[PhD Thesis] Parallel Algorithms For Numerical Linear Algebra on a Shared Memory Multiprocessor

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Author(s)Kaya D
Publication type Report
Series Title
Year1995
Pages
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This thesis discusses a variety of parallel algorithms for linear algebra problems including the solution of the linear system of equations Ax = b using QR and LU decomposition, reduction of a general matrix A to Hessenberg form, reduction of a real symmetric matrix B to tridiagonal form, and solution of the symmetric tridiagonal eigenproblem. Empirical comparison are carried out using various different versions of the above algorithms and this is described in this thesis. We also compare three different synchronisation mechanisms when applied to the reduction to Hessenberg form problem. We implement Cuppen's method for computing both eigenvalues and eigenvectors of a real symmetric tridiagonal matrix T using both recursive and non-recursive implementation. We consider parallel implementations of these versions and also consider parallelisation of the matrix multiplication part of the algorithm. We present some numerical results illustrating an experimental evaluation of the effect fo deflation on accuracy, comparison of the parallel implementations and comparison of the additional parallelisation for matrix multiplication. A variety of algorithms are investigated which involve varying amounts of overlap between different parts of the calculation and collecting together updates as far as possible to make good use of the storage hierarchy of the shared memory multiprocessor. Algorithms using dynamic task allocation are compared with ones which do not. The results presented have been obtained using the C++ programming language, with parallel constructs provided by the encore Parallel Threads package on a shared memory Encore Multimax (MIMD) computer. The experimental results demonstrate that dynamic task allocation can be sometimes very effective on this machine, and that very high efficiency is often obtainable with careful construction of the parallel algorithms even for relatively small matrices.
InstitutionDepartment of Computing Science, University of Newcastle upon Tyne
Place PublishedNewcastle upon Tyne
NotesBritish Lending Library DSC stock location number: DX187252
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