Engineering

Get Algorithm Engineering and Experimentation: International PDF

By Ruy Luiz Milidiú, Artur Alves Pessoa, Eduardo Sany Laber (auth.), Michael T. Goodrich, Catherine C. McGeoch (eds.)

ISBN-10: 354048518X

ISBN-13: 9783540485186

ISBN-10: 3540662278

ISBN-13: 9783540662273

Symmetric multiprocessors (SMPs) dominate the high-end server marketplace and are at present the first candidate for developing huge scale multiprocessor structures. but, the layout of e cient parallel algorithms for this platform c- rently poses numerous demanding situations. the reason is, the swift development in microprocessor velocity has left major reminiscence entry because the fundamental issue to SMP functionality. on the grounds that reminiscence is the bottleneck, easily expanding the n- ber of processors won't unavoidably yield higher functionality. certainly, reminiscence bus obstacles often restrict the dimensions of SMPs to sixteen processors. This has at the very least twoimplicationsfor the algorithmdesigner. First, considering the fact that there are quite few processors availableon an SMP, any parallel set of rules needs to be aggressive with its sequential counterpart with as low as one processor with a view to be r- evant. moment, for the parallel set of rules to scale with the variety of processors, it needs to be designed with cautious realization to minimizing the quantity and kind of major reminiscence accesses. during this paper, we current a computational version for designing e cient al- rithms for symmetric multiprocessors. We then use this version to create e cient options to 2 broadly di erent kinds of difficulties - associated record pre x com- tations and generalized sorting. either difficulties are reminiscence extensive, yet in die hire methods. while generalized sorting algorithms mostly require a wide numberofmemoryaccesses, they areusuallytocontiguousmemorylocations. against this, prex computation algorithms in general require a extra modest qu- tity of reminiscence accesses, yet they're are typically to non-contiguous reminiscence locations.

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Extra info for Algorithm Engineering and Experimentation: International Workshop ALENEX’99 Baltimore, MD, USA, January 15–16, 1999 Selected Papers

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The resulting sum of the successor indices is stored in location i of the array Z. – (2): Processor P0 computes the sum T of the p values in the array Z. The index of the head of the list is then h = 12 n(n − 1) − T . R. Helman, J. J´ aJ´ a – (3): For j = is p up to (i+1)s p − 1 , processor Pi randomly chooses a loca- tion x from the block of list elements with indices n (j − 1) (s−1) through n j (s−1) − 1 as a splitter which defines the head of a sublist in List (processor P0 chooses the head of the list as its first splitter).

Operation counts and the observer technique enabled us to explain these observations. It was also somewhat unexpected how much more difficult problems on the lowest density level turned out to be. However, this corresponds to a much higher number of blossom shrinking operations and a larger nesting level. Blossom shrinking is expensive but unfortunately a simple heuristic to reduce the number of shrinking steps is only successful for extremely sparse graphs which turned out to be the most difficult problem instances.

11. Our implementation: The raw data of Experiment 6 (CPU times in seconds), the fitted curve from the linear regression, and the 95% confidence intervals for the predicted values. 34 M. M¨ uller–Hannemann, A. Schwartz Experiment 7: Mesh refinement. We tested all implementation variants used for the previous experiments also for our real-world instances from the mesh refinement application. In the following table, the second column displays the number of nodes n, followed by the nesting level N and the number of blossoms B observed in our reference implementation (REF) in columns three and four.

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Algorithm Engineering and Experimentation: International Workshop ALENEX’99 Baltimore, MD, USA, January 15–16, 1999 Selected Papers by Ruy Luiz Milidiú, Artur Alves Pessoa, Eduardo Sany Laber (auth.), Michael T. Goodrich, Catherine C. McGeoch (eds.)


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