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Can the computational load be evenly balanced among the PEs This is easier if the tasks are independent, or if they are roughly the same size Is the overhead minimized Overhead can come from several sources, including creation and scheduling of the UEs, communication, and synchronization Creation and scheduling of UEs involves overhead, so each UE needs to have enough work to do to justify this overhead On the other hand, more UEs allow for better load balance Communication can also be a source of significant overhead, particularly on distributed memory platforms that depend on message passing As we discussed in Section 26, the time to transfer a message has two components: latency cost arising from operating system overhead and message start up costs on the network, and a cost that scales with the length of the message To minimize the latency costs, the number of messages to be sent should be kept to a minimum In other words, a small number of large messages is better than a large number of small ones The second term is related to the bandwidth of the network These costs can sometimes be hidden by overlapping communication with computation On shared memory machines, synchronization is a major source of overhead When data is shared between UEs, dependencies arise requiring one task to wait for another to avoid race conditions The synchronization mechanisms used to control this waiting are expensive compared to many operations carried out by a UE Furthermore, some synchronization constructs generate significant memory traffic as they flush caches, buffers, and other system resources to make sure UEs see a consistent view of memory This extra memory traffic can interfere with the explicit data movement within a computation Synchronization overhead can be reduced by keeping data well localized to a task, thereby minimizing the frequency of synchronization operations.

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4212 Using a Low-Level Input Stream Below we examine a practical application of using a low-level input stream to display the contents of a file A byte at a time is read from the file and displayed to the screen Though not the most efficient way to do this (performance could be sped up by buffering, for example), it is illustrative of how low-level streams can be read from Code for FileInputStreamDemo

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$sdb = new AmazonSDB(); $select = $sdb->select('SELECT * FROM users'); if ($select->isOK()) { print_select($select); } else { echo 'select failed: '; $error = $select->body->Errors->Error; echo $error->Code ': ' $error->Message; } function print_select($select) { foreach ($select->body->SelectResult->Item as $item) { echo $item->Name PHP_EOL; foreach ($item->Attribute as $attr) { echo "\t" $attr->Value PHP_EOL; } } } The code for multiple pages follows: $select = "SELECT Rating, Make, Supplier" " FROM products" " WHERE Rating > '85'" " AND ProductType = 'Cigar'" " ORDER BY Rating DESC" " LIMIT 2"; $sdb = new AmazonSDB(); $opt = null; do { $select_result = $sdb->select($select, $opt); if ($select_result->isOK()) { $next_token = $select_result->body->SelectResult->NextToken;

Simplicity To paraphrase Einstein: Make it as simple as possible, but not simpler Keep in mind that practically all programs will eventually need to be debugged, maintained, and often enhanced and ported A design even a generally superior design is not valuable if it is too hard to debug, maintain, and verify the correctness of the final program The medical imaging example initially described in Sec 313 and then discussed further in the Task Decomposition and Data Decomposition patterns is an excellent case in point in support of the value

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import javaio*; // 4, Listing 1 public class FileInputStreamDemo { public static void main(String args[]) {

print_select($select_result); } else { echo 'select failed: '; $error = $select_result->body->Errors->Error; echo $error->Code ': ' $error->Message; } $opt = array('NextToken' => $next_token); } while ($next_token != null);

if (argslength != 1) { Systemerrprintln ("Syntax - FileInputStreamDemo file"); return; } try { // Create an input stream, reading from the // specified file InputStream fileInput = new FileInputStream ( args[0] ); // Read the first byte of data int data = fileInputread(); // Repeat : until end of file (EOF) reached while (data != -1) { // Send byte to standard output Systemoutwrite ( data ); // Read next byte data = fileInputread(); } // Close the file fileInputclose(); } catch (IOException ioe) { Systemerrprintln ("I/O error - " + ioe); } } }

of simplicity In this problem, a large database could be decomposed, but this decomposition would force the parallel algorithm to include complex operations for passing trajectories between UEs and to distribute chunks of the database This complexity makes the resulting program much more difficult to understand and greatly complicates debugging The other approach, replicating the database, leads to a vastly simpler parallel program in which completely independent tasks can be passed out to multiple workers as they are read All complex communication thus goes away, and the parallel part of the program is trivial to debug and reason about

How FileInputStreamDemo Works The code for this example is quite simple to follow, and shows just how easy it is to use input streams in Java Aside from error checking (such as the presence of a command-line parameter and exception handling), very little code is required to work with streams There is a single line of code to create the initial FileInputStream object, which takes as a parameter a string representation of the location of a file

The problem decomposition carried out with the Finding Concurrency patterns defines the key components that will guide the design in the Algorithm Structure design space:

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