Sca/LAPACK库是密集线性代数的社区标准，已经被大量用户社区、计算中心和HPC供应商采用和/或支持。学习使用它们是许多领域和许多学术机构的计算科学家或工程师教育的基本组成部分。Sca/LAPACK历来被大量使用的应用领域包括（在许多其他例子中）飞机机翼设计、雷达横截面研究、船舶和其他近海建筑周围的流动、固体在液体中的扩散、降噪以及光通过小颗粒的扩散。此外，与SILAS合作，为下一代平台增强和改造这些库的应用伙伴列表扩展了这一传统列表，包括量子化学，自适应网格细化方案，计算材料科学，地球物理流，随机模拟和“大数据”数据库研究。没有其他数字库可以声称与社区集成的广度。因此，我们有充分的理由相信，用最先进的方法和算法来增强这些库，并使它们适应新的和新兴的平台（达到极端的规模），将对研究和教育界、政府实验室和私营企业产生相应的巨大影响。