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Instantly convert your text to HTML . Paste your text or ... Easily remove code you don't need with two clicks. ... to HTML . OCR for PDFs containing scanned text. Proof First, Send is a feasible schedule, since the node order is a topological order according to Lemma 41 Without loss of generality, suppose that both schedules start at time unit 0 It then suf ces to show that tf ,Send (n) tf ,S (n) n V (see De nition 410 of schedule length) Since the processor allocations are identical for both schedules, communications that are remote in one schedule are also remote in the other schedule and likewise for the local communications Thus, the DRT tdr (n) of a node n can only differ between the schedules through different nish times of the predecessors (ie, through different start times) as the execution time is identical in both schedules By induction, it is shown now that ts,Send (n) ts,S (n) n V Evidently, this is true for the rst node to be scheduled, as it starts in both schedules at time unit 0 Now let Send,cur be a partial schedule of the nodes of Vcur of G, for which ts,Send,cur (n) ts,S (n) n Vcur is true For the node ni V, Vcur to be scheduled / next on processor procS (ni ) = P, it holds that tdr,Send,cur (ni , P) tdr,S (ni , P), with the above argumentation With the end technique, ni is cheduled at the end of the last node already scheduled on P But this cannot be later than in S, because the nodes on P in Send,cur are the same nodes that are executed before ni on P in S, due to the schedule order of the nodes, and, according to the assumption, no node starts later than in S Thus, ts,Send,cur (ni , P) ts,S (ni , P) for node ni By induction this is true for all nodes of the schedule, in particular, the last node, which proves the theorem. tesseract.js ocr image: Tesseract.js | Pure Javascript OCR for 100 Languages! ocr api javascript HOW TO EXTRACT TEXT FROM IMAGE USING JAVASCRIPT ( OCR ...
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PDF export with full-featured barcode support, Unicode .Related: Generate PDF417 VB.NET , Codabar Generating VB.NET , Create EAN-8 ASP.NET 535 Other Algorithms As mentioned at the beginning of this clustering section, virtually any scheduling algorithm that is suitable for an unlimited number of processors can be employed for the rst phase of the three-step clustering based scheduling Algorithm 14 For example, Hanen and Munier [86] proposed an integer linear program (ILP) formulation of scheduling a task graph on an unlimited number of processors For coarse grained task graphs, that is, g(G) 1, their relaxed ILP heuristic produces clusterings with a length that is within a factor of 4 of the optimal solution, sl(C) 3 4 3 sl(Copt ) Apart from the above mentioned MCP algorithm, Wu and Gajski [207] proposed another algorithm called MD (mobility directed), which is a list scheduling algorithm for an unlimited number of processors with dynamic node priorities, using a node insertion technique (Section 61) A clustering algorithm for heterogeneous processors was proposed by Cirou and Jeannot [36], using the concept of node triplets In a survey paper, Kwok and Ahmad [113] go as far as to classify scheduling algorithms that are designed for an unlimited number of processors into one class Grain Packing Another problem related to task graphs, the so-called grain packing problem (El-Rewini et al [65]), is essentially the same as the clustering problem Grain packing addresses the problem of how to partition a program into subtasks (grains) in order to obtain the shortest possible execution time Generally, this is the subtask decomposition step of parallel programming (Section 24) However, when subtasks are grouped together (ie, packed), grain packing rather relates, like clustering, to orchestration (Culler and Singh [48]) or agglomeration ( Foster [69]) as described in Section 23 Given a ask graph, grain packing algorithms determine how to group the nodes together into larger nodes so that the program can run in minimum time Well, that is exactly what clustering algorithms are about Some algorithms that were proposed for the grain packing problem are in fact scheduling algorithms, for example, the ISH (insertion scheduling heuristic) and the DSH (duplication scheduling heuristic) by Kruatrachue and Lewis [105, 106] A noteworthy approach in this context is a grain packing algorithm proposed by McCreary and Gill [135] Nodes are grouped into so-called clans, done by a graph partitioning algorithm (ie, a pure graph theoretic approach) As a consequence, there is no implicit schedule associated with the resulting node packing Hence, this is a grain packing algorithm, which is indeed different from clustering In effect, the algorithm changes the granularity of the task graph before it is scheduled Clustering with Node Duplication One of the objectives of clustering algorithms is the reduction of communication costs Another scheduling technique, whose. 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