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Updating the data structure is done by executing the corresponding tasks (each responsible for the update of one chunk of the data structures) concurrently If all the needed data is present at the beginning of the update operation, and if none of this data is modified during the course of the update, parallelization is easier and more likely to be efficient If the required exchange of information has been performed before beginning the update operation, the update itself is usually straightforward to implement it is essentially identical to the analogous update in an equivalent sequential program, particularly if good choices have been made about how to represent nonlocal data If the exchange and update operations overlap, more care is needed to ensure that the update is performed correctly If a system supports lightweight threads that are well integrated with the communication system, then overlap can be achieved via multithreading within a single task, with one thread computing while another handles communication In this case, synchronization between the threads is required In some systems, for example MPI, nonblocking communication is supported by matching communication primitives: one to start the communication (without blocking), and the other (blocking) to complete the operation and use the results For maximal overlap, communication should be started as soon as possible, and completed as late as possible Sometimes, operations can be reordered to allow more overlap without changing the algorithm semantics

NOTE Failure to declare a default constructor is, as the Java API documents state, an error in programming, and will result in an exception being thrown at runtime

This will return the same results as the prior exampleThe BETWEEN operator takes the standard attribute name or itemName() on the left and two constant values separated by AND on the rightThe comparisons matches are inclusive:Values are returned when they are equal to or between the constants

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The final step in designing a parallel algorithm for a problem that fits this pattern is deciding how to map the collection of tasks (each corresponding to the update of one chunk) to UEs Each UE can then be said to "own" a collection of chunks and the data they contain Thus, we have a two tiered scheme for distributing data among UEs: partitioning the data into chunks and then assigning these chunks to UEs This scheme is flexible enough to represent a variety of popular schemes for distributing data among UEs In the simplest case, each task can be statically assigned to a separate UE; then all tasks can execute concurrently, and the intertask coordination needed to implement the exchange operation is straightforward This approach is most appropriate when the computation times of the tasks are uniform and the exchange operation has been implemented to overlap communication and computation within each tasks The simple approach can lead to poor load balance in some situations, however For example, consider a linear algebra problem in which elements of the matrix are successively eliminated as the

There are some legitimate reasons, too, for not supporting serialization For example, if an object contained very sensitive information, it might be unwise to serialize it and save it to disk or send it over a network Developers should be aware that no special care is taken to protect the contents of a serialized object from scrutiny or modification, and that any class in any JVM may choose to deserialize an object at a later time Of course, boycotting the serializable interface entirely isn't always an option For example, suppose a class stored password data that could be easily obtained elsewhere and would be dangerous to serialize To prevent individual member variables being serialized, they can be marked with the transient keyword, which indicates that the object or primitive datatype must not be serialized Consider the following class, which models a user's personal account that can be serialized but whose password field may not

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