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QR Code Printer In Visual C# Using Barcode creation for .NET .This determination of the start time is here called end technique, as node n is scheduled at the end of all other nodes scheduled on processor P In Section 61, another technique is presented the insertion technique which can compute earlier start times, yet with a higher complexity Since ts (n, P) tdr (n, P), ts (n, P) complies with Condition 43 that is, it complies with Condition 42 for all predecessors of node n Furthermore, as ts (n, P) tf (P) (here tf (P) is the nish time of P in the partial schedule Scur ), ts (n, P) cannot violate Condition 41 either An interesting property of the end technique is that it produces a schedule of optimal length if the nodes are scheduled in the right order on the right processor Theorem 51 (End Technique Is Optimal) Let S be a feasible schedule for task graph G = (V, E, w, c) on system P Using simple list scheduling (Algorithm 9 the schedule Send is created employing the end technique (De nition 52), whereby the nodes are scheduled in nondecreasing order of their start times in S and allocated to the same processors as in S Then sl(Send ) sl(S) (52).Related: Java QR Code Generator Size, Create QR Code VB.NET , Print QR Code Word Image



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The HarringtonO Shea Career Decision-Making System (CDM) and he Kaufman Adolescent and Adult Intelligence Test (KAIT): Relationship of interest scale scores to fluid and crystallized IQs at ages 12 to 22 years Research in the Schools, 2, 63 73 McLean, J E, Kaufman, A S, & Reynolds, C R (1988a) The canonical relationship between the WAIS-R Verbal and performance scales Perceptual and Motor Skills, 66, 432 434 McLean, J E, Kaufman, A S, & Reynolds, C R (1988b, November) What role does formal education play in the IQ age relationships across the adult life-span Mid-South Educational Researcher, 17(1), 6 8, 13 18 McLean, J E, Kaufman, A S, & Reynolds, C R (1989) Base rates of WAIS-R subtest scatter as a guide for clinical and neuropsychological assessment Journal of Clinical Psychology, 45(6), 919 926 McMullen, L M, & Rogers, D E (1984).

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WAIS characteristics of nonpathological obsessive and hysteric styles Journal of Clinical Psychology, 40, 577 579 McNemar, Q (1942) The revision of the Stanford-Binet Scale Boston: Houghton Mifflin McNemar, Q (1974) Correction to a correction Journal of Consulting and Clinical Psychology, 42, 145 146 McPherson, S, Buckwalter, J G, Tingus, K, Betz, B, & Back, C (2000) The Satz-Mogel short form of the Wechsler Adult Intelligence Scale-Revised: Effects of global mental status and age on test-retest reliability Journal of Clinical and Experimental Neuropsychology, 22, 545 553 McQuaid, M M, & Spreen, O (1989, February) Cognitive changes over 15 years in a person with a learning disability, as reflected by the Wechsler intelligence scale Paper presented at the meeting of the International Neuropsychological Society, Vancouver, British Columbia McShane, D A, & Berry, J W (1989) Native North American Indians: Indians and intuitive abilities In S H.

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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 tatic and dynamic node priorities How 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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