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165 lines
6.1 KiB
Java
165 lines
6.1 KiB
Java
/*
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* Copyright (c) 2025, 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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* @bug 8357530
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* @summary Test the effect of AutoVectorizationOverrideProfitability.
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* @library /test/lib /
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* @run driver compiler.loopopts.superword.TestAutoVectorizationOverrideProfitability
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*/
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package compiler.loopopts.superword;
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import compiler.lib.ir_framework.*;
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import compiler.lib.verify.*;
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import compiler.lib.generators.Generator;
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import static compiler.lib.generators.Generators.G;
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public class TestAutoVectorizationOverrideProfitability {
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public static final Generator<Integer> GEN_I = G.ints();
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public static final Generator<Float> GEN_F = G.floats();
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public static int[] aI = new int[10_000];
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public static int[] rI = new int[10_000];
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public static float[] aF = new float[10_000];
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public static float[] rF = new float[10_000];
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static {
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G.fill(GEN_I, aI);
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G.fill(GEN_F, aF);
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}
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public static void main(String[] args) throws Exception {
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// Do not vectorize, even if profitable.
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TestFramework.runWithFlags("-XX:+UnlockDiagnosticVMOptions", "-XX:AutoVectorizationOverrideProfitability=0");
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// Normal run, i.e. with normal heuristic. In some cases this vectorizes, in some not.
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// By default, we have AutoVectorizationOverrideProfitability=1
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TestFramework.run();
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// Vectorize even if not profitable.
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TestFramework.runWithFlags("-XX:+UnlockDiagnosticVMOptions", "-XX:AutoVectorizationOverrideProfitability=2");
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}
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public static final float GOLD_SIMPLE_FLOAT_REDUCTION = simpleFloatReduction();
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@Test
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@Warmup(10)
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@IR(applyIfCPUFeatureOr = {"avx", "true"},
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applyIf = {"AutoVectorizationOverrideProfitability", "= 2"},
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counts = {IRNode.ADD_REDUCTION_VF, "> 0"})
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@IR(applyIfCPUFeatureOr = {"avx", "true"},
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applyIf = {"AutoVectorizationOverrideProfitability", "< 2"},
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counts = {IRNode.ADD_REDUCTION_VF, "= 0"})
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// The simple float reduction is not profitable. We need to sequentially
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// add up the values, and so we cannot move the reduction out of the loop.
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private static float simpleFloatReduction() {
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float sum = 0;
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for (int i = 0; i < aF.length; i++) {
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sum += aF[i];
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}
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return sum;
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}
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@Check(test="simpleFloatReduction")
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public static void checkSimpleFloatReduction(float result) {
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Verify.checkEQ(GOLD_SIMPLE_FLOAT_REDUCTION, result);
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}
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static { simpleFloatCopy(); }
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public static final float[] GOLD_SIMPLE_FLOAT_COPY = rF.clone();
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@Test
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@Warmup(10)
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@IR(applyIfCPUFeatureOr = {"avx", "true"},
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applyIf = {"AutoVectorizationOverrideProfitability", "> 0"},
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counts = {IRNode.LOAD_VECTOR_F, "> 0"})
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@IR(applyIfCPUFeatureOr = {"avx", "true"},
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applyIf = {"AutoVectorizationOverrideProfitability", "= 0"},
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counts = {IRNode.LOAD_VECTOR_F, "= 0"})
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// The simple float copy is always profitable.
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private static void simpleFloatCopy() {
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for (int i = 0; i < aF.length; i++) {
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rF[i] = aF[i];
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}
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}
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@Check(test="simpleFloatCopy")
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public static void checkSimpleFloatCopy() {
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Verify.checkEQ(GOLD_SIMPLE_FLOAT_COPY, rF);
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}
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public static final int GOLD_SIMPLE_INT_REDUCTION = simpleIntReduction();
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@Test
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@Warmup(10)
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@IR(applyIfCPUFeatureOr = {"avx", "true"},
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applyIf = {"AutoVectorizationOverrideProfitability", "= 2"},
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counts = {IRNode.ADD_REDUCTION_VI, "> 0", IRNode.ADD_VI, "> 0"})
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@IR(applyIfCPUFeatureOr = {"avx", "true"},
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applyIf = {"AutoVectorizationOverrideProfitability", "< 2"},
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counts = {IRNode.ADD_REDUCTION_VI, "= 0", IRNode.ADD_VI, "= 0"})
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// Current heuristics say that this simple int reduction is not profitable.
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// But it would actually be profitable, since we are able to move the
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// reduction out of the loop (we can reorder the reduction). When moving
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// the reduction out of the loop, we instead accumulate with a simple
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// ADD_VI inside the loop.
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// See: JDK-8307516 JDK-8345044
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private static int simpleIntReduction() {
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int sum = 0;
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for (int i = 0; i < aI.length; i++) {
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sum += aI[i];
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}
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return sum;
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}
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@Check(test="simpleIntReduction")
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public static void checkSimpleIntReduction(int result) {
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Verify.checkEQ(GOLD_SIMPLE_INT_REDUCTION, result);
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}
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static { simpleIntCopy(); }
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public static final int[] GOLD_SIMPLE_INT_COPY = rI.clone();
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@Test
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@Warmup(10)
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@IR(applyIfCPUFeatureOr = {"avx", "true"},
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applyIf = {"AutoVectorizationOverrideProfitability", "> 0"},
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counts = {IRNode.LOAD_VECTOR_I, "> 0"})
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@IR(applyIfCPUFeatureOr = {"avx", "true"},
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applyIf = {"AutoVectorizationOverrideProfitability", "= 0"},
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counts = {IRNode.LOAD_VECTOR_I, "= 0"})
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// The simple int copy is always profitable.
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private static void simpleIntCopy() {
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for (int i = 0; i < aI.length; i++) {
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rI[i] = aI[i];
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}
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}
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@Check(test="simpleIntCopy")
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public static void checkSimpleIntCopy() {
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Verify.checkEQ(GOLD_SIMPLE_INT_COPY, rI);
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}
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}
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