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/*
* Copyright (c) 2025, Red Hat, Inc. 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 8307513
* @summary [SuperWord] MaxReduction and MinReduction should vectorize for long
* @library /test/lib /
* @run driver compiler.loopopts.superword.MinMaxRed_Long
*/
package compiler.loopopts.superword;
import compiler.lib.ir_framework.*;
import jdk.test.lib.Utils;
import java.util.Arrays;
import java.util.Random;
import java.util.stream.LongStream;
public class MinMaxRed_Long {
private static final Random random = Utils.getRandomInstance();
public static void main(String[] args) throws Exception {
TestFramework framework = new TestFramework();
framework.addFlags("-XX:+IgnoreUnrecognizedVMOptions",
"-XX:LoopUnrollLimit=250",
"-XX:CompileThresholdScaling=0.1");
framework.start();
}
@Run(test = {"maxReductionImplement"},
mode = RunMode.STANDALONE)
public void runMaxTest() {
runMaxTest(50);
runMaxTest(80);
runMaxTest(100);
}
private static void runMaxTest(int probability) {
long[] longs = reductionInit(probability);
long res = 0;
for (int j = 0; j < 2000; j++) {
res = maxReductionImplement(longs, res);
}
if (res == 11 * Arrays.stream(longs).max().getAsLong()) {
System.out.println("Success");
} else {
throw new AssertionError("Failed");
}
}
@Run(test = {"minReductionImplement"},
mode = RunMode.STANDALONE)
public void runMinTest() {
runMinTest(50);
runMinTest(80);
runMinTest(100);
}
private static void runMinTest(int probability) {
long[] longs = reductionInit(probability);
// Negating the values generated for controlling max branching
// allows same logic to be used for min tests.
longs = negate(longs);
long res = 0;
for (int j = 0; j < 2000; j++) {
res = minReductionImplement(longs, res);
}
if (res == 11 * Arrays.stream(longs).min().getAsLong()) {
System.out.println("Success");
} else {
throw new AssertionError("Failed");
}
}
static long[] negate(long[] nums) {
return LongStream.of(nums).map(l -> -l).toArray();
}
public static long[] reductionInit(int probability) {
int aboveCount, abovePercent;
long[] longs = new long[1024];
// Generates an array of numbers such that as the array is iterated
// there is P probability of finding a new max value,
// and 100-P probability of not finding a new max value.
// The algorithm loops around if the distribution does not match the probability,
// but it approximates the probability as the array sizes increase.
// The worst case of this algorithm is when the desired array size is 100
// and the aim is to get 50% of probability, which can only be satisfied
// with 50 elements being a new max. This situation can take 15 rounds.
// As sizes increase, say 10'000 elements,
// the number of elements that have to satisfy 50% increases,
// so the algorithm will stop as an example when 5027 elements are a new max values.
// Also, probability values in the edges will achieve their objective quicker,
// with 0% or 100% probability doing it in a single loop.
// To support the same algorithm for min calculations,
// negating the array elements achieves the same objective.
do {
long max = random.nextLong(10);
longs[0] = max;
aboveCount = 0;
for (int i = 1; i < longs.length; i++) {
long value;
if (random.nextLong(101) <= probability) {
long increment = random.nextLong(10);
value = max + increment;
aboveCount++;
} else {
// Decrement by at least 1
long diffToMax = random.nextLong(10) + 1;
value = max - diffToMax;
}
longs[i] = value;
max = Math.max(max, value);
}
abovePercent = ((aboveCount + 1) * 100) / longs.length;
} while (abovePercent != probability);
return longs;
}
@Test
@IR(applyIfAnd = {"SuperWordReductions", "true", "MaxVectorSize", ">=32"},
applyIfCPUFeatureOr = {"avx512", "true", "asimd" , "true"},
counts = {IRNode.MIN_REDUCTION_V, " > 0"})
public static long minReductionImplement(long[] a, long res) {
for (int i = 0; i < a.length; i++) {
final long v = 11 * a[i];
res = Math.min(res, v);
}
return res;
}
@Test
@IR(applyIfAnd = {"SuperWordReductions", "true", "MaxVectorSize", ">=32"},
applyIfCPUFeatureOr = {"avx512", "true", "asimd" , "true"},
counts = {IRNode.MAX_REDUCTION_V, " > 0"})
public static long maxReductionImplement(long[] a, long res) {
for (int i = 0; i < a.length; i++) {
final long v = 11 * a[i];
res = Math.max(res, v);
}
return res;
}
}
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