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 includes 4 keynote lectures and 86 revised papers of 15 invited minisymposia prepared at the following themes: fresh advances in dense linear algebra, CFD purposes for prime functionality computing, HPC environments: visualization and parallelization instruments, instruments, grid info administration, grids for clinical computing, simulations of fabrics, novel facts codecs and algorithms for dense linear algebra computations, bioinformatics and computational biology, software program instruments for parallel CFD functions, and multi-scale physics.
The forty five revised complete papers of the most music are equipped in topical sections on partial differential equations, grid computing, parallel clinical computing algorithms, linear algebra, simulation environments, algorithms and functions for blue gene/L, medical computing purposes, clinical computing instruments, parallel seek algorithms, peer-to-peer computing, mobility and safety, algorithms for single-chip multiprocessors.
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Extra resources 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
3) Produce LAPACK and ScaLAPACK wrappers in other languages. Based on current user surveys, these languages will tentatively be C, C++, Python and MATLAB. See below for software engineering details. Ease of conversion from serial code (LAPACK) to parallel code (ScaLAPACK) is done by making the interfaces (at least at the driver level) as similar as possible. This includes expanding ScaLAPACK’s functionality to include as much of LAPACK as possible (see Section 4). Obtaining predictable results in a dynamic environment is important for debugging (to get the same answer when the code is rerun), for reproducibility, auditability (for scientiﬁc or legal purposes), and for performance (so that runtimes do not vary widely and unpredictably).
Section 3 discusses two kinds of improved algorithms: faster ones and more accurate ones. Since it is hard to improve both simultaneously, we choose to include a new faster algorithm if it is about as accurate as previous algorithms, and we include a new more accurate algorithm if it is at least about as fast as the previous algorithms. Section 4 describes new linear algebra functionality that will be included in new Sca/LAPACK releases. Section 5 describes our proposed software structure for Sca/LAPACK.
The table also shows that in the ScaLAPACK library the implementation of some driver routines and their specialized computational routines are currently missing. The highest priority ones to include are marked “add”. We also want expert drivers that compute error bounds. Extending Current Functionality. e. to ﬁnd roots of polynomials, which would replace the roots() function in Matlab, based on recent work of Gu, Bini and others on semiseparable matrices [20,66,10]; (3) recent structure-preserving algorithms for matrix polynomial eigenvalue problems, especially quadratic eigenvalue problems ; (4) new algorithm for matrix functions like the square root, exponential and sign function ; (5) algorithms for various Sylvester-type matrix equations (recursive, RECSY; parallel, SCASY) [48,49,40].
Applied Parallel Computing. State of the Art in Scientific Computing: 8th International Workshop, PARA 2006, Umeå, Sweden, June 18-21, 2006, Revised Selected Papers by Alfredo Buttari, Jack Dongarra, Jakub Kurzak, Julien Langou, Piotr Luszczek (auth.), Bo Kågström, Erik Elmroth, Jack Dongarra, Jerzy Waśniewski (eds.)