573 lines
21 KiB
Java
573 lines
21 KiB
Java
/*
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* Copyright (c) 1999, 2020, Oracle and/or its affiliates. All rights reserved.
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* DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
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*
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* This code is free software; you can redistribute it and/or modify it
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* under the terms of the GNU General Public License version 2 only, as
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* published by the Free Software Foundation.
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*
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* This code is distributed in the hope that it will be useful, but WITHOUT
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* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
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* FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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* version 2 for more details (a copy is included in the LICENSE file that
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* accompanied this code).
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*
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* You should have received a copy of the GNU General Public License version
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* 2 along with this work; if not, write to the Free Software Foundation,
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* Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
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*
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* Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
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* or visit www.oracle.com if you need additional information or have any
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* questions.
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*/
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/*
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* @test
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* @key stress randomness
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*
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* @summary converted from VM testbase nsk/stress/numeric/numeric009.
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* VM testbase keywords: [stress, slow, nonconcurrent, quick]
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* VM testbase readme:
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* DESCRIPTION
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* This test calculates the product A*A for a square matrix A, and checks
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* if such product is calculated correctly. Elements of the matrix A are
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* initiated with integer numbers, so that A*A must be the same if calculated
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* with double, float, long, or int precision. The test just checks, if
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* double, float, long, and int variants of the product calculation result
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* in the same A*A matrix.
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* Calculation of the product A*A is iterated two times, because HotSpot
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* releases 1.0 and 1.3 seem to do not adjust JVM for better performance
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* in 1st iteration, while 2nd iteration usually runs much faster. I guess,
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* that the 1st iteration is probably executed by HotSpot interpreter, and
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* HotSpot compiler is probably involved to execute the 2nd iteration. So,
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* the test apparently checks accuracy of A*A calculation in both compilation
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* and interpretation modes.
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* By the way, the test checks JVM performance. The test is treated failed
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* due to poor performance, if 1st iteration is essentially slower than the
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* 2nd iteration. The calculations algorithm is encoded as rather compact
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* 3-levels cycle like:
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* for (int line=0; line<N; line++)
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* for (int column=0; column<N; column++) {
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* float sum = 0;
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* for (int k=0; k<N; k++)
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* sum += A[line][k] * A[k][column];
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* AA[line][column] = sum;
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* }
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* In this test, N=200, so that A is 200x200 matrix; and multiplication
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* A[line][k]*A[k][column] is executed 200**3=8 millions times in each
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* iteration of execution of this cycle. I believe, that this is HotSpot
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* bug to do not adjust JVM for best performance during such a huge series
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* of executions of the rather compact portion of program code.
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* COMMENTS
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* See the bug-report:
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* #4242172 (P3/S5) 2.0: poor performance in matrix calculations
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*
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* @library /test/lib
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* @run main/othervm nsk.stress.numeric.numeric009.numeric009 200 2
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*/
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package nsk.stress.numeric.numeric009;
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import java.io.PrintStream;
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import java.util.Random;
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import jdk.test.lib.Utils;
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/**
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* This test calculates the product <code>A<sup>.</sup>A</code> for a square
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* matrix <code>A</code>, and checks if such product is calculated correctly.
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* Elements of the matrix <code>A</code> are initiated with integer numbers,
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* so that <code>A<sup>.</sup>A</code> must be the same if calculated with
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* <code>double</code>, <code>float</code>, <code>long</code>, or
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* <code>int</code> precision. The test just checks, if <code>double</code>,
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* <code>float</code>, <code>long</code>, and <code>int</code> variants of
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* the product calculation result in the same <code>A<sup>.</sup>A</code>
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* matrix.
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* <p>
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* <p>Calculation of the product <code>A<sup>.</sup>A</code> is iterated two
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* times, because HotSpot releases 1.0 and 1.3 seem to do not adjust JVM for
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* better performance in 1<sup>st</sup> iteration, while 2<sup>nd</sup>
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* iteration usually runs much faster. I guess, that the 1<sup>st</sup> iteration
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* is probably executed by HotSpot interpreter, and HotSpot compiler is probably
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* involved to execute the 2<sup>nd</sup> iteration. So, the test apparently
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* checks accuracy of <code>A<sup>.</sup>A</code> calculation in both compilation
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* and interpretation modes.
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* <p>
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* <p>By the way, the test checks JVM performance. The test is treated failed
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* due to poor performance, if 1<sup>st</sup> iteration is essentially slower
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* than the 2<sup>nd</sup> iteration. The calculations algorithm is encoded
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* as compact ``canonical'' 3-levels cycle like:
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* <pre>
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* for (int line=0; line<N; line++)
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* for (int column=0; column<N; column++) {
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* float sum = 0;
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* for (int k=0; k<N; k++)
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* sum += A[line][k] * A[k][column];
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* AA[line][column] = sum;
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* }
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* </pre>
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* <p>
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* In this test, <code>N</code>=200, so that <code>A</code> is 200x200 matrix;
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* and multiplication <code>A[line][k]*A[k][column]</code> is executed
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* 200<sup>3</sup>=8 millions times in each iteration of execution of this
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* cycle. I believe, that this is HotSpot bug to do not adjust JVM for best
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* performance during such a huge series of executions of the rather compact
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* portion of program code.
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* <p>
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* <p>See the bug-report:
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* <br>
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* #4242172 (P3/S5) 2.0: poor performance in matrix calculations
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*/
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public class numeric009 {
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private static final Random RNG = Utils.getRandomInstance();
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/**
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* When testing performance, 1st iteration is allowed to be 10% slower
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* than 2nd iteration (<code>tolerance</code> is assigned to 10 now).
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*/
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public static double tolerance = 100; // 10;
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/**
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* Re-assign this value to <code>true</code> for better
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* diagnostics.
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*
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* @see #print(Object)
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* @see #println(Object)
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*/
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private static boolean verbose = false;
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/**
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* Stream to print execution trace and/or error messages.
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* This stream usually equals to <code>System.out</code>
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*/
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private static PrintStream out = null;
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/**
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* Print error-message.
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*
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* @see #out
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*/
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private static void complain(Object x) {
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out.println("# " + x);
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}
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/**
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* Print to execution trace, if mode is <code>verbose</code>.
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*
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* @see #verbose
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* @see #out
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*/
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private static void print(Object x) {
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if (verbose)
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out.print(x);
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}
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/**
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* Print line to execution trace, if mode is <code>verbose</code>.
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*
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* @see #verbose
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* @see #out
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*/
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private static void println(Object x) {
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print(x + "\n");
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}
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/**
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* Re-invoke <code>run(args,out)</code> in order to simulate
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* JCK-like test interface.
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*/
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public static void main(String args[]) {
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int exitCode = run(args, System.out);
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System.exit(exitCode + 95);
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// JCK-like exit status
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}
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/**
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* Parse command-line parameters stored in <code>args[]</code> and run
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* the test.
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* <p>
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* <p>Command-line parameters are:
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* <br>
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* <code>java numeric009 [-verbose] [-performance]
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* [-tolerance:<i>percents</i>]
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* <i>matrixSize</i> <i>iterations</i></code>
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* <p>
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* <p>Here:
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* <br> <code>-verbose</code> -
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* keyword alowing to print execution trace
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* <br> <code>-performance</code> -
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* keyword turning on performance testing
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* <br> <code>-tolerance</code> -
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* setup tolerance of performance checking
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* <br> <code><i>percents</i></code> -
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* 1<sup>st</sup> iteration is allowed to be
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* <code><i>percents</i></code>% slower
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* <br> <code><i>matrixSize</i></code> -
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* number of rows (and columns) in square matrix <code>A</code>
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* <br> <code><i>iterations</i></code> -
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* how many times to execute the test for stronger checking
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*
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* @param args strings array containing command-line parameters
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* @param out the test log, usually <code>System.out</code>
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*/
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public static int run(String args[], PrintStream out) {
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numeric009.out = out;
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boolean testPerformance = false;
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int numberOfCPU = 1;
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// Parse parameters starting with "-" (like: "-verbose"):
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int argsShift = 0;
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for (; argsShift < args.length; argsShift++) {
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String argument = args[argsShift];
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if (!argument.startsWith("-"))
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break;
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if (argument.equals("-performance")) {
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testPerformance = true;
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continue;
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}
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if (argument.equals("-verbose")) {
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verbose = true;
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continue;
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}
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if (argument.startsWith("-tolerance:")) {
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String percents =
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argument.substring("-tolerance:".length(), argument.length());
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tolerance = Integer.parseInt(percents);
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if ((tolerance < 0) || (tolerance > 100)) {
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complain("Tolerance should be 0 to 100%: " + argument);
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return 2; // failure
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}
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continue;
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}
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complain("Cannot recognize argument: args[" + argsShift + "]: " + argument);
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return 2; // failure
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}
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if (args.length != argsShift + 2) {
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complain("Illegal arguments. Execute:");
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complain(
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" java numeric009 [-verbose] [-performance] " +
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"[-tolerance:percents] matrixSize iterations");
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return 2; // failure
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}
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int size = Integer.parseInt(args[argsShift]);
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if ((size < 100) || (size > 10000)) {
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complain("Matrix size should be 100 to 1000 lines & columns.");
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return 2; // failure
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}
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int iterations = Integer.parseInt(args[argsShift + 1]);
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if ((iterations < 1) || (iterations > 100)) {
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complain("Iterations number should be 1 to 100.");
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return 2; // failure
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}
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print("Preparing A[" + size + "," + size + "]:");
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int[][] intA = newIntegerMatrix(size);
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int[][] intAA = new int[size][size];
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long[][] longA = newLongMatrix(intA);
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long[][] longAA = new long[size][size];
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double[][] doubleA = newDoubleMatrix(intA);
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double[][] doubleAA = new double[size][size];
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float[][] floatA = newFloatMatrix(intA);
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float[][] floatAA = new float[size][size];
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println(" done.");
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println("Should try " + iterations + " iteration(s):");
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println("==========================" +
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((iterations > 99) ? "==" : (iterations > 9) ? "=" : ""));
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println("");
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double overallTime = 0;
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double firstTime = 0;
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double lastTime = 0;
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for (int i = 1; i <= iterations; i++) {
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double seconds = elapsedTime(i,
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intA, intAA, longA, longAA, floatA, floatAA, doubleA, doubleAA);
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if (i == 1)
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firstTime = seconds;
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else
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lastTime = seconds;
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overallTime += seconds;
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print("Checking accuracy:");
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for (int line = 0; line < size; line++)
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for (int column = 0; column < size; column++) {
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if (intAA[line][column] != longAA[line][column]) {
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println("");
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complain("Test failed:");
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complain("Integer and Long results differ at:");
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complain(" line=" + line + ", column=" + column);
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return 2; // FAILED
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}
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if (intAA[line][column] != floatAA[line][column]) {
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println("");
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complain("Test failed:");
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complain("Integer and Float results differ at:");
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complain(" line=" + line + ", column=" + column);
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return 2; // FAILED
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}
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if (intAA[line][column] != doubleAA[line][column]) {
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println("");
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complain("Test failed:");
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complain("Integer and Double results differ at:");
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complain(" line=" + line + ", column=" + column);
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return 2; // FAILED
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}
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}
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println(" done.");
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}
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double averageTime = overallTime / iterations / 4;
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double averagePerformance = size * size * (size + size) / averageTime / 1e6;
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println("");
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println("=======================" +
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((iterations > 99) ? "==" : (iterations > 9) ? "=" : ""));
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println("Overall iteration(s): " + iterations);
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println("Overall elapsed time: " + overallTime + " seconds.");
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println("Average elapsed time: " + averageTime + " seconds.");
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println("Average performance: " + averagePerformance + " MFLOPS");
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if (testPerformance) {
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print("Checking performance: ");
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if (firstTime > lastTime * (1 + tolerance / 100)) {
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println("");
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complain("Test failed:");
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complain("1st iterartion is essentially slower:");
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complain("Calculation time elapsed (seconds):");
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complain(" 1-st iteration: " + firstTime);
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complain(" last iteration: " + lastTime);
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complain(" tolerance: " + tolerance + "%");
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return 2; // FAILED
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}
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println("done.");
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}
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println("Test passed.");
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return 0; // PASSED
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}
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/**
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* Return time (in seconds) elapsed for calculation of matrix
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* product <code>A*A</code> with <code>int</code>, <code>long</code>,
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* <code>float</code>, and <code>double</code> representations.
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*/
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private static double elapsedTime(int i,
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int[][] intA, int[][] intAA,
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long[][] longA, long[][] longAA,
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float[][] floatA, float[][] floatAA,
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double[][] doubleA, double[][] doubleAA) {
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int size = intA.length;
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if (i > 1)
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println("");
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println("Iteration #" + i + ":");
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print("Computing int, long, float, and double A*A:");
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long mark1 = System.currentTimeMillis();
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setSquare(intA, intAA);
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setSquare(longA, longAA);
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setSquare(floatA, floatAA);
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setSquare(doubleA, doubleAA);
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long mark2 = System.currentTimeMillis();
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println(" done.");
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double sec = (mark2 - mark1) / 1000.0;
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double perf = size * size * (size + size) / (sec / 4);
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println("Elapsed time: " + sec + " seconds");
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println("Performance: " + perf / 1e6 + " MOPS");
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return sec;
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}
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/**
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* Compute <code>A*A</code> for the given square matrix <code>A</code>.
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*/
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private static void setSquare(int[][] A, int[][] AA) {
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if (A.length != A[0].length)
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throw new IllegalArgumentException(
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"the argument matrix A should be square matrix");
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if (AA.length != AA[0].length)
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throw new IllegalArgumentException(
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"the resulting matrix AA should be square matrix");
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if (A.length != AA.length)
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throw new IllegalArgumentException(
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"the matrices A and AA should have equal size");
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int size = A.length;
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for (int line = 0; line < size; line++)
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for (int column = 0; column < size; column++) {
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int sum = 0;
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for (int k = 0; k < size; k++)
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sum += A[line][k] * A[k][line];
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AA[line][column] = sum;
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}
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}
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/**
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* Compute <code>A*A</code> for the given square matrix <code>A</code>.
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*/
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private static void setSquare(long[][] A, long[][] AA) {
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if (A.length != A[0].length)
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throw new IllegalArgumentException(
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"the argument matrix A should be square matrix");
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if (AA.length != AA[0].length)
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throw new IllegalArgumentException(
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"the resulting matrix AA should be square matrix");
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if (A.length != AA.length)
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throw new IllegalArgumentException(
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"the matrices A and AA should have equal size");
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int size = A.length;
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for (int line = 0; line < size; line++)
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for (int column = 0; column < size; column++) {
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long sum = 0;
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for (int k = 0; k < size; k++)
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sum += A[line][k] * A[k][line];
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AA[line][column] = sum;
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}
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}
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/**
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* Compute <code>A*A</code> for the given square matrix <code>A</code>.
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*/
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private static void setSquare(float[][] A, float[][] AA) {
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if (A.length != A[0].length)
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throw new IllegalArgumentException(
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"the argument matrix A should be square matrix");
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if (AA.length != AA[0].length)
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throw new IllegalArgumentException(
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"the resulting matrix AA should be square matrix");
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if (A.length != AA.length)
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throw new IllegalArgumentException(
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"the matrices A and AA should have equal size");
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int size = A.length;
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for (int line = 0; line < size; line++)
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for (int column = 0; column < size; column++) {
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float sum = 0;
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for (int k = 0; k < size; k++)
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sum += A[line][k] * A[k][line];
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AA[line][column] = sum;
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}
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}
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/**
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* Compute <code>A*A</code> for the given square matrix <code>A</code>.
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*/
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private static void setSquare(double[][] A, double[][] AA) {
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if (A.length != A[0].length)
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throw new IllegalArgumentException(
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"the argument matrix A should be square matrix");
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if (AA.length != AA[0].length)
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throw new IllegalArgumentException(
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"the resulting matrix AA should be square matrix");
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if (A.length != AA.length)
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throw new IllegalArgumentException(
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"the matrices A and AA should have equal size");
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int size = A.length;
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for (int line = 0; line < size; line++)
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for (int column = 0; column < size; column++) {
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double sum = 0;
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for (int k = 0; k < size; k++)
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sum += A[line][k] * A[k][line];
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AA[line][column] = sum;
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}
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}
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/**
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* Generate new square matrix of the given <code>size</code>
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* and with elements initiated with random numbers.
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*/
|
|
private static int[][] newIntegerMatrix(int size) {
|
|
if ((size < 1) || (size > 1000))
|
|
throw new IllegalArgumentException(
|
|
"matrix size should be 1 to 1000");
|
|
|
|
int[][] A = new int[size][size];
|
|
|
|
for (int line = 0; line < size; line++)
|
|
for (int column = 0; column < size; column++)
|
|
A[line][column] =
|
|
Math.round((float) ((1 - 2 * RNG.nextDouble()) * size));
|
|
|
|
return A;
|
|
}
|
|
|
|
/**
|
|
* Generate new square matrix with <code>long</code> elements,
|
|
* and initiate them with the same values as in the given matrix
|
|
* <code>intA</code>.
|
|
*/
|
|
private static long[][] newLongMatrix(int[][] intA) {
|
|
if (intA.length != intA[0].length)
|
|
throw new IllegalArgumentException(
|
|
"need square argument matrix");
|
|
|
|
int size = intA.length;
|
|
long[][] longA = new long[size][size];
|
|
|
|
for (int line = 0; line < size; line++)
|
|
for (int column = 0; column < size; column++)
|
|
longA[line][column] = intA[line][column];
|
|
|
|
return longA;
|
|
}
|
|
|
|
/**
|
|
* Generate new square matrix with <code>double</code> elements,
|
|
* and initiate them with the same values as in the given matrix
|
|
* <code>intA</code>.
|
|
*/
|
|
private static double[][] newDoubleMatrix(int[][] intA) {
|
|
if (intA.length != intA[0].length)
|
|
throw new IllegalArgumentException(
|
|
"need square argument matrix");
|
|
|
|
int size = intA.length;
|
|
double[][] doubleA = new double[size][size];
|
|
|
|
for (int line = 0; line < size; line++)
|
|
for (int column = 0; column < size; column++)
|
|
doubleA[line][column] = intA[line][column];
|
|
|
|
return doubleA;
|
|
}
|
|
|
|
/**
|
|
* Generate new square matrix with <code>float</code> elements,
|
|
* and initiate them with the same values as in the given matrix
|
|
* <code>intA</code>.
|
|
*/
|
|
private static float[][] newFloatMatrix(int[][] intA) {
|
|
if (intA.length != intA[0].length)
|
|
throw new IllegalArgumentException(
|
|
"need square argument matrix");
|
|
|
|
int size = intA.length;
|
|
float[][] floatA = new float[size][size];
|
|
|
|
for (int line = 0; line < size; line++)
|
|
for (int column = 0; column < size; column++)
|
|
floatA[line][column] = intA[line][column];
|
|
|
|
return floatA;
|
|
}
|
|
|
|
}
|