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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 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 a number of demanding situations. the reason is, the swift growth in microprocessor pace has left major reminiscence entry because the basic quandary to SMP functionality. considering reminiscence is the bottleneck, easily expanding the n- ber of processors won't unavoidably yield greater functionality. certainly, reminiscence bus barriers regularly restrict the dimensions of SMPs to sixteen processors. This has at the least twoimplicationsfor the algorithmdesigner. First, on the grounds that there are rather few processors availableon an SMP, any parallel set of rules needs to be aggressive with its sequential counterpart with as low as one processor to be able to be r- evant. moment, for the parallel set of rules to scale with the variety of processors, it has to be designed with cautious realization to minimizing the quantity and sort 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 extensively di erent different types of difficulties - associated record pre x com- tations and generalized sorting. either difficulties are reminiscence extensive, yet in die lease methods. while generalized sorting algorithms as a rule require a wide numberofmemoryaccesses, they areusuallytocontiguousmemorylocations. against this, prex computation algorithms commonly require a extra modest qu- tity of reminiscence accesses, yet they're tend to be to non-contiguous reminiscence locations.

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

Example text

Our measurements for the mesh refinement test-suite confirm all of our previous results for the artificial test-suites. The reference implementation (REF) is slightly faster than the variants (DU2), (CS3), and (HB1) but clearly faster than all others. 5 Conclusion This paper presents an experimental analysis of several variants of Pulleyblank’s primal-dual b-matching algorithm. The choice of experiments was guided by a set of hypotheses based on previous computational experiments with 1-matching problems (Derigs & Metz [DM86], Applegate & Cook [AC93]) and with bmatching problems (Miller & Pekny [MP95]).

3 Prefix Computations Consider the problem of performing a prefix computation on a linked list of n elements stored in arbitrary order in an array X. data, its input value for the prefix computation. prefix = if Xi is the head of the list. prefix otherwise. (1) where pre is the index of the predecessor of Xi . The last element in the list is distinguished by a negative index in its successor field, and nothing is known about the location of the first element. Any of the known parallel prefix algorithms in the literature can be considered for implementation on an SMP.

Hence, the amount of “remaining work” for the integral phase is nearly constant. The other is that the average augmentation value increases roughly with the same speed as the node capacities. The latter explains why the number of augmentations does not increase (as suspected). Experiment 6: Comparison with Miller & Pekny. Miller & Pekny kindly provided us with an executable of their b-matching code which is specialized to solve geometric problem instances. Because of its limited interface we could test this code only on problems generated by the built-in problem generator.

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