Collaborative Research: Frameworks: Basic ALgebra LIbraries for Sustainable Technology with Interdisciplinary Collaboration (BALLISTIC)

协作研究：框架：跨学科协作可持续技术的基本代数库（BALLISTIC）

• 批准号: 2004541
• 负责人: Piotr Luszczek
• 金额: $ 122.33万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2004541&HistoricalAwards=false
• 关键词: Collaborative; Research; Frameworks; Basic; ALgebra

Scientific software libraries have long provided a large and growing resource for high-quality, reusable software components upon which applications from science and engineering can be rapidly constructed — with improved robustness, portability, and sustainability. For this, a team of researchers from four collaborating organizations proposed to develop BALLISTIC (Basic ALgebra Libraries for Sustainable Technology with Interdisciplinary Collaboration). The BALLISTIC project, through the leading-edge research it channels into its software deliverables, will lead to the introduction of tools that will simplify the transition to the next generation of extreme-scale computer architectures. The main impact of the project will be to develop, push, and deploy software into the scientific community to make it competitive on a world-wide scale and to contribute to standardization efforts in the area. BALLISTIC has the potential to become the community standard for dense linear algebra and be adopted and/or supported by a large community of users, computing centers, and High-Performance Computing (HPC) vendors. Learning to use numerical libraries is a basic part of the education of a computational scientist or engineer in many fields and at many academic institutions. BALLISTIC will have a correspondingly large impact on the research and education community, government laboratories, and private industry and support national efforts to build a workforce capable of employing state of the art tools in pursuit of science and engineering discoveries.The goal of BALLISTIC is to create a layered package of software components that is capable of running at every level of the platform deployment pyramid and achieves three complementary objectives: (1) deliver seamless access to the most up-to-date algorithms, numerics, and performance via familiar Sca/LAPACK interfaces, wherever possible; (2) make advanced algorithms, numerics, and performance capabilities available through new interface extensions, wherever necessary; and (3) provide 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 applications from science and engineering communities that depend on high-performance linear algebra libraries.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

长期以来，科学软件库为高质量、可重用的软件组件提供了大量且不断增长的资源，科学和工程应用程序可以在这些组件上快速构建，并具有更好的鲁棒性、可移植性和可持续性。为此，来自四个合作组织的研究人员提出开发BALLISTIC（跨学科合作的可持续技术基础代数库）。BALLISTIC项目通过将前沿研究引入其软件交付成果，将导致引入工具，简化向下一代极端规模计算机架构的过渡。该项目的主要影响将是开发，推动和部署软件到科学界，使其在世界范围内具有竞争力，并为该领域的标准化工作做出贡献。BALLISTIC有可能成为稠密线性代数的社区标准，并被大型用户社区、计算中心和高性能计算（HPC）供应商采用和/或支持。学习使用数字库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部分。BALLISTIC将对研究和教育界，政府实验室，BALLISTIC的目标是创建一个分层的软件组件包，能够在平台部署金字塔的每一个级别上运行，并实现三个目标：互补目标：（1）尽可能通过熟悉的Sca/LAPACK接口提供对最新算法、数值和性能的无缝访问;（2）必要时通过新的接口扩展提供高级算法、数值和性能功能;及（3）提供一个良好的-工程管道，通过该管道，这些领域研究前沿的新发现可以尽快被引导到科学和工程界的所有应用程序，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Replacing Pivoting in Distributed Gaussian Elimination with Randomized Techniques

• DOI: 10.1109/scala51936.2020.00010
• 发表时间: 2020-11
• 期刊: 2020 IEEE/ACM 11th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA)
• 作者: Neil Lindquist;P. Luszczek;J. Dongarra

Improving the Performance of the GMRES Method using Mixed-Precision Techniques

使用混合精度技术提高 GMRES 方法的性能

• 发表时间: 2020
• 期刊: Smoky Mountains Computational Sciences & Engineering Conference (SMC2020
• 作者: Lindquist, N.;Luszczek, P.;Dongarra, J.
• 通讯作者: Dongarra, J.

Investigating the Benefit of FP16-Enabled Mixed-Precision Solvers for Symmetric Positive Definite Matrices Using GPUs

• DOI: 10.1007/978-3-030-50417-5_18
• 发表时间: 2020-06-15
• 期刊: Computational Science – ICCS 2020
• 作者: Abdelfattah A;Tomov S;Dongarra J
• 通讯作者: Dongarra J

Accelerating Restarted GMRES With Mixed Precision Arithmetic

• DOI: 10.1109/tpds.2021.3090757
• 发表时间: 2021-06
• 期刊: IEEE Transactions on Parallel and Distributed Systems
• 影响因子: 5.3
• 作者: Neil Lindquist;P. Luszczek;J. Dongarra

Task-graph scheduling extensions for efficient synchronization and communication

• DOI: 10.1145/3447818.3461616
• 发表时间: 2020-11
• 期刊: Proceedings of the 35th ACM International Conference on Supercomputing
• 作者: Seonmyeong Bak;Oscar R. Hernandez;Mark Gates;P. Luszczek;Vivek Sarkar