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their own routing algorithms Hence, their produced schedules can only be accurate and ef cient if the target parallel system can be directed to use the computed routes However, that is rarely the case in a generic parallel system, especially at the application level (Culler and Singh [48]) Fortunately, using the target system s own routing algorithm does not imply that for every target system a different routing algorithm must be implemented in scheduling Most parallel computers employ minimal routing, which means they choose the shortest possible path, in terms of number of edges, through the network for every communication An example is dimension ordered routing in a multidimensional mesh (Culler and Singh [48], Kumar et al [108]) A message is rst routed along one dimension, then along the second dimension, and so on until it reaches its destination Given the graph based representation of the network, nding a shortest path can be accomplished with a BFS (breadth rst search Algorithm 1) Thus, the BFS can be used as a generic routing algorithm in the topology graph, which serves, at least, as a good approximation in many cases Shortest Path in Topology Graph with BFS Although BFS is an algorithm for directed and undirected graphs, it can readily be applied to the topology graph The only graph concept used in BFS is that of adjacency (in the for loop, Algorithm 1) As this is already de ned for directed and undirected edges (Section 31), it only remains to de ne adjacency for hyperedges De nition 74 (Adjacency Hyperedge) Let V be a nite set of vertices and H a nite set of hyperedges A vertex u V, u H, H H is adjacent to all vertices v H-u and all vertices v H-u are adjacent to u The set H H:u H H-u of all vertices adjacent to u V is denoted by adj(u) Now in the topology graph, the total set of all vertices adjacent to a given vertex u is the union of the two adjacent sets induced by the directed edges and the hyperedges So with this de nition of vertex adjacency, the BFS can be applied to the topology graph without any modi cation and returns a shortest path in terms of number of edges Complexity As routing depends on the algorithm of the target parallel system, there is no general time complexity expression that is valid for all etworks On that account, the routing complexity shall be denoted generically by O(routing) The algorithm for routing in regular networks is usually linear in the number of network vertices or even of constant time For example, in a fully connected network it is obviously O(1), as it is in a network with one central switch (Figure 77(b)) In a topology graph for a mesh network of any dimension (Section 221), it is at most linear in the number of processors O(P) Whenever it is possible to calculate the routes for a given system once and then to store them; for example, in a table, O(routing) is just the complexity of the length of the route For example, in a ring network the length of a route (ie, the number of links) is O(P); hence, O(routing) O(P).

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ASSESSMENT FRAMEWORKS in .NET Encode Quick Response Code in .NET SSESSMENT FRAMEWORKS.At the general level, however, computer-based simulation can bene t from a number of improvements related to the computerisation of tests There are two main ways in which computerisation has undoubtedly improved assessment practice The rst is in increased ef ciency in the delivery and administration of assessment materials and the second is in increased exibility Increased ef ciency can be achieved in a number of ways: Quicker, and more accurate, scoring You only have to consider the volume of testing reported by Melnick and Clauser to appreciate how expensive and time consuming the hand-marking of test papers can be Providing different types of score, some of which would be dif cult or impossible to produce otherwise For example, Hornke and Kersting refer to formula scores, maximum likelihood scores, person t indices and others that are only feasible with the advent of computerised scoring Adaptive testing, which, in the right circumstances, can give equally reliable and valid results for igni cantly less testing effort Hornke and Kersting report time savings of up to 25% Item banks and form generation can cut down hugely on the time taken to produce parallel versions of tests More rapid evaluation of test quality Likewise, exibility can be achieved in a number of ways: On-demand testing Wider distribution of assessment sites and accommodation Greater variety of answer and response formats Greater variety of content presentation formats with greater control of the presentation. Data Matrix 2d Barcode Drawer In VS .NET Using .Related: C# QR Code Generation , Create EAN 128 .NET WinForms , Creating EAN-8 VB.NET

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