CSR:Medium:Collaborative Research: SparseKaffe: high-performance, auto-tuned, energy-aware algorithms for sparse direct methods on modern heterogeneous architectures

CSR：Medium：协作研究：SparseKaffe：现代异构架构上稀疏直接方法的高性能、自动调整、能量感知算法

• 批准号: 1514286
• 负责人: Jack Dongarra
• 金额: $ 40万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1514286&HistoricalAwards=false
• 关键词: CSR; Medium; Collaborative; Research; SparseKaffe

The use of sparse direct methods in computational science is ubiquitous. Direct methods can be used to find solutions to many numerical algebra applications, including sparse linear systems, sparse linear least squares, and eigenvalue problems; consequently they form the backbone of a broad spectrum of large scale applications. In the widely used and actively growing University of Florida Sparse Matrix Collection, there are problems from structural engineering, computational fluid dynamics (CFD), computer graphics/vision, robotics/kinematics, theoretical and quantum chemistry, power networks, social networks, document networks, among others. The SparseKaffe project team will develop algorithms and software for high-performance parallel sparse direct methods with irregular and hierarchical structure that can exploit clusters of Hybrid Multicore Processors to achieve orders of magnitude gains in computational performance, while also paying careful attention to the energy requirements. This requires the development of novel and innovative algorithms for scheduling, energy minimization, and memory management; development of novel user-guided autotuning algorithms that exploit different hardware characteristics; and designing a common infrastructure for creating auto-tuned software. The use of sparse direct methods is extensive, with many of the relevant science and engineering application areas being pushed to run at ever higher scales. The team expects SparseKaffe solvers to be able deliver not only high performance to the applications that use them, but also the energy efficiency that they will increasingly demand. The team will also create a course, and a corresponding set of course modules, to teach students how to develop algorithms and software that deliver orders of magnitude gains in performance on clusters of hybrid multicore processors.

稀疏直接方法在计算科学中的使用无处不在。直接方法可用于找到许多数值代数应用的解决方案，包括稀疏线性系统，稀疏线性最小二乘正方形和特征值问题；因此，它们形成了大规模应用的广泛频谱的骨干。 在佛罗里达大学的广泛使用和积极发展的稀疏矩阵收藏中，从结构工程，计算流体动力学（CFD），计算机图形/视觉，机器人/运动学，理论和量子化学，电力化学，电力网络，社交网络，文档网络等问题。 SparseKaffe项目团队将开发算法和软件，用于具有不规则和层次结构的高性能平行稀疏直接方法，可以利用混合多层处理器的群集，以实现计算性能的数量级增长，同时还要仔细注意能量需求。 这需要开发用于调度，能量最小化和记忆管理的新颖和创新算法；开发利用不同硬件特征的新型用户引导的自动传输算法；并设计用于创建自动调整软件的常见基础架构。稀疏直接方法的使用是广泛的，许多相关的科学和工程应用领域都被推动以更高的规模运行。 该团队预计，SparseKaffe求解器不仅能够为使用它们的应用程序提供高性能，而且能够提供越来越多的能源效率。该团队还将创建一个课程和相应的课程模块集，以教学学生如何开发算法和软件，这些算法和软件在混合多层处理器的群集中提供了数量级的增长订单。