Updated sparse cholesky factors for corotational elastodynamics

Parallel Sparse Cholesky Factorization

Sparse matrix factorization plays an important role in many numerical algorithms. In this paper we describe a scalable parallel algorithm based on the Multifrontal Method. Computational experiments on a Parsytec CC system with 32 processors show that large sparse matrices can be factorized in only a few seconds.

Highly Parallel Sparse Cholesky Factorization

We develop and compare several fine-grained parallel algorithms to compute the Cholesky factorisation of a sparse matrix. Our experimental implementations are on the Connection Machine, a distributedmemory SIMD machine whose programming model conceptually supplies one processor per data element. In contrast to special-purpose algorithms in which the matrix structure conforms to the connection s...

Parallel Sparse Cholesky Fa torization

Modifying a Sparse Cholesky Factorization

Given a sparse symmetric positive definite matrix AAT and an associated sparse Cholesky factorization LDLT or LLT, we develop sparse techniques for obtaining the new factorization associated with either adding a column to A or deleting a column from A. Our techniques are based on an analysis and manipulation of the underlying graph structure and on ideas of Gill et al. [Math. Comp., 28 (1974), ...

Sparse Hypermatrix Cholesky: Customization for High Performance

Efficient execution of numerical algorithms requires adapting the code to the underlying execution platform. In this paper we show the process of fine tuning our sparse Hypermatrix Cholesky factorization in order to exploit efficiently two important machine resources: processor and memory. Using the techniques we presented in previous papers we tune our code on a different platform. Then, we ex...