Sparse Matrix Algorithms on Distributed Memory Multiprocessors

算法的设计、分析和实现 分布式存储器上稀疏矩阵问题求解 将研究多处理器。发展这些 并行稀疏矩阵算法应该具有挑战性的影响， 大规模的计算问题，在几个科学，计量经济学， 和工程学科。 将调查的问题包括： O 一种谱嵌套剖分算法 有效分解稀疏矩阵的好序 同时， O 映射计算子任务的算法和 给定矩阵到分布式存储器中的处理器 多处理器， O 团树所扮演的角色，团树是一种用于 填充矩阵的紧凑表示，在 几种稀疏矩阵算法，以及 O 稀疏正交分解算法的设计 最小二乘问题的解 分布式内存多处理机。

The design, analysis, and implementation of algorithms for the solution of sparse matrix problems on distributed memory multiprocessors will be investigated. The development of these parallel sparse matrix algorithms should have an impact of challenging large-scale computational problems in several scientific, econometric, and engineering disciplines. The following are among the problems that will be investigated: o A spectral nested dissection algorithm for computing good orderings for factoring sparse matrices efficiently in parallel, o Algorithms for mapping computational subtasks and the given matrix to the processors in a distributed memory multiprocessor, o The role played by the clique tree, a data structure for the compact representation of filled matrices, in several sparse matrix algorithms, and o The design of sparse orthogonal factorization algorithms for the solution of least-squares problems on distributed memory multiprocessors.