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determined by a heuristic Again, this can be done globally (ie, identically for all chromosomes) or individually for each chromosome, which naturally can be done with list scheduling (Algorithm 9) Kwok and Ahmad [110] propose to encode the node list in the chromosome, with the nodes always being in precedence order Some authors proposed value encoding for the node priorities (eg, Ahmad and Dhodhi [3], and Dhodi et al [55], Sandnes and Megson [164, 165]) The chromosome has then the same structure as the processor allocation chromosome that is, each gene represents a node while each gene s value is the corresponding node s priority The actual node order is determined for each chromosome with a procedure such as Algorithm 12 A isadvantage of such an encoding is that nodes can have the same priority Figure 616 gives examples for the two different encoding approaches The chromosomes are for the scheduling of the sample task graph (Figure 616(a)) on three processors Direct Representation In the direct representation, the chromosome encodes the processor allocation and the execution order of the nodes Consequently, the chromosome consists of two parts the two chromosome approaches used in the indirect representation as discussed earlier With this information, the nodes start times are readily computed, using a simple list scheduling (see Theorem 51) No heuristic decisions are taken in the construction of the schedule Wang et al [198] use such a two-part chromosome one for the processor allocation and the other for a global node list. allocation and the other for a global node list. . Barcode Encoder In Java Using Barcode creation for Java .Related: Print Intelligent Mail .NET

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Algorithm 12 Create Node List 1: Assign a priority to ach n V 2: Put source nodes n V : pred(n) = into priority queue Q 3: while Q = do Q only contains free nodes 4: Let n be the node of Q with highest priority; remove n from Q 5: Append n to list L 6: Put {ni succ(n) : pred(ni ) L} into Q 7: end while which costs O(log V) and the determination of the free nodes amortizes to O(E) Removing the node with the highest priority from the heap is O(1) An example for a metric that is not compatible with the precedence order is tl + bl a metric that gives preference to the nodes of the critical path Table 51 displays several node orders of the sample task graph obtained with node level based priority metrics The values of the node levels are given in Table 41 As analyzed in Section 44, calculating these levels has a complexity of O(V + E) For all these metrics the rst part of the simple list scheduling is thus O(V log V + E) (calculating levels: O(V + E) + sorting: O(V log V) or O(V log V + E)) Already the few examples in Table 51 illustrate the large variety of possible node orders In experiments, the relevance of the node order for the schedule length was demonstrated (eg, Adam et al [2], Sinnen and Sousa [177]) Unfortunately, most comparisons of scheduling algorithms (eg, Ahmad et al [6], Gerasoulis and Yang [76], Khan et al [103], Kwok and Ahmad [111], McCreary et al [136]) analyze entire algorithms: that is, the algorithms differ not only in one but many aspects This generally impedes one from drawing conclusions on discrete aspects of the algorithms (eg, on the node order) Still, most algorithms apply orders based on node levels and the bottom level order exhibits good average performance (eg, Adam et al [2], Sinnen and Sousa [177]) Breaking Ties Table 51 also shows examples where the node order is not fully de ned, indicated in the table by parentheses around the respective nodes Incidentally,.

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Load Minimization Mapping Essentially, this algorithm is just a special case of list scheduling with start time minimization (Section 51) To apply list scheduling to this problem, it suf ces to consider each cluster as an independent node In the rst phase they are ordered in decreasing order of their total weight w(C), identical to the wrap mapping In the second phase they are assigned to the processor with the least load, that is, earliest nish time in list scheduling terminology In comparison to wrap mapping, the actual assignment of the clusters to the processors increases to O(C log P), using a priority queue for the processors (eg, heap, Cormen et al [42]) With the above conjecture that |C| > |P|, however, the total complexity is not affected and remains O(V + C log C) Gerasoulis et al [75], proposed a variant of wrap mapping, where the clusters with above average total weight are separated from the other clusters A quite different technique was proposed by Sarkar [167] Essentially, he suggested to use list scheduling to assign the clusters to the processors As usual, the nodes are rst ordered into a priority list While iterating over the list, each node is assigned to the processor that results in the smallest increase of the schedule length The crucial difference from ordinary list scheduling is that as oon as one node of a cluster C is assigned to a processor P, all other nodes belonging to the same cluster C are also assigned to processor P A major difference from the load balancing heuristics discussed earlier stems from the fact that communication costs are also taken into account, namely, when determining the schedule length increase. MSI Plessey Encoder In VS .NET Using Barcode generation for .Related: 

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