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In a loop nest, the iteration DG gains one dimension for every loop Figure 310(a) displays the double loop from Example 7 and its iteration DG Here, the approach differs from the one in Figure 39, since each node of the graph encapsulates all statements of one iteration: that is, the entire loop kernel is one task A more precise decomposition of the loop body into a task for every statement, as done with the iteration DG in Figure 39, would add one more dimension to the graph It depends on the context in which the graph is generated if this is necessary and/or desirable Note that if the entire loop body is modeled as one task, the graph is only able to re ect interiteration dependence Certain parallelization techniques, for example, software pipelining (eg, Aiken and Nicolau [8]), are based on both intra- and interiteration dependence In Section 252, the distance vectors (1, 1) and (1, 0) were identi ed for the dependence relations in the code of Figure 310(a): S(i + 1, j + 1) depends on T(i, j), and T(i + 1, j) depends on S(i, j) As with the single loop of Figure 39(a), the arrows in the iteration DG correspond directly to the dependence distance vectors; but recall that the two statements of the kernel are merged into one task, that is, one node in the graph The uniform dependence relations of the loop create a regular iteration DG For the construction of iteration dependence graphs, it is obvious that knowledge of the distance vectors of all dependence relations is crucial In other words, if the distance vector cannot be determined for every single dependence relation, the complete



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com.asprise.ocr: java - ocr - api - javalibs
A Java OCR SDK Library API allows you to perform OCR and bar code recognition on images (JPEG, PNG, TIFF, PDF, etc.) and output as plain text, xml with full ...

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Needless to say, such estimates should be approached with care [5]. Nonetheless, Eq. (9.1) already provides some qualitative insights into the queueing behavior at the multiplexer and could, in principle, be used to produce guidelines for sizing up its buffers.

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Tesseract: Open-source OCR library for Java . September 7, 2013. Weeks ago I was given a task to read values from an e-commerce website. The idea was ...

In this chapter we provide an overview of some recent work on this issue. Drastically different behaviors emerge depending on whether v* O t or v* o t t t ^ ^ (with t 3 I), where v* ln P s > t ; t 1; 2; . . . , and s is the forward t recurrence time (9.9) associated with s. The case v* O t is associated with the t service time s having exponential tails, while the case v* o t corresponds to t heavy or subexponential tails for s. Our focus here is primarily (but not exclusively) on large deviations techniques in order to obtain Eq. (9.1). This approach has already been adopted by a number of authors [10, 16, 19]. Applying results by Duf eld and O'Connell [10] (and some recent extensions thereof [11, 30]), we are able to compute h b and g under reasonably general conditions. In fact, for a large class of distributions, we can select h b vdbe , and the asymptotics (9.1) and (9.2) then take the compact form * ^ P qI > b $ P s > b g b 3 I : 9:3

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Hence, in many cases, including Weibull, lognormal, and Pareto service times, qI ^ and s (thus s) belong to the same distributional class as characterized by tail behavior. In many cases of interest, in lieu of Eq. (9.1), these large deviations techniques yield only the weaker asymptotic bounds g and lim sup

DEPENDENCE GRAPH (DG)

9:4

9:5

with g T g*. This situation typically occurs when s is heavy tailed (more generally, subexponential) with either nite or in nite E s2 , in which case large deviations excursions are only one of several causes for buffer exceedances [19]. While Eqs. (9.4) and (9.5) are still useful in providing bounds on decay rates, they will not be tight in the heavy-tail case and other approaches are needed. Of particular relevance are the approaches of Liu et al. [22] (summarized in Section 9.11) and of Likhanov (discussed in 8). Liu et al. [22] derive bounds through direct arguments that rely on the asymptotics of Pakes [26] for the GI =GI =1 queue under subexponential assumptions [12]. While Likhanov presents lower and upper bounds only when s is Pareto, these bounds are asymptotically tight. Results for the continuous-time model can be found in Jelenkovic and Lazar [17], and in s 10 and 7. Comparison of Eq. (9.3) with results from Norros [25] and Parulekar and Makowski [28] points already to the complex and subtle impact of (long-range) dependencies on the tail probability P qI > b . Indeed, in Norros [25] the input stream to the multiplexer was modeled as a fractional Gaussian noise process (or rather its continuous-time analog) exhibiting long-range dependence (in fact, selfsimilarity), and the buffer asymptotics displayed Weibull-like characteristics. On the other hand, by the results described above, an M =G=I input process with a Weibull

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Duration: 9:23 Posted: Feb 9, 2018












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