Toward a Linear Time Sparse Solver with Locality-Enhanced Scalable Parallelism

具有局部增强的可扩展并行性的线性时间稀疏求解器

• 批准号: 0830679
• 负责人: Padma Raghavan
• 金额: $ 20万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830679&HistoricalAwards=false
• 关键词: Toward; Linear; Time; Sparse; Solver

Toward a Linear Time Sparse Solver with Locality-Enhanced Scalable ParallelismPadma Raghavan Many computational science simulations concern the numeric solution of partial differential equations using implicit or semi-implicit methods. Such simulations often involve nonlinear systems where the dominant costs are those for sparse linear system solution. Sparse solvers present an array of performance challenges on current and future generation multicore chip-multiprocessor architectures, and their networked ensembles into massively parallel processing systems. A new structured hybrid algorithm is sought to enable reliable and scalable solution. An inner-outer tree-structured scheme is formulated by exploiting geometry for tearing and interlacing of the graph of the coefficient matrix. Research issues concern scalable parallelism, hardware latency-masking mapping of data, and geometric mechanisms to convey numeric properties of the coefficient matrix to accelerate the solution process.

面向具有局部性增强的可扩展并行性的线性时间稀疏求解器Padma Raghavan 许多计算科学模拟都涉及使用隐式或半隐式方法的偏微分方程的数值求解。 此类模拟通常涉及非线性系统，其中主要成本是稀疏线性系统解决方案的成本。 稀疏求解器对当前和未来一代多核芯片多处理器架构及其网络集成到大规模并行处理系统中提出了一系列性能挑战。寻求一种新的结构化混合算法来实现可靠且可扩展的解决方案。 通过利用几何学对系数矩阵图进行撕裂和交错，制定了内外树结构方案。研究问题涉及可扩展并行性、数据的硬件延迟屏蔽映射以及传达系数矩阵数值属性以加速求解过程的几何机制。