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ordering of the nodes Any priority scheme for ordering the nodes, as discussed in Section 513, can be employed Not surprisingly, Gerasoulis and Yang [76] suggest employing the allocated bottom level (De nition 419) of the current clustering Ci 1 The last issue to address in this clustering approach is the order in which edges are considered There is no constraint on the possible orders; however, an argument for a good heuristic is to zero important edges rst This is the same argument as in list scheduling, where important nodes should be considered rst Another analogy to list scheduling is that the priority of the edges can be static or dynamic Algorithm 17 is ormulated in a generic way that allows one to handle both static and dynamic priorities Static Edge Order In case static priorities are used, the edges can be sorted into a list before the main part of the algorithm starts, just like in static list scheduling (Algorithm 9) An idea that comes to mind is to sort the edges in decreasing order of their weights (as suggested by Sarkar [167]); that is, edges with high costs are considered earlier than those with low costs Such an ordering will substantially reduce the total communication cost of the resulting clustering, with the implicit assumption that this also reduces the schedule length As with the ordering of the nodes in, for example, list scheduling (Section 513), many other orderings are conceivable Exercise 57 asks you to suggest priority schemes to order edges The complexity of single edge clustering with a static edge order is as follows In each step one edge is considered for zeroing; that is, there are |E| steps When an edge is zeroed, the node order must be determined, which usually is O(V + E) (eg, using the allocated bottom level as the node priority; see above) Then the schedule length is determined, which implies in the worst case a construction of a schedule, which is also O(V + E) (see Section 52) It results in a complexity of O(V + E) for each step and a total complexity of O(E(V + E)) This complexity result implies that the ordering of the edges has lower or equal complexity The total complexity might increase for the case where higher complexity scheduling algorithms are used in each step.

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It is irrelevant to which processor node a is scheduled and P1 is chosen The next node, b, is also scheduled on P1 , as local communication between a and b permits node b s earliest DRT on P1 and thus its earliest start time Nodes c and d are best scheduled on processors P2 and P3 , despite the remote communication with a on these processors; on P1 they had to wait until b nishes, which is later than their start times on P2 and P3 The partial schedule at this point is shown in Figure 51(a) Node e is now best scheduled on P1 , due to P1 s early nish time Node f s start time is identical on all processors and P1 is chosen Next, node g is scheduled on P2 , since its predecessor node c is scheduled on this processor (Figure 51(b)) For node i, all processors allow the same earliest start time and the node is scheduled on P3 Node h depends on nodes d and e, whereby node d sends the larger message This determines the earliest start time on the two processors P1 and P2 , of which P2 is chosen (Figure 51(c)) The next node j is best scheduled on processor P1 and node k s start must be delayed on every processor in the wait for communications from nodes h and j The nal schedule, with k on P2 , is depicted in Figure 51(d) Complexity The complexity of the simple list scheduling can be broken down into the complexity of the rst and the second part (Algorithm 9) As for the rst part, its complexity is analyzed in Section 513, since it depends on the employed priority scheme The complexity of the second part depends on the way in which a processor is chosen for a node With start time minimization (Algorithm 10) the omplexity is as follows To calculate the start time of a node n according to Eq (51), the data ready time tdr (n) is computed, which involves one calculation for every predecessor of n For all nodes, calculating the DRT amortizes to O(E), as the sum of the predecessors of all nodes is |E| Since this is done for each processor, the total complexity of the DRT calculation is O(PE) The start time of every node is computed on every processor, that.

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