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If the chosen quote character appears within the constant, it must be escapedAs with the backtick name quoting, escaping is accomplished by replacing each quote character with two of that same quote character Rewriting the preceding examples using the alternate quote character for each demonstrates the escaping:

to the specified output stream The default size of this buffer is 512 bytes in length BufferedOutputStream (OutputStream output int bufferSize) throws javalangIllegalArgumentException creates a buffer for writing to the specified output stream, overriding the default buffer sizing The buffer is set to the specified buffer size, which must be greater than zero or an exception is thrown

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Methods No extra methods have been added to this class However, the flush() method is important to be aware of this has been overridden, and will flush the contents of a buffer, sending it immediately to the output stream it is connected to This is particularly important in networking, as a protocol request can't be sent if it is still stuck in the buffer, and the remote program may be waiting for a response 4332 DataOutputStream Class Like its sister class, DataInputStream, the DataOutputStream class is designed to deal with primitive datatypes, such as numbers or bytes Most of the read methods of DataInputStream have a corresponding write method mirrored in DataOutputStream This

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The tasks within each group are expressed as the iterations of a loop over the atoms within the molecular system We can choose a suitable algorithm structure by working through the decision tree shown earlier in Fig 42 One option is to organize the parallel algorithm in terms of the flow of data among the groups of tasks Note that only the first three task groups (the vibrational, rotational, and nonbonded force calculations) can execute concurrently; that is, they must finish computing the forces before the atomic positions, velocities and neighbor lists can be updated This is not very much concurrency to work with, so a different branch in Fig 42 should be used for this problem Another option is to derive exploitable concurrency from the set of tasks within each group, in this case the iterations of a loop over atoms This suggests an organization by tasks with a linear arrangement of tasks, or based on Fig 42, the Task Parallelism pattern should be used Total available concurrency is large (on the order of the number of atoms), providing a great deal of flexibility in designing the parallel algorithm The target machine can have a major impact on the parallel algorithm for this problem The dependencies discussed in the Data Decomposition pattern (replicated coordinates on each UE and a combination of partial sums from each UE to compute a global force array) suggest that on the order of 2 3 N terms (where N is the number of atoms) will need to be passed among the UEs The computation, however, is of order n N, where n is the number of atoms in the neighborhood of each atom and considerably less than N Hence, the communication and computation are of the same order and management of communication overhead will be a key factor in designing the algorithm.

allows developers to write datatypes to a file or other type of stream, and to have them read back by another Java application without any compatibility issues over how primitive datatypes are represented by different hardware and software platforms It implements the javaioDataOutput interface, which provides additional methods for writing primitive datatypes Constructors

These two rewritten examples are functionally identical to the preceding twoThe choice of quote character has no bearing on the outcome of the Select It is merely a matter of preference or convenience

Problem When the problem is best decomposed into a collection of tasks that can execute concurrently, how can this concurrency be exploited efficiently Context Every parallel algorithm is fundamentally a collection of concurrent tasks These tasks and any dependencies among them can be identified by inspection (for simple problems) or by application of the patterns in the Finding Concurrency design space For some problems, focusing on these tasks and their interaction might not be the best way to organize the algorithm: In some cases it makes sense to organize the tasks in terms of the data (as in the Geometric Decomposition pattern) or the flow of data among concurrent tasks (as in the Pipeline pattern) However, in many cases it is best to work directly with the tasks themselves When the design is based directly on the tasks, the algorithm is said to be a task parallel algorithm The class of task parallel algorithms is very large Examples include the following

DataOutputStream (OutputStream output) creates a data output stream,

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