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Complexity In comparison to contention aware list scheduling, the time complexity under the involvement contention model does not increase The additional scheduling of the edge on the processors increases the effort to implement the algorithm, but it does not modify its time complexity For example, the complexity of the second part of simple list scheduling (Algorithm 9) with start/ nish time minimization and the end technique is O(P(V + EO(routing))) (see Section 751) Also, the complexity does not increase for the insertion technique (Section 61), even though the individual scheduling of a node and an edge on a processor is more complex In the involvement contention model, there are more objects on the processors, namely, nodes and edges, which must be searched to nd an idle time interval On all processors there are at most O(V + E) nodes and edges; hence, the total complexity for the scheduling of the nodes increases from O(V2 ), under the contention model, to O(V(V + E)) The scheduling of the edges on the processors is O(E(V + E)), compared to O(PE) with the end technique, and on the links it remains O(PE2 O(routing)) Hence, the total complexity of the second part of simple list scheduling with start/ nish time minimization and the insertion technique is O 2 + VE + PE2 O(routing)), that is, O(V2 + PE2 O(routing)), as under the contention model (Section 751) 842 Two-Phase Heuristics The generic form of a two-phase scheduling algorithm was already outlined in Algorithm 13 The rst phase establishes the processor allocation and the second phase constructs the actual schedule with a simple list scheduling heuristic, as described in Section 52 The next paragraphs discuss how such an algorithm is applied under the involvement contention model Phase 1 Processor Allocation The processor allocation can originate from any heuristic or can be extracted from a given schedule For example, a schedule produced under the classic or contention model might serve as the input (see also Section 52) Using the rst two steps of a clustering based heuristic, that is, the clustering itself and the cluster-to-processor mapping (Algorithm 14, Section 53), is very promising, because clustering tries to minimize communication costs, which is even more important under the involvement contention model The clustering itself can be performed under the classic or, as described in Section 753, under the contention model Even the involvement contention model can be used with a straightforward extension of the considerations made in Section 753 Genetic Algorithm In Sinnen [172] and Sinnen et al [180], a genetic algorithm, called GICS (genetic involvement contention scheduling), is proposed for the determination of the processor allocation The simple idea is that the genetic algorithm searches for an ef cient processor allocation, while the actual scheduling is performed with a list scheduling heuristic as discussed later for phase 2.

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By examining users goals, we are naturally guided to an appropriate form for the application Instead of merely putting every function in a dialog box, we can see that some functions shouldn t be enclosed in a dialog at all Others should be put into a dialog that is integral to the main body of the interface, and still other functions should be completely removed from the programRelated: Intelligent Mail Generating NET.

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" Tracking down bugs known as "debugging" quickly and effectively requires developers to be familiar with a variety of tools, sometimes available from a hird party, and techniques a combination of programming techniques and techniques for using a particular tool. The .NET Framework simplifies debugging with support for Runtime diagnostics. Runtime diagnostics not only help you track down bugs but also help you determine how well your applications perform and assess the condition of your application. The .NET Framework provides three types of Runtime diagnostics: Event logging Performance counters Tracing. Code-39 Creation In Java Using Barcode generation for Java Control to .Related: 

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With charged cursor tools, users again select a ool or shape from a palette, but this time, rather than the cursor switching permanently (until the user switches again) Interaction Details Control qr bidimensional barcode image for The rest of the window is not pliant for repositioning, so the click-and-drag idiom is available for functions ithin the window, as you would expect The only hints that the window can be dragged are its color and slight dimensionality of the title bar, a subtle visual hint that is purely idiomatic (Thankfully, the idiom is very effective) In general, however, you should provide more explicit visual hinting of an area s pliancy For a title bar, you could use cursor hinting or a gripable texture as a pliancy hint To move an object, it must first be selected.

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This is why selection must take place on the mouse-down transition: The user can drag without having to first click and release on an object to select it, then click and drag it to reposition it It feels so much more natural to simply click it and then drag it to where you want it in one easy motion This creates a problem for moving contiguous data In Word, for example, Microsoft uses this clumsy click-wait-click operation to drag chunks of text You must click and drag to select a section of text, wait a second or so and click, then drag to move it This is unfortunate, but there is no good alternative for contiguous selection.

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If Microsoft were willing to dispense with its meta-key idioms for extending the selection, those same meta-keys could be used to select a sentence and drag it in a single movement, but this still wouldn t solve the problem of selecting and moving arbitrary chunks of text When repositioning, a meta-key (such as Shift) is often used to constrain the drag to a single dimension (either horizontal or vertical) This type of drag is called a constrained drag Constrained drags are extremely helpful in drawing programs, particularly when drawing neatly organized diagrams The predominant motion of the first five or ten pixels of the drag determines the angle of the drag If a user begins dragging on a predominantly horizontal axis, for example, the drag will henceforth be constrained to the horizontal axis.

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