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Barcode generation engine re-written from ground up .Related: . node on every processor That node of all free nodes that allows the earliest start time is selected together with the processor on which this time is achieved The choice of the node and the processor is made simultaneously, based on the state of the partial schedule Two examples for dynamic list scheduling algorithms employing this technique are ETF (earliest time rst) by Hwang et al [94] and DLS (dynamic level scheduling) by Sih and Lee [169] In comparison to the simple list scheduling, the complexity increases by a factor of |V| Evaluating the start time of every free node on every processor is in the worst case O(P(V + E)), which is already the complexity of the simple list scheduling As this step is done |V| times, the total complexity is O(P(V2 + VE)) 513 Node Priorities Discussion of the list scheduling technique distinguished between static and dynamic node priorities ow these priority schemes are realized is discussed in the next paragraphs Static Static priorities are based on the characteristics of the task graph In the simplest case, the priority metric itself establishes a precedence order among the nodes It is then suf cient to order the nodes according to their priorities with any sort algorithm, for example, with Mergesort (Cormen et al [42]), which has a complexity of O(V log V) Prominent examples for such priority metrics are the top and the bottom level of a node (Section 442) Lemma 51 (Level Orders Are Precedence Orders) Let G = (V, E, w, c) be a task graph The nonincreasing bottom level (bl) order and the nondecreasing top level (tl) order of the nodes n V are precedence orders Proof The lemma follows immediately from the recursive de nition of the bottom level and top level in Eqs (443) and (444), respectively A node always has a smaller bottom level than any of its predecessors and a higher top level than any of its successors If the priority metric is not compatible with the precedence constraints of the task graph, a correct order can be established by employing the free node concept Algorithm 12 shows how to create a priority ordered node list that complies with the precedence constraints As mentioned before, the general structure is similar to Algorithm 11, but a priority queue (Q) is used instead of a set (S), because the priorities assigned to the nodes are static The simple list scheduling outlined in Algorithm 9 could be rewritten by using the below algorithm and substituting line 5 with lines 5 and 6 from Algorithm 9 However, then the conceptually nice separation of the rst and the second part of simple list scheduling is lost Suppose the priority queue in Algorithm 12 is a heap (Cormen et al [42]); the algorithm s complexity is O(V log V + E): every node is inserted once in the heap,. 7.0 for ASP.NET; Microsoft .NET Framework 2.0 (or . 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You will find .Related: RDLC ASP.NET Barcode Generation , Generate Barcode Crystal , Generate Barcode SSRS Library Algorithm 12 Create Node List 1: Assign a priority to each 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 hus 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,. for ASP.NET and ASP.NET Mobile framework. . we are going to generate a fixed URL barcode . 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