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486 lines
20 KiB
Java
486 lines
20 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 id=vanilla
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* @bug 8373026
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* @summary Test auto vectorization and Vector API with some vector
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* algorithms. Related benchmark: VectorAlgorithms.java
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* @library /test/lib /
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* @modules jdk.incubator.vector
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* @run driver ${test.main.class} vanilla
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*/
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/*
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* @test id=noSuperWord
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* @bug 8373026
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* @library /test/lib /
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* @modules jdk.incubator.vector
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* @run driver ${test.main.class} noSuperWord
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*/
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/*
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* @test id=noOptimizeFill
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* @bug 8373026
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* @library /test/lib /
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* @modules jdk.incubator.vector
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* @run driver ${test.main.class} noOptimizeFill
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*/
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package compiler.vectorization;
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import java.util.Map;
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import java.util.HashMap;
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import jdk.test.lib.Utils;
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import java.util.Random;
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import java.lang.foreign.*;
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import compiler.lib.ir_framework.*;
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import compiler.lib.generators.*;
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import static compiler.lib.generators.Generators.G;
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import compiler.lib.verify.*;
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/**
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* The goal of this benchmark is to show the power of auto vectorization
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* and the Vector API.
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*
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* Please only modify this benchark in synchronization with the JMH benchmark:
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* micro/org/openjdk/bench/vm/compiler/VectorAlgorithms.java
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*/
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public class TestVectorAlgorithms {
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private static final Random RANDOM = Utils.getRandomInstance();
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private static final RestrictableGenerator<Integer> INT_GEN = Generators.G.ints();
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interface TestFunction {
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Object run();
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}
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Map<String, Map<String, TestFunction>> testGroups = new HashMap<String, Map<String, TestFunction>>();
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int[] aI;
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int[] rI1;
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int[] rI2;
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int[] rI3;
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int[] rI4;
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int eI;
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int[] oopsX4;
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int[] memX4;
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public static void main(String[] args) {
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TestFramework framework = new TestFramework();
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framework.addFlags("--add-modules=jdk.incubator.vector", "-XX:CompileCommand=inline,*VectorAlgorithmsImpl::*");
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switch (args[0]) {
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case "vanilla" -> { /* no extra flags */ }
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case "noSuperWord" -> { framework.addFlags("-XX:-UseSuperWord"); }
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case "noOptimizeFill" -> { framework.addFlags("-XX:-OptimizeFill"); }
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default -> { throw new RuntimeException("Test argument not recognized: " + args[0]); }
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}
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framework.start();
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}
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public TestVectorAlgorithms () {
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// IMPORTANT:
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// If you want to use some array but do NOT modify it: just use it.
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// If you want to use it and DO want to modify it: clone it. This
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// ensures that each test gets a separate copy, and that when we
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// capture the modified arrays they are different for every method
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// and run.
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// An alternative to cloning is to use different return arrays for
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// different implementations of the same group, e.g. rI1, rI2, ...
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testGroups.put("fillI", new HashMap<String,TestFunction>());
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testGroups.get("fillI").put("fillI_loop", () -> { return fillI_loop(rI1); });
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testGroups.get("fillI").put("fillI_VectorAPI", () -> { return fillI_VectorAPI(rI1); });
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testGroups.get("fillI").put("fillI_Arrays", () -> { return fillI_Arrays(rI1); });
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testGroups.put("iotaI", new HashMap<String,TestFunction>());
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testGroups.get("iotaI").put("iotaI_loop", () -> { return iotaI_loop(rI1); });
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testGroups.get("iotaI").put("iotaI_VectorAPI", () -> { return iotaI_VectorAPI(rI1); });
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testGroups.put("copyI", new HashMap<String,TestFunction>());
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testGroups.get("copyI").put("copyI_loop", () -> { return copyI_loop(aI, rI1); });
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testGroups.get("copyI").put("copyI_VectorAPI", () -> { return copyI_VectorAPI(aI, rI1); });
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testGroups.get("copyI").put("copyI_System_arraycopy", () -> { return copyI_System_arraycopy(aI, rI1); });
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testGroups.put("mapI", new HashMap<String,TestFunction>());
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testGroups.get("mapI").put("mapI_loop", () -> { return mapI_loop(aI, rI1); });
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testGroups.get("mapI").put("mapI_VectorAPI", () -> { return mapI_VectorAPI(aI, rI1); });
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testGroups.put("reduceAddI", new HashMap<String,TestFunction>());
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testGroups.get("reduceAddI").put("reduceAddI_loop", () -> { return reduceAddI_loop(aI); });
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testGroups.get("reduceAddI").put("reduceAddI_reassociate", () -> { return reduceAddI_reassociate(aI); });
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testGroups.get("reduceAddI").put("reduceAddI_VectorAPI_naive", () -> { return reduceAddI_VectorAPI_naive(aI); });
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testGroups.get("reduceAddI").put("reduceAddI_VectorAPI_reduction_after_loop", () -> { return reduceAddI_VectorAPI_reduction_after_loop(aI); });
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testGroups.put("scanAddI", new HashMap<String,TestFunction>());
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testGroups.get("scanAddI").put("scanAddI_loop", () -> { return scanAddI_loop(aI, rI1); });
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testGroups.get("scanAddI").put("scanAddI_loop_reassociate", () -> { return scanAddI_loop_reassociate(aI, rI2); });
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testGroups.get("scanAddI").put("scanAddI_VectorAPI_permute_add", () -> { return scanAddI_VectorAPI_permute_add(aI, rI4); });
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testGroups.put("findMinIndexI", new HashMap<String,TestFunction>());
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testGroups.get("findMinIndexI").put("findMinIndexI_loop", () -> { return findMinIndexI_loop(aI); });
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testGroups.get("findMinIndexI").put("findMinIndexI_VectorAPI", () -> { return findMinIndexI_VectorAPI(aI); });
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testGroups.put("findI", new HashMap<String,TestFunction>());
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testGroups.get("findI").put("findI_loop", () -> { return findI_loop(aI, eI); });
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testGroups.get("findI").put("findI_VectorAPI", () -> { return findI_VectorAPI(aI, eI); });
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testGroups.put("reverseI", new HashMap<String,TestFunction>());
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testGroups.get("reverseI").put("reverseI_loop", () -> { return reverseI_loop(aI, rI1); });
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testGroups.get("reverseI").put("reverseI_VectorAPI", () -> { return reverseI_VectorAPI(aI, rI2); });
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testGroups.put("filterI", new HashMap<String,TestFunction>());
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testGroups.get("filterI").put("filterI_loop", () -> { return filterI_loop(aI, rI1, eI); });
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testGroups.get("filterI").put("filterI_VectorAPI", () -> { return filterI_VectorAPI(aI, rI2, eI); });
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testGroups.put("reduceAddIFieldsX4", new HashMap<String,TestFunction>());
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testGroups.get("reduceAddIFieldsX4").put("reduceAddIFieldsX4_loop", () -> { return reduceAddIFieldsX4_loop(oopsX4, memX4); });
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testGroups.get("reduceAddIFieldsX4").put("reduceAddIFieldsX4_VectorAPI", () -> { return reduceAddIFieldsX4_VectorAPI(oopsX4, memX4); });
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}
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@Warmup(100)
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@Run(test = {"fillI_loop",
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"fillI_VectorAPI",
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"fillI_Arrays",
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"iotaI_loop",
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"iotaI_VectorAPI",
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"copyI_loop",
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"copyI_VectorAPI",
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"copyI_System_arraycopy",
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"mapI_loop",
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"mapI_VectorAPI",
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"reduceAddI_loop",
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"reduceAddI_reassociate",
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"reduceAddI_VectorAPI_naive",
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"reduceAddI_VectorAPI_reduction_after_loop",
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"scanAddI_loop",
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"scanAddI_loop_reassociate",
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"scanAddI_VectorAPI_permute_add",
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"findMinIndexI_loop",
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"findMinIndexI_VectorAPI",
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"findI_loop",
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"findI_VectorAPI",
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"reverseI_loop",
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"reverseI_VectorAPI",
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"filterI_loop",
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"filterI_VectorAPI",
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"reduceAddIFieldsX4_loop",
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"reduceAddIFieldsX4_VectorAPI"})
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public void runTests(RunInfo info) {
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// Repeat many times, so that we also have multiple iterations for post-warmup to potentially recompile
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int iters = info.isWarmUp() ? 1 : 20;
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for (int iter = 0; iter < iters; iter++) {
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// Set up random inputs, random size is important to stress tails.
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int size = 100_000 + RANDOM.nextInt(10_000);
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aI = new int[size];
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G.fill(INT_GEN, aI);
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// Pick some random element. Most of the time it is in aI, sometimes not.
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eI = (RANDOM.nextInt(10) == 0) ? RANDOM.nextInt() : aI[RANDOM.nextInt(size)];
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//for (int i = 0; i < aI.length; i++) { aI[i] = i; }
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rI1 = new int[size];
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rI2 = new int[size];
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rI3 = new int[size];
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rI4 = new int[size];
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// X4 oop setup.
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oopsX4 = new int[size];
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int numX4 = 10_000;
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for (int i = 0; i < size; i++) {
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// assign either a zero=null, or assign a random oop.
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oopsX4[i] = (RANDOM.nextInt(10) == 0) ? 0 : RANDOM.nextInt(numX4) * 4;
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}
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// Just fill the whole array with random values.
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// The relevant field is only at every "4 * i + 3" though.
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memX4 = new int[4 * numX4];
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for (int i = 0; i < 4 * numX4; i++) {
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memX4[i] = RANDOM.nextInt();
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}
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// Run all tests
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for (Map.Entry<String, Map<String,TestFunction>> group_entry : testGroups.entrySet()) {
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String group_name = group_entry.getKey();
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Map<String, TestFunction> group = group_entry.getValue();
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Object gold = null;
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String gold_name = "NONE";
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for (Map.Entry<String,TestFunction> entry : group.entrySet()) {
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String name = entry.getKey();
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TestFunction test = entry.getValue();
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Object result = test.run();
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if (gold == null) {
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gold = result;
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gold_name = name;
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} else {
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try {
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Verify.checkEQ(gold, result);
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} catch (VerifyException e) {
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throw new RuntimeException("Verify.checkEQ failed for group " + group_name +
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", gold " + gold_name + ", test " + name, e);
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}
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}
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}
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}
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}
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}
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@Test
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@IR(counts = {IRNode.REPLICATE_I, "= 1",
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IRNode.STORE_VECTOR, "> 0"},
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applyIfCPUFeatureOr = {"sse4.1", "true", "asimd", "true"},
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applyIfAnd = {"UseSuperWord", "true", "OptimizeFill", "false"})
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@IR(counts = {".*CallLeafNoFP.*arrayof_jint_fill.*", "= 1"},
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phase = CompilePhase.BEFORE_MATCHING,
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applyIfCPUFeatureOr = {"sse4.1", "true", "asimd", "true"},
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applyIf = {"OptimizeFill", "true"})
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// By default, the fill intrinsic "arrayof_jint_fill" is used, but we can disable
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// the detection of the fill loop, and then we auto vectorize.
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public Object fillI_loop(int[] r) {
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return VectorAlgorithmsImpl.fillI_loop(r);
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}
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@Test
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@IR(counts = {IRNode.REPLICATE_I, "= 1",
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IRNode.STORE_VECTOR, "> 0"},
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applyIfCPUFeatureOr = {"sse4.1", "true", "asimd", "true"})
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public Object fillI_VectorAPI(int[] r) {
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return VectorAlgorithmsImpl.fillI_VectorAPI(r);
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}
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@Test
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// Arrays.fill is not necessarily inlined, so we can't check
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// for vectors in the IR.
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public Object fillI_Arrays(int[] r) {
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return VectorAlgorithmsImpl.fillI_Arrays(r);
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}
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@Test
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@IR(counts = {IRNode.POPULATE_INDEX, "> 0",
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IRNode.STORE_VECTOR, "> 0"},
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applyIfCPUFeatureOr = {"avx2", "true", "sve", "true"},
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applyIf = {"UseSuperWord", "true"})
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// Note: the Vector API example below can also vectorize for AVX,
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// because it does not use a PopulateIndex.
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public Object iotaI_loop(int[] r) {
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return VectorAlgorithmsImpl.iotaI_loop(r);
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}
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@Test
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@IR(counts = {IRNode.ADD_VI, "> 0",
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IRNode.STORE_VECTOR, "> 0"},
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applyIfCPUFeatureOr = {"sse4.1", "true", "asimd", "true"})
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public Object iotaI_VectorAPI(int[] r) {
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return VectorAlgorithmsImpl.iotaI_VectorAPI(r);
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}
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@Test
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@IR(counts = {IRNode.LOAD_VECTOR_I, "> 0",
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IRNode.STORE_VECTOR, "> 0"},
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applyIfCPUFeatureOr = {"sse4.1", "true", "asimd", "true"},
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applyIf = {"UseSuperWord", "true"})
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public Object copyI_loop(int[] a, int[] r) {
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return VectorAlgorithmsImpl.copyI_loop(a, r);
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}
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@Test
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@IR(counts = {IRNode.LOAD_VECTOR_I, "> 0",
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IRNode.STORE_VECTOR, "> 0"},
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applyIfCPUFeatureOr = {"sse4.1", "true", "asimd", "true"})
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public Object copyI_VectorAPI(int[] a, int[] r) {
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return VectorAlgorithmsImpl.copyI_VectorAPI(a, r);
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}
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@Test
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@IR(counts = {".*CallLeafNoFP.*arrayof_jint_disjoint_arraycopy.*", "= 1"},
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phase = CompilePhase.BEFORE_MATCHING,
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applyIfCPUFeatureOr = {"sse4.1", "true", "asimd", "true"})
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public Object copyI_System_arraycopy(int[] a, int[] r) {
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return VectorAlgorithmsImpl.copyI_System_arraycopy(a, r);
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}
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@Test
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@IR(counts = {IRNode.LOAD_VECTOR_I, "> 0",
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IRNode.MUL_VI, "> 0",
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IRNode.STORE_VECTOR, "> 0"},
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applyIfCPUFeatureOr = {"sse4.1", "true", "asimd", "true"},
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applyIf = {"UseSuperWord", "true"})
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public Object mapI_loop(int[] a, int[] r) {
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return VectorAlgorithmsImpl.mapI_loop(a, r);
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}
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@Test
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@IR(counts = {IRNode.LOAD_VECTOR_I, "> 0",
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IRNode.MUL_VI, "> 0",
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IRNode.STORE_VECTOR, "> 0"},
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applyIfCPUFeatureOr = {"sse4.1", "true", "asimd", "true"})
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public Object mapI_VectorAPI(int[] a, int[] r) {
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return VectorAlgorithmsImpl.mapI_VectorAPI(a, r);
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}
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@Test
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@IR(counts = {IRNode.LOAD_VECTOR_I, "> 0",
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IRNode.ADD_REDUCTION_VI, "> 0",
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IRNode.ADD_VI, "> 0"},
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applyIfCPUFeatureOr = {"sse4.1", "true", "asimd", "true"},
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applyIf = {"UseSuperWord", "true"})
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public int reduceAddI_loop(int[] a) {
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return VectorAlgorithmsImpl.reduceAddI_loop(a);
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}
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@Test
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public int reduceAddI_reassociate(int[] a) {
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return VectorAlgorithmsImpl.reduceAddI_reassociate(a);
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}
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@Test
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@IR(counts = {IRNode.LOAD_VECTOR_I, "> 0",
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IRNode.ADD_REDUCTION_VI, "> 0"}, // reduceLanes inside loop
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applyIfCPUFeatureOr = {"sse4.1", "true", "asimd", "true"})
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public int reduceAddI_VectorAPI_naive(int[] a) {
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return VectorAlgorithmsImpl.reduceAddI_VectorAPI_naive(aI);
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}
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@Test
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@IR(counts = {IRNode.LOAD_VECTOR_I, "> 0",
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IRNode.ADD_REDUCTION_VI, "> 0",
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IRNode.ADD_VI, "> 0"},
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applyIfCPUFeatureOr = {"sse4.1", "true", "asimd", "true"})
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public int reduceAddI_VectorAPI_reduction_after_loop(int[] a) {
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return VectorAlgorithmsImpl.reduceAddI_VectorAPI_reduction_after_loop(aI);
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}
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@Test
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@IR(counts = {IRNode.LOAD_VECTOR_I, "= 0",
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IRNode.STORE_VECTOR, "= 0"})
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// Currently does not vectorize, but might in the future.
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public Object scanAddI_loop(int[] a, int[] r) {
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return VectorAlgorithmsImpl.scanAddI_loop(a, r);
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}
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@Test
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public Object scanAddI_loop_reassociate(int[] a, int[] r) {
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return VectorAlgorithmsImpl.scanAddI_loop_reassociate(a, r);
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}
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@Test
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@IR(counts = {IRNode.LOAD_VECTOR_I, "> 0",
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IRNode.REARRANGE_VI, "> 0",
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IRNode.AND_VI, "> 0",
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IRNode.ADD_VI, "> 0",
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IRNode.STORE_VECTOR, "> 0"},
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applyIfCPUFeatureOr = {"sse4.1", "true", "asimd", "true"},
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applyIf = {"MaxVectorSize", ">=64"})
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public Object scanAddI_VectorAPI_permute_add(int[] a, int[] r) {
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return VectorAlgorithmsImpl.scanAddI_VectorAPI_permute_add(a, r);
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}
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@Test
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@IR(counts = {IRNode.LOAD_VECTOR_I, "= 0"})
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// Currently does not vectorize, but might in the future.
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public int findMinIndexI_loop(int[] a) {
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return VectorAlgorithmsImpl.findMinIndexI_loop(a);
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}
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@Test
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@IR(counts = {IRNode.LOAD_VECTOR_I, "> 0",
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IRNode.VECTOR_MASK_CMP, "> 0",
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IRNode.VECTOR_BLEND_I, "> 0",
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IRNode.MIN_REDUCTION_V, "> 0",
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IRNode.ADD_VI, "> 0"},
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applyIfCPUFeatureOr = {"avx", "true", "asimd", "true"})
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public int findMinIndexI_VectorAPI(int[] a) {
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return VectorAlgorithmsImpl.findMinIndexI_VectorAPI(a);
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}
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@Test
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@IR(counts = {IRNode.LOAD_VECTOR_I, "= 0"})
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// Currently does not vectorize, but might in the future.
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public int findI_loop(int[] a, int e) {
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return VectorAlgorithmsImpl.findI_loop(a, e);
|
|
}
|
|
|
|
@Test
|
|
@IR(counts = {IRNode.LOAD_VECTOR_I, "> 0",
|
|
IRNode.VECTOR_MASK_CMP, "> 0",
|
|
IRNode.VECTOR_TEST, "> 0"},
|
|
applyIfCPUFeatureOr = {"avx", "true", "asimd", "true"})
|
|
public int findI_VectorAPI(int[] a, int e) {
|
|
return VectorAlgorithmsImpl.findI_VectorAPI(a, e);
|
|
}
|
|
|
|
@Test
|
|
@IR(counts = {IRNode.LOAD_VECTOR_I, "= 0",
|
|
IRNode.STORE_VECTOR, "= 0"})
|
|
// Currently does not vectorize, but might in the future.
|
|
public Object reverseI_loop(int[] a, int[] r) {
|
|
return VectorAlgorithmsImpl.reverseI_loop(a, r);
|
|
}
|
|
|
|
@Test
|
|
@IR(counts = {IRNode.LOAD_VECTOR_I, "> 0",
|
|
IRNode.REARRANGE_VI, "> 0",
|
|
IRNode.AND_VI, "> 0",
|
|
IRNode.STORE_VECTOR, "> 0"},
|
|
applyIfCPUFeatureOr = {"sse4.1", "true", "asimd", "true"})
|
|
public Object reverseI_VectorAPI(int[] a, int[] r) {
|
|
return VectorAlgorithmsImpl.reverseI_VectorAPI(a, r);
|
|
}
|
|
|
|
@Test
|
|
@IR(counts = {IRNode.LOAD_VECTOR_I, "= 0",
|
|
IRNode.STORE_VECTOR, "= 0"})
|
|
public Object filterI_loop(int[] a, int[] r, int threshold) {
|
|
return VectorAlgorithmsImpl.filterI_loop(a, r, threshold);
|
|
}
|
|
|
|
@Test
|
|
@IR(counts = {IRNode.LOAD_VECTOR_I, "> 0",
|
|
IRNode.VECTOR_MASK_CMP, "> 0",
|
|
IRNode.VECTOR_TEST, "> 0",
|
|
IRNode.VECTOR_LONG_TO_MASK, "> 0",
|
|
IRNode.STORE_VECTOR_MASKED, "> 0"},
|
|
applyIfCPUFeatureOr = {"avx", "true", "asimd", "true"})
|
|
public Object filterI_VectorAPI(int[] a, int[] r, int threshold) {
|
|
return VectorAlgorithmsImpl.filterI_VectorAPI(a, r, threshold);
|
|
}
|
|
|
|
@Test
|
|
@IR(counts = {IRNode.LOAD_VECTOR_I, "= 0"})
|
|
// Currently does not vectorize, but might in the future.
|
|
public int reduceAddIFieldsX4_loop(int[] oops, int[] mem) {
|
|
return VectorAlgorithmsImpl.reduceAddIFieldsX4_loop(oops, mem);
|
|
}
|
|
|
|
@Test
|
|
@IR(counts = {IRNode.LOAD_VECTOR_I, "> 0",
|
|
IRNode.VECTOR_MASK_CMP, "> 0",
|
|
IRNode.VECTOR_TEST, "> 0",
|
|
IRNode.LOAD_VECTOR_GATHER_MASKED, "> 0",
|
|
IRNode.OR_V_MASK, "> 0",
|
|
IRNode.ADD_VI, "> 0",
|
|
IRNode.ADD_REDUCTION_VI, "> 0"},
|
|
applyIfCPUFeatureOr = {"avx512", "true", "sve", "true"})
|
|
public int reduceAddIFieldsX4_VectorAPI(int[] oops, int[] mem) {
|
|
return VectorAlgorithmsImpl.reduceAddIFieldsX4_VectorAPI(oops, mem);
|
|
}
|
|
}
|