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set of task graphs is scheduled by different heuristics under the classic model and the contention model on various target systems The employed target systems cover a wide spectrum of parallel architectures: (1) a cluster of 16 PCs (Telford [184]); (2) an 8-processor shared-memory SMP system, Sun Enterprise 3500 (Culler and Singh [48]); and (3) a 344-processor massively parallel system, Cray T3E-900 (Booth et al [26]) These systems possess different network topologies and are modeled with corresponding topology graphs In the next step, code is generated that corresponds to the task graphs and their schedules, using the C language and MPI (message passing interface [138]), the standard for message passing on parallel systems This code is then executed on the real parallel systems and the accuracy, the speedup, and the ef ciency of a schedule and its model are evaluated Let pt(S) be the execution time the parallel time of the code generated for schedule S and task graph G = (V, E, w, c) on a target system The accuracy of S is the ratio of the real execution time on the parallel system to the schedule length (ie, the estimated execution time) acc(S) = pt(S)/sl(S) The speedup of S is the ratio of sequential time to execution time sup(S) = seq(G)/pt(S) Finally, the ef ciency of S is the ratio of speedup to processor number eff ( S) = sup(S)/P| = seq(G)/( pt(S) |P|) The experimental evaluation exposed that scheduling under the classic model results in very inaccurate and inef cient schedules for real parallel systems Contention aware scheduling signi cantly improves both the accuracy and the ef ciency of the produced schedules Still, the results are improvable It is observed that results get worse under both models as the amount of communication in the task graph increases In terms of the CCR (De nition 423), the classic model is extremely inaccurate for medium (CCR = 1) and high (CCR = 10) communications Note that the ef ciency improvements from the employment of the contention model are superior to the typical difference between various scheduling algorithms under the classic model (Ahmad et al [6], Khan et al [103], Kwok and Ahmad [111]) Nonetheless, contention aware scheduling, even though signi cantly more accurate and ef cient than scheduling under the classic model, does not produce results that really satisfy As analyzed by Sinnen and Sousa [171, 176, 178], this is in part due to inappropriate topology graphs employed in the experiments, which do not exploit the full potential of the topology graph of De nition 73 It is argued that especially the switch vertex and the hyperedge are important instruments of the topology graph which should be used for an accurate re ection of communication networks Another possible explanation for the still improvable results is that communication contention is not the only aspect that is inaccurately re ected in the classic model Even for the contention model, a relation between the increase of communication (CCR) and the degradation of accuracy can be observed in the presented experimental results This indicates another de ciency of the scheduling models regarding communication Like the classic model (De nition 43), the contention model (De nition 710) supposes a dedicated communication subsystem to exist in the target system (Property 4) With the assumed subsystem, computation can overlap with communication, because.

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In support of this evaluation is a key tenet of risk management derived from odern portfolio theory that says you only get paid for bearing nondiversifiable risk In other words, if an efficient market exists for a particular risk, a company would be better off to pass the risk on to this efficient market, and only keep those risks that are nondiversifiable and that they can manage to produce a superior profit During this stage of risk separation, do not forget to look at the impact on various stakeholders For example, a common exercise especially given the recent investment returns is for plan sponsors to focus on the investment risk in their retirement plans The first question is, Who should bear this risk In a DB plan, this risk resides with the plan sponsor With a defined contribution (DC) plan, this risk is transferred to the participant.

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Moving beyond this first-order question, you should then ask, Who is best able to bear this risk Both now and in the future Research has shown that when employees make direct investments in a DC plan, they typically perform less well than professional investment managers This risk shifting becomes asymmetric: It reduces the employers risk while increasing the employees risk, but by a much larger amount Remember to look beyond one single viewpoint to include all stakeholders Asset Allocation Question Combining all this and getting back to the original question Is the 60/40 asset allocation a dinosaur my opinion is that the jury is still out in the near term In the long term, however, I believe we will see the allocation move to 20 percent equity and 80 percent fixed income for the following reasons: Tax arbitrage.

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