[jira] [Commented] (MATH-628) use only SparseIterator, on RealVectors, that implement SpareRealVectors

["Gilles (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MATH-628?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13069531#comment-13069531 ] 

Gilles commented on MATH-628:
-----------------------------

Sorry but I don't follow: The performance test in the description seems to compare timings of the methods "getValue" and "getEntry" for two implementations of "RealVector", but then you provide a patch that modifies an "add" method...

Please, let's first sort out what should be observed, and when this shows something wrong, we'll go further to discuss a solution.

Since micro-benchmarks are often misleading, to be (somewhat) sure that we observe the same thing, I've written the above code slightly differently. Here are the results:
{noformat}
getValue
OpenMapRealVector: 0.3615042 ms
ArrayRealVector: 0.41832509999999995 ms
getEntry
OpenMapRealVector: 33.91892349999999 ms
ArrayRealVector: 5.0002195 ms
{noformat}

Does this conform with your results?
If so, please restate what conclusion you draw from them.


> use only SparseIterator, on RealVectors, that implement SpareRealVectors 
> -------------------------------------------------------------------------
>
>                 Key: MATH-628
>                 URL: https://issues.apache.org/jira/browse/MATH-628
>             Project: Commons Math
>          Issue Type: Improvement
>    Affects Versions: 3.0
>            Reporter: Arne Plöse
>            Priority: Minor
>         Attachments: ArrayRealVector.diff
>
>
> the performance suffers badly if one tries to use SparseIterator when there is no sparse backing.
> Currently there is only a check for ArrayRealvector, all other is supposed to be a SparseRealvector.
> If one creates a new implementation of RealVector ... bang.
> here is a performance test case:
> {code}
> package org.apache.commons.math.linear;
> import java.util.Iterator;
> import org.junit.Test;
> /**
>  *
>  * @author aploese
>  */
> public class PerformanceTest {
>     final static int ITER = 100;
>     final static int VECTOR_SIZE = 2048;
>     final static double SPARSE_FILL_STATE = 0.8;
>     @Test
>     public void performanceSparseVectorOpenMap() {
>         System.out.println("performanceSparseVectorOpenMap");
>         RealVector v = new OpenMapRealVector(VECTOR_SIZE);
>         for (int i = 0; i < v.getDimension() * SPARSE_FILL_STATE; i++) {
>             v.setEntry(i, i);
>         }
>         for (int j = 0; j < ITER; j++) {
>             long t1 = System.nanoTime();
>             double a = 0;
>             Iterator<RealVector.Entry> it = v.sparseIterator();
>             RealVector.Entry e;
>             while (it.hasNext() && (e = it.next()) != null) {
>                 a += e.getValue();
>             }
>             long t2 = System.nanoTime();
>             for (int i = 0; i < v.getDimension(); i++) {
>                 a += v.getEntry(i);
>             }
>             long t3 = System.nanoTime();
>             System.out.println(String.format("OpenMap: %d\t%s\t| %s", j, Long.toString(t2 - t1), Long.toString(t3 - t2)));
>         }
>     }
>     @Test
>     public void performanceSparseVectorArray() {
>         System.out.println("performanceSparseVectorArray");
>         RealVector v = new ArrayRealVector(VECTOR_SIZE);
>         for (int i = 0; i < v.getDimension() * SPARSE_FILL_STATE; i++) {
>             v.setEntry(i, i);
>         }
>         for (int j = 0; j < ITER; j++) {
>             long t1 = System.nanoTime();
>             double a = 0;
>             Iterator<RealVector.Entry> it = v.sparseIterator();
>             RealVector.Entry e;
>             while (it.hasNext() && (e = it.next()) != null) {
>                 a += e.getValue();
>             }
>             long t2 = System.nanoTime();
>             for (int i = 0; i < v.getDimension(); i++) {
>                 a += v.getEntry(i);
>             }
>             long t3 = System.nanoTime();
>             System.out.println(String.format("Array: %d\t%s\t| %s", j, Long.toString(t2 - t1), Long.toString(t3 - t2)));
>         }
>     }
> {code}
> Patch will follow.

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