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/*
* Copyright (c) 2021, 2022, Oracle and/or its affiliates. All rights reserved.
* DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
*
* This code is free software; you can redistribute it and/or modify it
* under the terms of the GNU General Public License version 2 only, as
* published by the Free Software Foundation.
*
* This code is distributed in the hope that it will be useful, but WITHOUT
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
* FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
* version 2 for more details (a copy is included in the LICENSE file that
* accompanied this code).
*
* You should have received a copy of the GNU General Public License version
* 2 along with this work; if not, write to the Free Software Foundation,
* Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
*
* Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
* or visit www.oracle.com if you need additional information or have any
* questions.
*/
/*
* @test
* @bug 8214761
* @key randomness
* @library /test/lib
* @build jdk.test.lib.RandomFactory
* @run testng CompensatedSums
* @summary
*/
import java.util.Random;
import java.util.function.BiConsumer;
import java.util.function.ObjDoubleConsumer;
import java.util.function.Supplier;
import java.util.stream.DoubleStream;
import jdk.test.lib.RandomFactory;
import org.testng.Assert;
import org.testng.annotations.Test;
public class CompensatedSums {
@Test
public void testCompensatedSums() {
Random r = RandomFactory.getRandom();
double naive = 0;
double jdkSequentialStreamError = 0;
double goodSequentialStreamError = 0;
double jdkParallelStreamError = 0;
double goodParallelStreamError = 0;
double badParallelStreamError = 0;
for (int loop = 0; loop < 100; loop++) {
// sequence of random numbers of varying magnitudes, both positive and negative
double[] rand = r.doubles(1_000_000)
.map(Math::log)
.map(x -> (Double.doubleToLongBits(x) % 2 == 0) ? x : -x)
.toArray();
// base case: standard Kahan summation
double[] sum = new double[2];
for (int i=0; i < rand.length; i++) {
sumWithCompensation(sum, rand[i]);
}
// All error is the squared difference of the standard Kahan Sum vs JDK Stream sum implementation
// Older less accurate implementations included here as the baseline.
// squared error of naive sum by reduction - should be large
naive += square(DoubleStream.of(rand).reduce((x, y) -> x+y).getAsDouble() - sum[0]);
// squared error of sequential sum - should be 0
jdkSequentialStreamError += square(DoubleStream.of(rand).sum() - sum[0]);
goodSequentialStreamError += square(computeFinalSum(DoubleStream.of(rand).collect(doubleSupplier,objDoubleConsumer,goodCollectorConsumer)) - sum[0]);
// squared error of parallel sum from the JDK
jdkParallelStreamError += square(DoubleStream.of(rand).parallel().sum() - sum[0]);
// squared error of parallel sum
goodParallelStreamError += square(computeFinalSum(DoubleStream.of(rand).parallel().collect(doubleSupplier,objDoubleConsumer,goodCollectorConsumer)) - sum[0]);
// the bad parallel stream
badParallelStreamError += square(computeFinalSum(DoubleStream.of(rand).parallel().collect(doubleSupplier,objDoubleConsumer,badCollectorConsumer)) - sum[0]);
}
Assert.assertTrue(jdkParallelStreamError <= goodParallelStreamError);
/*
* Due to floating-point addition being inherently non-associative,
* and due to the unpredictable scheduling of the threads used
* in parallel streams, this assertion can fail intermittently,
* hence is suppressed for now.
*/
// Assert.assertTrue(badParallelStreamError >= jdkParallelStreamError);
Assert.assertTrue(goodSequentialStreamError >= jdkSequentialStreamError);
Assert.assertTrue(naive > jdkSequentialStreamError);
Assert.assertTrue(naive > jdkParallelStreamError);
}
private static double square(double arg) {
return arg * arg;
}
// from OpenJDK 18 Collectors, unmodified
static double[] sumWithCompensation(double[] intermediateSum, double value) {
double tmp = value - intermediateSum[1];
double sum = intermediateSum[0];
double velvel = sum + tmp; // Little wolf of rounding error
intermediateSum[1] = (velvel - sum) - tmp;
intermediateSum[0] = velvel;
return intermediateSum;
}
// from OpenJDK 18 Collectors, unmodified
static double computeFinalSum(double[] summands) {
// Final sum with better error bounds subtract second summand as it is negated
double tmp = summands[0] - summands[1];
double simpleSum = summands[summands.length - 1];
if (Double.isNaN(tmp) && Double.isInfinite(simpleSum))
return simpleSum;
else
return tmp;
}
//Suppliers and consumers for Double Stream summation collection.
static Supplier<double[]> doubleSupplier = () -> new double[3];
static ObjDoubleConsumer<double[]> objDoubleConsumer = (double[] ll, double d) -> {
sumWithCompensation(ll, d);
ll[2] += d;
};
static BiConsumer<double[], double[]> badCollectorConsumer =
(ll, rr) -> {
sumWithCompensation(ll, rr[0]);
sumWithCompensation(ll, rr[1]);
ll[2] += rr[2];
};
static BiConsumer<double[], double[]> goodCollectorConsumer =
(ll, rr) -> {
sumWithCompensation(ll, rr[0]);
sumWithCompensation(ll, -rr[1]);
ll[2] += rr[2];
};
}
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