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addition) Each thread sums its values into the local copy Following the close of the parallel region, all the local copies are combined with the global copy to produce the final value Because we need to compute the average time, we need to know how many threads were used in the parallel region The only way to safely determine the number of threads is by a call to the omp_num_threads () function inside the parallel region Multiple writes to the same variable can interfere with each other, so we place the call to omp_num_threads () inside a single construct, thereby ensuring that only one thread will assign a value to the shared variable num_threads A single in OpenMP implies a barrier, so we do not need to explicitly include one to make sure all the threads enter the timed code at the same time Finally, note that we compute only the average time in Fig 617 because min and max operators are not included in the C/C++ OpenMP version 20

SELECT * FROM vendors WHERE `year-to-date-billing` > '2147508648'

void unread(char[] charArray, int offset, int length) throws javaioIOException pushes a subset of the characters in the specified array into

Figure 616 MPI program to time the execution of a function called runit() We use MPI_Reduce to find minimum, maximum, and average runtimes

The LineNumberReader class provides a useful line counter, which measures how many lines have been read It is the writer equivalent of the LineNumberInputStream As it extends the BufferedReader class, it also supports the mark/reset operations Constructors

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No one reading that query without a calculator is going to know what the value is If there is an arithmetic error, it is unlikely to be caught by someone reviewing the codeAlthough it is not possible to preserve readability for all values, we can tweak our offset value to make the positive numbers readable If you take the minimum offset, with lengths of 10 and 19 characters, and round it up to the next digit, you get a much friendlier numberThe 10-digit 2147483648 becomes the 11-digit 10000000000, and the same technique applies to 64-bit values Here is the base query repeated from the previous example:

At some level of recursion, the amount of computation required for a subproblems can become so small that it is not worth the overhead of creating a new task to solve it In this case, a hybrid program that creates new tasks at the higher levels of recursion, then switches to a sequential solution when the subproblems become smaller than some threshold, will be more effective As discussed next, there are tradeoffs involved in choosing the threshold, which will depend on the specifics of the problem and the number of PEs available Thus, it is a good idea to design the program so that this "granularity knob" is easy to change

LineNumberReader (Reader reader) creates a new line-number reader LineNumberReader (Reader reader int size) creates a new line-number

SELECT * FROM vendors WHERE `year-to-date-billing` > '25000'

Conceptually, this pattern follows a straightforward fork/join approach (see the Fork/Join pattern) One task splits the problem, then forks new tasks to compute the subproblems, waits until the subproblems are computed, and then joins with the subtasks to merge the results The easiest situation is when the split phase generates subproblems that are known to be about the same size in terms of needed computation Then, a straightforward implementation of the fork/join strategy, mapping each task to a UE and stopping the recursion when the number of active subtasks is the same as the number of PEs, works well In many situations, the problem will not be regular, and it is best to create more, finer grained tasks and use a master/worker structure to map tasks to units of execution This implementation of this approach is described in detail in the Master/Worker pattern The basic idea is to conceptually maintain a queue of tasks and a pool of UEs, typically one per PE When a subproblem is split, the new tasks are placed in the queue When a UE finishes a task, it obtains another one from the queue In this way, all of the UEs tend to remain busy, and the solution shows a good load balance Finer grained tasks allow a better load balance at the cost of more overhead for task management Many parallel programming environments directly support the fork/join construct For example, in OpenMP, we could easily produce a parallel application by turning the for loop of Fig 46 into an OpenMP parallel for construct Then the subproblems will be solved concurrently rather than

int getLineNumber() returns the current line number void setLineNumber(int lineNumber) modifies the line-number counter

in sequence, with the OpenMP runtime environment handling the thread management Unfortunately, this technique will only work with implementations of OpenMP that support true nesting of parallel regions Currently, only a few OpenMP implementations do so Extending OpenMP to better address recursive parallel algorithms is an active area of research in the OpenMP community [Mat03] One proposal likely to be adopted in a future OpenMP specification is to add an explicit taskqueue construct designed to support the expression of recursive algorithms [SHPT00] The FJTask framework for Java [Lea00b, Lea] provides support for fork/join programs with a pool of threads backing the implementation Several example programs using a divide and conquer strategy are provided with the package

Going from an output stream to a writer is not a difficult task An equivalent class to javaioOutputStream, the javaioWriter class has similar method signatures and supports Unicode characters 4461 The javaioWriter Class Constructors There are no public constructors for this class Instead, a writer subclass should be instantiated Methods The following methods are included in this class; all are public

The constant must now be converted into a new format and the result is almost as easy to understand:

void close() throws javaioIOException invokes the flush() method to send any buffered data, and then closes the writer

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