Algorithm Engineering and Experimentation: International by Ruy Luiz Milidiú, Artur Alves Pessoa, Eduardo Sany Laber

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

Symmetric multiprocessors (SMPs) dominate the high-end server industry and are at the moment the first candidate for developing huge scale multiprocessor structures. but, the layout of e cient parallel algorithms for this platform c- rently poses a number of demanding situations. it is because the fast growth in microprocessor velocity has left major reminiscence entry because the fundamental predicament to SMP functionality. seeing that reminiscence is the bottleneck, easily expanding the n- ber of processors won't unavoidably yield larger functionality. certainly, reminiscence bus barriers mostly restrict the scale of SMPs to sixteen processors. This has at the very least twoimplicationsfor the algorithmdesigner. First, in view that there are really few processors availableon an SMP, any parallel set of rules needs to be aggressive with its sequential counterpart with as low as one processor in an effort 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 cognizance 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 ideas to 2 extensively di erent different types 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 as a rule require a wide numberofmemoryaccesses, they areusuallytocontiguousmemorylocations. in contrast, prex computation algorithms generally require a extra modest qu- tity of reminiscence accesses, yet they're tend to be to non-contiguous reminiscence locations.

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

Symmetric multiprocessors (SMPs) dominate the high-end server marketplace and are at the moment the first candidate for developing huge scale multiprocessor platforms. but, the layout of e cient parallel algorithms for this platform c- rently poses numerous demanding situations. this is because the quick growth in microprocessor velocity has left major reminiscence entry because the fundamental predicament to SMP functionality.

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B. Orlin, A faster strongly polynomial minimum cost flow algorithm, Proceedings of the 20th Annual ACM Symposium on Theory of Computing (1988), 377–387. PR82. M. Padberg and M. R. Rao, Odd minimum cut-sets and b-matchings, Math. Oper. Res. 7 (1982), 67–80. Pul73. W. R. D. thesis, Faculty of Mathematics, University of Waterloo, 1973. Pul95. W. R. Pulleyblank, Matchings and extensions, Handbook of Combinatorics, vol. 1, North-Holland, 1995, pp. 179–232. BD83. Designing Practical Efficient Algorithms for Symmetric Multiprocessors (Extended Abstract) David R.

Our algorithm for generalized sorting is a modification of our algorithm for sorting by regular sampling on distributed memory architectures [10]. The algorithm is a stable sort which appears to be asymptotically faster than any of the published algorithms we are aware of. Both of our algorithms were implemented in C using POSIX threads and run on four symmetric multiprocessors - the IBM SP-2 (High Node), the HP-Convex Exemplar (S-Class), the DEC AlphaServer, and the Silicon Graphics Power Challenge.

Evaluation. Fig. 7 shows that an increase of✄ node capacities has almost no effect on the time to solve problems of test-suite ✂E3 ✁, neither in the staged approach with a combined fractional and integral phase, nor in the fractional phase alone. There is again an anomaly for density d = 2 where a jump in running time oc- Implementing Weighted b-Matching Algorithms 14000 2 + 10000 8000 3 + 2 3 + 2 3 6000 3+2 4000 0 20 3 + 2 + 2 + 2 3 + 2 3 + 2 3 + 2 + + 32 3+2 3+2 3+2 3+2 3+2 3+2 3+2 32 22 24 26 28 210 212 214 Maximum node capacity log scale 5000 0 3 20 Exp.

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