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The schedule (static) clause decomposes the iterations of the parallel loop into one contiguous block per thread with each block being approximately the same size This schedule is important for Loop Parallelism programs implementing the Geometric Decomposition pattern Good performance for most Geometric Decomposition problems (and mesh programs in particular) requires that the data in the processor's cache be used many times before it is displaced by data from new cache lines Using large blocks of contiguous loop iterations increases the chance that multiple values fetched in a cache line will be utilized and that subsequent loop iterations are likely to find at least some of the required data in cache The last detail to discuss for the program in Fig 412 is the synchronization required to safely copy the pointers It is essential that all of the threads complete their work before the pointers they manipulate are swapped in preparation for the next iteration In this program, this synchronization happens automatically due to the implied barrier (see Sec 632) at the end of the parallel loop The program in Fig 412 works well with a small number of threads When large numbers of threads are involved, however, the overhead incurred by placing the thread creation and destruction inside the loop over k would be prohibitive We can reduce thread management overhead by splitting the parallel for directive into separate parallel and for directives and moving the thread creation outside the loop over k This approach is shown in Fig 413 Because the whole k loop is now inside a parallel region, we must be more careful about how data is shared between threads The private clause causes the loop indices k and i to be local to each thread The pointers uk and ukp1 are shared, however, so the swap operation must be protected The easiest way to do this is to ensure that only one member of the team of threads does the swap In OpenMP, this is most easily done by placing the update inside a single construct As described in more detail in the OpenMP appendix, Appendix A, the first thread to encounter the construct will carry out the swap while the other threads wait at the end of the construct.

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ObjectOutputStream (OutputStream output) throws javaio IOException creates an object output stream capable of serializing objects to the

#include <stdioh> #include <stdlibh> #define NX 100 #define LEFTVAL 10 #define RIGHTVAL 100 #define NSTEPS 10000 void initialize(double uk[], double ukp1[]) { uk[0] = LEFTVAL; uk[NX-1] = RIGHTVAL; for (int i = 1; i < NX-1; ++i) uk[i] = 00; for (int i=0; i < NX; ++i) ukp1[i] = uk[i]; } void printValues(double uk[], int step) { /* NOT SHOWN */ } int main(void) {

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Methods Like the ObjectInputStream class, ObjectOutputStream offers extra methods that may be used by subclasses to implement object serialization However, this is a complex task and is rarely done For this reason, only the most important method, used for writing objects, is covered here for further in formation, the Java API documentation should be consulted Readers are reminded that the ObjectOutputStream class also provides method implementations for the DataOutput interface these methods are covered in Section 4332 on DataOutputStream, and may be used to write primitive data types to a stream

/* pointers to arrays for two iterations of algorithm */ double *uk = malloc(sizeof(double) * NX); double *ukp1 = malloc(sizeof(double) * NX); double *temp; double dx = 10/NX; double dt = 05*dx*dx; initialize(uk, ukp1); for (int k = 0; k < NSTEPS; ++k) { #pragma omp parallel for schedule(static) /* compute new values */ for (int i = 1; i < NX-1; ++i) { ukp1[i]=uk[i]+ (dt/(dx*dx))*(uk[i+1]-2*uk[i]+uk[i-1]); } /* "copy" ukp1 to uk by swapping pointers */ temp = ukp1; ukpl = uk; uk = temp; printValues(uk, k); } return 0; }

void writeObject (Object object) throws javaioIOException javaioInvalidClassException, javaioNotSerializableException writes the specified object to the output

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You can also place the wildcard character at the beginning:

An MPI based program for this example is shown in Fig 414 and 415 The approach used in this program uses a data distribution with ghost cells and the SPMD pattern Each process is given a single chunk of the data domain of size NX/NP, where NX is the total size of the global data array and NP is the number of processes For simplicity, we assume NX is evenly divided by NP The update of the chunk is straightforward and essentially identical to that from the sequential code The length and greater complexity in this MPI program arises from two sources First, the data initialization is more complex, because it must account for the data values at the edges of the first and last chunks Second, message passing routines are required inside the loop over k to exchange ghost cells

static will be written, providing the specified class is an instance of the javaioSerializable interface

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4543 Serialization Example Let's look at an example of an application that can save and restore state information by using the Java serialization classes Code for SerializationDemo

Figure 413 Parallel heat-diffusion program using OpenMP This version has less thread-management overhead

import javaio*; import javautil*; // 4, Listing 5 public class SerializationDemo { public static void main(String args[]) { try { Vector list; // Create a buffered reader for easy input BufferedReader reader = new BufferedReader ( new InputStreamReader ( Systemin ) ); Systemoutprintln ("Checking for previous serialized list"); // Check to see if serialized list exists try { FileInputStream fin = new FileInputStream ("listout"); // Connect an object input stream to the // list ObjectInputStream oin = new ObjectInputStream ( fin ); try

Finally, it can be placed at both the beginning and the end:

#define RIGHTVAL 100 #define NSTEPS 10000 void initialize(double uk[], double ukp1[]){/* NOT SHOWN */} void printValues(double uk[], int step) { /* NOT SHOWN */ } int main(void) { /* pointers to arrays for two iterations of algorithm */ double *uk = malloc(sizeof(double) * NX); double *ukp1 = malloc(sizeof(double) * NX); double *temp; int i,k; double dx = 10/NX; double dt = 05*dx*dx; #pragma omp parallel private (k, i) { initialize(uk, ukp1); for (k = 0; k < NSTEPS; ++k) { #pragma omp for schedule(static) for (i = 1; i < NX-1; ++i) { ukp1[i]=uk[i]+ (dt/(dx*dx))*(uk[i+1]-2*uk[i]+uk[i-1]); } /* "copy" ukp1 to uk by swapping pointers */ #pragma omp single { temp = ukp1; ukp1 = uk; uk = temp; } } } return 0; }

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