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linguist/samples/X10/HeatTransfer_v1.x10
2015-08-24 13:26:43 -04:00

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/*
* This file is part of the X10 project (http://x10-lang.org).
*
* This file is licensed to You under the Eclipse Public License (EPL);
* You may not use this file except in compliance with the License.
* You may obtain a copy of the License at
* http://www.opensource.org/licenses/eclipse-1.0.php
*
* (C) Copyright IBM Corporation 2006-2014.
*/
import x10.array.*;
import x10.compiler.Foreach;
import x10.util.Team;
/**
* This is a sample program illustrating how to use
* X10's distributed array classes. It also illustrates the use
* of foreach to achieve intra-place parallelism and the mixture
* of APGAS finish/async/at with Team collective operations.
*
* This version of the program uses a vanilla DistArray without
* ghost regions. As a result, the stencil function does
* inefficient fine-grained neighbor communication to get individual values.
* Compare this to HeatTransfer_v2 which utilizes ghost regions and
* bulk ghost-region exchange functions.
*
* The program solves a set of 2D partial differential
* equations by iteratively applying a 5-point stencil
* operation until convergence is reached.
*/
public class HeatTransfer_v1 {
static val EPSILON = 1.0e-5;
val N:Long;
val A:DistArray_BlockBlock_2[Double]{self!=null};
val Tmp:DistArray_BlockBlock_2[Double]{self!=null};
public def this(size:Long) {
N = size;
val init = (i:Long, j:Long)=>i==0 ? 1.0 : 0.0;
A = new DistArray_BlockBlock_2[Double](N+2, N+2, init);
Tmp = new DistArray_BlockBlock_2[Double](N+2, N+2, init);
}
final def stencil(x:Long, y:Long):Double {
val cls = (dx:Long, dy:Long)=>{
val p = A.place(x+dx, y+dy);
p == here ? A(x+dx,y+dy) : at (p) A(x+dx,y+dy)
};
val tmp = cls(-1,0) + cls(1,0) + cls(0,-1) + cls(0,1);
return tmp / 4;
}
def run() {
val myTeam = new Team(A.placeGroup());
finish for (p in A.placeGroup()) at (p) async {
// Compute the subset of the local indices on which
// we want to apply the stencil (the interior points of the N+2 x N+2 grid)
val li = A.localIndices();
val interior = new DenseIterationSpace_2(li.min(0) == 0 ? 1 : li.min(0),
li.min(1) == 0 ? 1 : li.min(1),
li.max(0) == N+1 ? N : li.max(0),
li.max(1) == N+1 ? N : li.max(1));
var delta:Double;
do {
// Compute new values, storing in tmp
val myDelta = Foreach.blockReduce(interior,
(i:Long, j:Long)=>{
Tmp(i,j) = stencil(i,j);
// Reduce max element-wise delta (A now holds previous values)
return Math.abs(Tmp(i,j) - A(i,j));
},
(a:Double, b:Double)=>Math.max(a,b), 0.0
);
myTeam.barrier();
// Unlike Array, DistArray doesn't provide an optimized swap.
// So, until it does, we have to copy the data elements.
Foreach.block(interior, (i:Long, j:Long)=>{
A(i,j) = Tmp(i,j);
});
delta = myTeam.allreduce(myDelta, Team.MAX);
} while (delta > EPSILON);
}
}
def prettyPrintResult() {
for (i in 1..N) {
for (j in 1..N) {
val x = at (A.place(i,j)) A(i,j);
Console.OUT.printf("%1.4f ", x);
}
Console.OUT.println();
}
}
public static def main(args:Rail[String]) {
val n = args.size > 0 ? Long.parse(args(0)) : 8;
Console.OUT.println("HeatTransfer example with N="+n+" and epsilon="+EPSILON);
Console.OUT.println("Initializing data structures");
val ht = new HeatTransfer_v1(n);
Console.OUT.println("Beginning computation...");
val start = System.nanoTime();
ht.run();
val stop = System.nanoTime();
Console.OUT.printf("...completed in %1.3f seconds.\n", ((stop-start) as double)/1e9);
if (n <= 10) {
ht.prettyPrintResult();
}
}
}