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。直接方法可用于寻找许多数值代数应用的解，包括稀疏线性系统、稀疏线性最小二乘和特征值问题；因此，它们构成了广泛的大规模应用的支柱。 在广泛使用且不断增长的佛罗里达大学稀疏矩阵集合中，存在来自结构工程、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、电力网络、社交网络、文档网络的问题等。 SparseKaffe 项目团队将开发具有不规则和分层结构的高性能并行稀疏直接方法的算法和软件，这些算法和软件可以利用混合多核处理器集群来实现计算性能的数量级提升，同时也密切关注能源需求。 这需要开发用于调度、能量最小化和内存管理的新颖和创新算法；开发利用不同硬件特性的新颖的用户引导自动调整算法；并设计用于创建自动调整软件的通用基础设施。稀疏直接方法的使用非常广泛，许多相关的科学和工程应用领域被推动以更高的规模运行。 该团队期望 SparseKaffe 求解器不仅能够为使用它们的应用程序提供高性能，而且能够满足他们日益要求的能源效率。该团队还将创建一门课程和一组相应的课程模块，教学生如何开发算法和软件，从而在混合多核处理器集群上实现数量级的性能提升。