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/*
* Copyright (c) 2022, Arm Limited. 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.
*/
package compiler.vectorapi;
import compiler.lib.ir_framework.*;
import java.util.Random;
import jdk.incubator.vector.ByteVector;
import jdk.incubator.vector.ShortVector;
import jdk.incubator.vector.IntVector;
import jdk.incubator.vector.LongVector;
import jdk.incubator.vector.VectorSpecies;
import jdk.test.lib.Asserts;
import jdk.test.lib.Utils;
/**
* @test
* @bug 8275275
* @key randomness
* @library /test/lib /
* @requires os.arch=="aarch64"
* @summary AArch64: Fix performance regression after auto-vectorization on NEON
* @modules jdk.incubator.vector
*
* @run driver compiler.vectorapi.TestVectorMulAddSub
*/
public class TestVectorMulAddSub {
private static final VectorSpecies<Byte> B_SPECIES = ByteVector.SPECIES_MAX;
private static final VectorSpecies<Short> S_SPECIES = ShortVector.SPECIES_MAX;
private static final VectorSpecies<Integer> I_SPECIES = IntVector.SPECIES_MAX;
private static final VectorSpecies<Long> L_SPECIES = LongVector.SPECIES_MAX;
private static int LENGTH = 1024;
private static final Random RD = Utils.getRandomInstance();
private static byte[] ba;
private static byte[] bb;
private static byte[] bc;
private static byte[] br;
private static short[] sa;
private static short[] sb;
private static short[] sc;
private static short[] sr;
private static int[] ia;
private static int[] ib;
private static int[] ic;
private static int[] ir;
private static long[] la;
private static long[] lb;
private static long[] lc;
private static long[] lr;
static {
ba = new byte[LENGTH];
bb = new byte[LENGTH];
bc = new byte[LENGTH];
br = new byte[LENGTH];
sa = new short[LENGTH];
sb = new short[LENGTH];
sc = new short[LENGTH];
sr = new short[LENGTH];
ia = new int[LENGTH];
ib = new int[LENGTH];
ic = new int[LENGTH];
ir = new int[LENGTH];
la = new long[LENGTH];
lb = new long[LENGTH];
lc = new long[LENGTH];
lr = new long[LENGTH];
for (int i = 0; i < LENGTH; i++) {
ba[i] = (byte) RD.nextInt();
bb[i] = (byte) RD.nextInt();
bc[i] = (byte) RD.nextInt();
sa[i] = (short) RD.nextInt();
sb[i] = (short) RD.nextInt();
sc[i] = (short) RD.nextInt();
ia[i] = RD.nextInt();
ib[i] = RD.nextInt();
ic[i] = RD.nextInt();
la[i] = RD.nextLong();
lb[i] = RD.nextLong();
lc[i] = RD.nextLong();
}
}
@Test
@IR(counts = {IRNode.VMLA, "> 0"})
public static void testByteMulAdd() {
for (int i = 0; i < LENGTH; i += B_SPECIES.length()) {
ByteVector av = ByteVector.fromArray(B_SPECIES, ba, i);
ByteVector bv = ByteVector.fromArray(B_SPECIES, bb, i);
ByteVector cv = ByteVector.fromArray(B_SPECIES, bc, i);
av.add(bv.mul(cv)).intoArray(br, i);
}
}
@Run(test = "testByteMulAdd")
public static void testByteMulAdd_runner() {
testByteMulAdd();
for (int i = 0; i < LENGTH; i++) {
Asserts.assertEquals((byte) (ba[i] + bb[i] * bc[i]), br[i]);
}
}
@Test
@IR(counts = {IRNode.VMLA, "> 0"})
public static void testShortMulAdd() {
for (int i = 0; i < LENGTH; i += S_SPECIES.length()) {
ShortVector av = ShortVector.fromArray(S_SPECIES, sa, i);
ShortVector bv = ShortVector.fromArray(S_SPECIES, sb, i);
ShortVector cv = ShortVector.fromArray(S_SPECIES, sc, i);
av.add(bv.mul(cv)).intoArray(sr, i);
}
}
@Run(test = "testShortMulAdd")
public static void testShortMulAdd_runner() {
testShortMulAdd();
for (int i = 0; i < LENGTH; i++) {
Asserts.assertEquals((short) (sa[i] + sb[i] * sc[i]), sr[i]);
}
}
@Test
@IR(counts = {IRNode.VMLA, "> 0"})
public static void testIntMulAdd() {
for (int i = 0; i < LENGTH; i += I_SPECIES.length()) {
IntVector av = IntVector.fromArray(I_SPECIES, ia, i);
IntVector bv = IntVector.fromArray(I_SPECIES, ib, i);
IntVector cv = IntVector.fromArray(I_SPECIES, ic, i);
av.add(bv.mul(cv)).intoArray(ir, i);
}
}
@Run(test = "testIntMulAdd")
public static void testIntMulAdd_runner() {
testIntMulAdd();
for (int i = 0; i < LENGTH; i++) {
Asserts.assertEquals((ia[i] + ib[i] * ic[i]), ir[i]);
}
}
@Test
@IR(applyIf = {"UseSVE", " > 0"}, counts = {IRNode.VMLA, "> 0"})
public static void testLongMulAdd() {
for (int i = 0; i < LENGTH; i += L_SPECIES.length()) {
LongVector av = LongVector.fromArray(L_SPECIES, la, i);
LongVector bv = LongVector.fromArray(L_SPECIES, lb, i);
LongVector cv = LongVector.fromArray(L_SPECIES, lc, i);
av.add(bv.mul(cv)).intoArray(lr, i);
}
}
@Run(test = "testLongMulAdd")
public static void testLongMulAdd_runner() {
testLongMulAdd();
for (int i = 0; i < LENGTH; i++) {
Asserts.assertEquals((la[i] + lb[i] * lc[i]), lr[i]);
}
}
@Test
@IR(counts = {IRNode.VMLS, "> 0"})
public static void testByteMulSub() {
for (int i = 0; i < LENGTH; i += B_SPECIES.length()) {
ByteVector av = ByteVector.fromArray(B_SPECIES, ba, i);
ByteVector bv = ByteVector.fromArray(B_SPECIES, bb, i);
ByteVector cv = ByteVector.fromArray(B_SPECIES, bc, i);
av.sub(bv.mul(cv)).intoArray(br, i);
}
}
@Run(test = "testByteMulSub")
public static void testByteMulSub_runner() {
testByteMulSub();
for (int i = 0; i < LENGTH; i++) {
Asserts.assertEquals((byte) (ba[i] - bb[i] * bc[i]), br[i]);
}
}
@Test
@IR(counts = {IRNode.VMLS, "> 0"})
public static void testShortMulSub() {
for (int i = 0; i < LENGTH; i += S_SPECIES.length()) {
ShortVector av = ShortVector.fromArray(S_SPECIES, sa, i);
ShortVector bv = ShortVector.fromArray(S_SPECIES, sb, i);
ShortVector cv = ShortVector.fromArray(S_SPECIES, sc, i);
av.sub(bv.mul(cv)).intoArray(sr, i);
}
}
@Run(test = "testShortMulSub")
public static void testShortMulSub_runner() {
testShortMulSub();
for (int i = 0; i < LENGTH; i++) {
Asserts.assertEquals((short) (sa[i] - sb[i] * sc[i]), sr[i]);
}
}
@Test
@IR(counts = {IRNode.VMLS, "> 0"})
public static void testIntMulSub() {
for (int i = 0; i < LENGTH; i += I_SPECIES.length()) {
IntVector av = IntVector.fromArray(I_SPECIES, ia, i);
IntVector bv = IntVector.fromArray(I_SPECIES, ib, i);
IntVector cv = IntVector.fromArray(I_SPECIES, ic, i);
av.sub(bv.mul(cv)).intoArray(ir, i);
}
}
@Run(test = "testIntMulSub")
public static void testIntMulSub_runner() {
testIntMulSub();
for (int i = 0; i < LENGTH; i++) {
Asserts.assertEquals((ia[i] - ib[i] * ic[i]), ir[i]);
}
}
@Test
@IR(applyIf = {"UseSVE", " > 0"}, counts = {IRNode.VMLS, "> 0"})
public static void testLongMulSub() {
for (int i = 0; i < LENGTH; i += L_SPECIES.length()) {
LongVector av = LongVector.fromArray(L_SPECIES, la, i);
LongVector bv = LongVector.fromArray(L_SPECIES, lb, i);
LongVector cv = LongVector.fromArray(L_SPECIES, lc, i);
av.sub(bv.mul(cv)).intoArray(lr, i);
}
}
@Run(test = "testLongMulSub")
public static void testLongMulSub_runner() {
testLongMulSub();
for (int i = 0; i < LENGTH; i++) {
Asserts.assertEquals((la[i] - lb[i] * lc[i]), lr[i]);
}
}
public static void main(String[] args) {
TestFramework.runWithFlags("--add-modules=jdk.incubator.vector");
}
}
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