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an ideal world, however, and software designed without considering the major features of the target platform is unlikely to run efficiently The primary issue is how many units of execution (UEs) the system will effectively support, because an algorithm that works well for ten UEs may not work well for hundreds of UEs It is not necessary to decide on a specific number (in fact to do so would overly constrain the applicability of the design), but it is important to have in mind at this point an order of magnitude for the number of UEs Another issue is how expensive it is to share information among UEs If there is hardware support for shared memory, information exchange takes place through shared access to common memory, and frequent data sharing makes sense If the target is a collection of nodes connected by a slow network, however, the communication required to share information is very expensive and must be avoided wherever possible When thinking about both of these issues the number of UEs and the cost of sharing information avoid the tendency to over constrain the design Software typically outlives hardware, so over the course of a program's life it may be used on a tremendous range of target platforms The goal is to obtain a design that works well on the original target platform, but at the same time is flexible enough to adapt to different classes of hardware Finally, in addition to multiple UEs and some way to share information among them, a parallel computer has one or more programming environments that can be used to implement parallel algorithms Different programming environments provide different ways to create tasks and share information among UEs, and a design that does not map well onto the characteristics of the target programming environment will be difficult to implement 422 Major Organizing Principle When considering the concurrency in the problem, is there a particular way of looking at it that stands out and provides a high level mechanism for organizing this concurrency The analysis carried out using the patterns of the Finding Concurrency design space describes the potential concurrency in terms of tasks and groups of tasks, data (both shared and task local), and ordering constraints among task groups The next step is to find an algorithm structure that represents how this concurrency maps onto the UEs There is usually a major organizing principle implied by the concurrency This usually falls into one of three camps: organization by tasks, organization by data decomposition, and organization by flow of data We now consider each of these in more detail For some problems, there is really only one group of tasks active at one time, and the way the tasks within this group interact is the major feature of the concurrency Examples include so called embarrassingly parallel programs in which the tasks are completely independent, as well as programs in which the tasks in a single group cooperate to compute a result For other problems, the way data is decomposed and shared among tasks stands out as the major way to organize the concurrency For example, many problems focus on the update of a few large data structures, and the most productive way to think about the concurrency is in terms of how this structure is decomposed and distributed among UEs Programs to solve differential equations or carry out linear algebra computations often fall into this category because they are frequently based on updating large data structures.

4323 LineNumberInputStream Class This class provides helpful functionality by tracking the number of lines read from an input stream It is deprecated as of JDK11, however, since the preferred way to process text data is to use a reader class Also, line numbers are not very serviceable in terms of a stream of bytes Nonetheless, if writing for JDK102 systems, it may be useful Constructors

resultgetItems()get("Domain")getAttribute("Count")

.

LineNumberInputStream(InputStream input) creates a line number stream,

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