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The last chapter concluded withreturned itemsThishow to call , that discussion the mechanics of how to handle the errors, and how to access the chapter continues

In other designs, the interaction between tasks is not so chronologically regular An example is the discrete event simulation described previously, in which the events that lead to interaction between tasks can be chronologically irregular Are the tasks grouped in the best way The temporal relations are easy: Tasks that can run at the same time are naturally grouped together But an effective design will also group tasks together based on their logical relationship in the overall problem As an example of grouping tasks, consider the molecular dynamics problem discussed in the Examples section of the Group Tasks, Order Tasks, and Data Sharing patterns The grouping we eventually arrive at (in the Group Tasks pattern) is hierarchical: groups of related tasks based on the high level operations of the problem, further grouped on the basis of which ones can execute concurrently Such an approach makes it easier to reason about whether the design meets the necessary constraints (because the constraints can be stated in terms of the task groups defined by the high level operations) while allowing for scheduling flexibility

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A number of output streams are available in the javaio package for a variety of tasks, such as writing to data structures including strings and arrays, or to files or communication pipes As seen in Table 42, six important low-level output streams may be written to (in addition to filter streams that may be connected to these low-level streams) As mentioned earlier, there are other streams which may be written to that developers cannot create and instantiate directly, but that nonetheless will be encountered For example, a networking stream such as a connection to a TCP service is not created directly, but is provided by invoking the appropriate method on a socket These types of streams will be covered in later chapters, as appropriate Output streams work somewhat differently from input streams, as might reasonably be expected due to their different purposes While an input stream is a data consumer, an output stream is a data producer it literally creates bytes of information and transmits them to something else (such as a file or data structure or network connection) Like input streams, data is communicated sequentially; that is, the first byte in will be the first byte out (see Figure 4-6) This approach is analogous to a FIFO queue, in which data that is sent may be read back in order, without regard to sequencing Unlike some specialized filter input streams, which allow you to "go back n" bytes within a sequence, once data is sent to an output stream it cannot be undone Consider it as like an e-mail message once sent you cannot (no matter how much you may want to) take it back Figure 4-6 Output streams send data one byte at a time, sequentially

int done[MAX]; // an array of flags dimensioned for the // boundary data // // Create Team of Threads // init (Nsize, Bsize, field, boundary, incoming); #pragma omp parallel shared(boundary, field, Bsize, Nsize) // // Set up the SPMD program Note: by declaring ID and Num_threads // inside the parallel region, we make them private to each thread // int ID, nprocs, i, left; ID = omp_get_thread_num(); nprocs = omp_get_num_threads(); if (nprocs > MAX) { exit (-1); } // // assume a ring of processors and a communication pattern // where boundaries are shifted to the right // left = (ID-1); if(left<0) left = nprocs-1; done[ID] = 0; // set flag stating "buffer ready to fill" for(i = 0; i < Nsteps; i++){ #pragma omp barrier // all visible variables flushed, so we // don't need to flush "done" extract_boundary(Nsize, Bsize, ID, num_procs, boundary[ID]);

with a walkthrough of the query language used in Select The chapter starts by walking through the five clauses that make up the Select query language; some are required or conditionally required Following that is the section on data formatting Nowhere in SimpleDB is the storage format of your data more important than in the realm of queriesWe then get into the nitty-gritty details of what SimpleDB allows when forming expressions and predicatesThe comparisons and operators and how they interrelate is the real heart of SelectThe chapter concludes with a look at query performance: how to measure it, how to improve it, and how to avoid the common query pitfalls No two people have the same level of technical backgroundTo account for this, the chapter follows a logical progression and is filled with small query examples Seasoned SQL veterans will be in familiar territory with this query language and may easily skim over the simpler bits by scanning the titles and examples Be aware, however, that despite the similar syntax, there are differences

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