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Barcode Maker In Java Using Barcode printer for Java Control . 2d Barcode Encoder In C# Using Barcode creator for .value can also be used Common multiplicities include 1, 0*, 1*, and * NSAPI Netscape's C language API for adding application extensions to their Web servers object The basic unit of object orientation An object is an entity that has attributes, behavior, and identity Objects are members of a class, and the attributes and behavior of an object are defined by the class definition An bject and an instance are the same thing object database An object-oriented database system See ODMG and ODBMS object orientation A method of developing software that uses abstraction with objects, encapsulated classes, communication via messages, object lifetime, class hierarchies, and polymorphism object lifetime The time an object exists - from its instantiation in its constructor until the object no longer exists and has been finalized by the Java garbage collector The creation point of an object is under program control, but the exact moment when an object ceases to exist cannot be determined because of the way the Java garbage collector works ODBC Open Database Connectivity (ODBC) is Microsoft's standard based on the X/Open SQL call-level interface specifications It does not work well with Java ODBMS Object Database Management System, the result of integrating database capabilities with object-oriented programming language capabilities ODMG Object Data Management Group, responsible for setting standards for object databases OMG Object Management Group, an organization that defines and promotes object oriented programming standards.Related: Create EAN-8 .NET , Make PDF417 .NET WinForms , ASP.NET ISBN Generator



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length is 3k (Figure 52(c)) For any C a k with k > 1, k > C can be chosen so that sl(S) = 3k > 3C = C sl(Sopt ) In particular, list scheduling with start time minimization does not even guarantee a schedule length shorter than the sequential time of a task graph Still, numerous experiments have demonstrated that list scheduling produces good schedules for task graphs with low to medium communication (eg, Khan et al [103], Kwok and Ahmad [111], Sinnen and Sousa [177]) 512 With Dynamic Priorities The simple list scheduling outlined in Algorithm 9 establishes the schedule order of the nodes before the actual scheduling process During the node scheduling in the second part, this order remains unaltered, so the node priorities are static To achieve better schedules, it might be bene cial to consider the state of the partial schedule in the schedule order of the nodes Changing the order of the nodes is equivalent to changing their priorities, hence the node priorities are dynamic It remains that the node order must be compatible with the precedence constraints of the task graph, which is obeyed if only free nodes are scheduled A list scheduling algorithm for dynamic node priorities is given in Algorithm 11 Algorithm 11 List Scheduling Dynamic Priorities (G (V, E, w, c), P) 1: Put source nodes {ni V : pred(ni ) = } into set S 2: while S = do S only contains free nodes 3: Calculate priorities for nodes ni S 4: Choose a node n from S; S S n 5: Choose a processor P for n 6: Schedule n on P 7: S S {ni succ(n) : pred(ni ) Scur } 8: end while Instead of iterating over the node list, Algorithm 11 calculates in each step the priority of all free nodes and only then selects a node and a processor for scheduling After the scheduling of node n, the set S of free nodes is updated with the nodes that became free by the scheduling of n (line 7) Obviously, this approach has higher complexity in general than the simple list scheduling presented in Algorithm 9, because the priority of every node is determined several times Algorithm 11 is a generalization of the simple list scheduling, which becomes apparent by considering line 3 If this line is moved to the beginning of the algorithm and the priorities are calculated for all nodes, not only for the free nodes, the simple list scheduling is obtained This becomes even more distinct in Section 513, where the construction of node lists is explained Choosing Node Processor Pair Some algorithms dynamically compute the priorities of the free nodes by evaluating the start time (see Eq (51)) for every free.

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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,.

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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,.

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