Applied Parallel and Scientific Computing: 10th by Bo Kågström, Daniel Kressner, Meiyue Shao (auth.), Kristján

By Bo Kågström, Daniel Kressner, Meiyue Shao (auth.), Kristján Jónasson (eds.)

The quantity set LNCS 7133 and LNCS 7134 constitutes the completely refereed post-conference complaints of the tenth overseas convention on utilized Parallel and medical Computing, PARA 2010, held in Reykjavík, Iceland, in June 2010. those volumes comprise 3 keynote lectures, 29 revised papers and forty five minisymposia displays prepared at the following issues: cloud computing, HPC algorithms, HPC programming instruments, HPC in meteorology, parallel numerical algorithms, parallel computing in physics, medical computing instruments, HPC software program engineering, simulations of atomic scale platforms, instruments and environments for accelerator dependent computational biomedicine, GPU computing, excessive functionality computing period tools, real-time entry and processing of enormous info units, linear algebra algorithms and software program for multicore and hybrid architectures in honor of Fred Gustavson on his seventy fifth birthday, reminiscence and multicore concerns in clinical computing - thought and praxis, multicore algorithms and implementations for software difficulties, quick PDE solvers and a posteriori mistakes estimates, and scalable instruments for top functionality computing.

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Additional info for Applied Parallel and Scientific Computing: 10th International Conference, PARA 2010, Reykjavík, Iceland, June 6-9, 2010, Revised Selected Papers, Part I

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Mm }. (4) – The (time) ordered set of corresponding values of f : s1 , . . , sm . (5) – The set FM|C of all fit functions/probability distributions defined on M, but with fixed values in C. Consider a blind inversion problem where we have no knowledge of the actual fit function, and we search for at least one acceptable solution to the problem. From the outset, the total number of possible fit functions is equal to |FM | = |S||M| . (6) We can now ask: What is the probability that an algorithm, when sampling M in m distinct points, sees the function values s1 , .

This is actually the only case for which PDLAQR0 is called within the AED phase, which seems to indicate that the choice of the crossover point requires some additional fine tuning. Note that the largest AED window in all these experiments is of size 1536. According to Table 1, we expect even more significant improvements for larger matrices, which have larger AED windows. On Aggressive Early Deflation in Parallel Variants of the QR Algorithm 9 Table 2. Execution time in seconds for old PDHSEQR (1st line for each n), new PDHSEQR (2nd line).

The new implementation of PDLAQR1 turns out to require much less execution time than the old one, with a few, practically nearly irrelevant exceptions. Also, the new PDLAQR1 scales slightly better than PDLAQR0, especially when the size of matrix is not large. It is worth emphasizing that the scaling of all implementations eventually deteriorates as the number of processor increases, simply because the involved matrices are not sufficiently large to create enough potential for parallelization. Quite naturally, PDLAQR0 becomes faster than the new PDLAQR1 as the matrix size increases.

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