By Alfredo Buttari, Jack Dongarra, Jakub Kurzak, Julien Langou, Piotr Luszczek (auth.), Bo Kågström, Erik Elmroth, Jack Dongarra, Jerzy Waśniewski (eds.)
This publication constitutes the completely refereed post-proceedings of the eighth overseas Workshop on utilized Parallel Computing, PARA 2006, held in Umeå, Sweden, June 2006.
This quantity comprises 4 keynote lectures and 86 revised papers of 15 invited minisymposia prepared at the following issues: fresh advances in dense linear algebra, CFD functions for prime functionality computing, HPC environments: visualization and parallelization instruments, instruments, grid facts administration, grids for medical computing, simulations of fabrics, novel facts codecs and algorithms for dense linear algebra computations, bioinformatics and computational biology, software program instruments for parallel CFD purposes, and multi-scale physics.
The forty five revised complete papers of the most tune are geared up in topical sections on partial differential equations, grid computing, parallel medical computing algorithms, linear algebra, simulation environments, algorithms and purposes for blue gene/L, clinical computing functions, clinical computing instruments, parallel seek algorithms, peer-to-peer computing, mobility and safety, algorithms for single-chip multiprocessors.
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Additional info for Applied Parallel Computing. State of the Art in Scientific Computing: 8th International Workshop, PARA 2006, Umeå, Sweden, June 18-21, 2006, Revised Selected Papers
Lin. Alg. Appl. 358, 219–238 (2003) 60. 41. IBM Research technical report (2005) 61. : Highly accurate symmetric eigenvalue decomposition and hyperbolic SVD. Lin. Alg. Appl. 358, 387–424 (2002) 62. : A comparison of lookahead and algorithmic blocking techniques for parallel matrix factorization. Int. J. Parallel Distrib. Systems Networks 4(1), 26–35 (2001) 63. : A survey of the quadratic eigenvalue problem. SIAM Review 43, 234–286 (2001) 64. gov/tnt 65. : Towards an accurate model for collective communications.
SIAM J. Matrix Anal. Appl. 25(2), 464–485 (2003) 24. : Error bounds from extra precise iterative reﬁnement. ACM TOMS 32(2), 325–351 (2006) 25. : Reliable computation of the condition number of a tridiagonal matrix in O(n) time. SIAM J. Matrix Anal. Appl. 19(3), 776–796 (1998) 26. : LINPACK User’s Guide. SIAM, Philadelphia, PA (1979) 27. : The design and implementation of the parallel out-ofcore ScaLAPACK LU, QR, and Cholesky factorization routines. Computer Science Dept. ps 28. : Key concepts for parallel out-of-core LU factorization.
LU has left-looking and right-looking formulations . Transition between the two can be done by automatic code transformations , although more than simple dependency analysis is needed. Lookahead can improve performance by performing panel factorizations in parallel with updates to the trailing matrix from the previous algorithm steps . The lookahead can be of arbitrary depth (as exploited by the LINPACK benchmark ). In fact lookahead provides a spectrum of implementations from right-looking (no lookahead) to left-looking (maximum lookahead).
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