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

稀疏直接方法在计算科学中的使用是无处不在的。直接方法可以用来求解许多数值代数应用，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骨干。在广泛使用并正在积极发展的佛罗里达大学稀疏矩阵收藏中，存在着来自结构工程、计算流体动力学(CFD)、计算机图形学/视觉、机器人/运动学、理论和量子化学、电力网络、社交网络、文档网络等方面的问题。SparseKaffe项目团队将为具有不规则和分层结构的高性能并行稀疏直接方法开发算法和软件，这些算法和软件可以利用混合多核处理器的集群来实现计算性能的数量级提升，同时还会仔细关注能源需求。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的算法；开发利用不同硬件特性的用户引导的新型自动调优算法；以及设计用于创建自动调优软件的公共基础设施。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被推向更高的规模。该团队希望SparseKaffe解算器不仅能为使用它们的应用程序提供高性能，还能提供他们日益增长的能效需求。该团队还将创建一门课程和一套相应的课程模块，教授学生如何开发在混合多核处理器集群上实现数量级性能提升的算法和软件。

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.