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162 lines
6.6 KiB
Java
162 lines
6.6 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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package compiler.vectorization;
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import jdk.incubator.vector.*;
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/**
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* The code below is supposed to be an exact copy of:
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* micro/org/openjdk/bench/vm/compiler/VectorAlgorithmsImpl.java
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*/
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public class VectorAlgorithmsImpl {
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private static final VectorSpecies<Integer> SPECIES_I = IntVector.SPECIES_PREFERRED;
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private static final VectorSpecies<Integer> SPECIES_I512 = IntVector.SPECIES_512;
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public static int reduceAddI_loop(int[] a) {
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int sum = 0;
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for (int i = 0; i < a.length; i++) {
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// Relying on simple reduction loop should vectorize since JDK26.
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sum += a[i];
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}
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return sum;
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}
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public static int reduceAddI_reassociate(int[] a) {
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int sum = 0;
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int i;
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for (i = 0; i < a.length - 3; i+=4) {
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// Unroll 4x, reassociate inside.
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sum += a[i] + a[i + 1] + a[i + 2] + a[i + 3];
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}
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for (; i < a.length; i++) {
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// Tail
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sum += a[i];
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}
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return sum;
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}
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public static int reduceAddI_VectorAPI_naive(int[] a) {
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var sum = 0;
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int i;
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for (i = 0; i < SPECIES_I.loopBound(a.length); i += SPECIES_I.length()) {
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IntVector v = IntVector.fromArray(SPECIES_I, a, i);
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// reduceLanes in loop is better than scalar performance, but still
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// relatively slow.
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sum += v.reduceLanes(VectorOperators.ADD);
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}
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for (; i < a.length; i++) {
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sum += a[i];
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}
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return sum;
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}
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public static int reduceAddI_VectorAPI_reduction_after_loop(int[] a) {
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var acc = IntVector.broadcast(SPECIES_I, 0);
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int i;
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for (i = 0; i < SPECIES_I.loopBound(a.length); i += SPECIES_I.length()) {
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IntVector v = IntVector.fromArray(SPECIES_I, a, i);
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// Element-wide addition into a vector of partial sums is much faster.
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// Now, we only need to do a reduceLanes after the loop.
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// This works because int-addition is associative and commutative.
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acc = acc.add(v);
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}
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int sum = acc.reduceLanes(VectorOperators.ADD);
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for (; i < a.length; i++) {
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sum += a[i];
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}
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return sum;
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}
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//@Benchmark
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//public void scanAddI_loop() {
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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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// RI[i] = sum;
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// }
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//}
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//@Benchmark
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//public void scanAddI_loop_reassociate() {
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// int sum = 0;
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// for (int i = 0; i < AI.length; i+=4) {
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// // We cut the latency by a factor of 4, but increase the number of additions.
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// int old_sum = sum;
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// int v0 = AI[i + 0];
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// int v1 = AI[i + 1];
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// int v2 = AI[i + 2];
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// int v3 = AI[i + 3];
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// int v01 = v0 + v1;
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// int v23 = v2 + v3;
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// int v0123 = v01 + v23;
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// sum += v0123;
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// RI[i + 0] = old_sum + v0;
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// RI[i + 1] = old_sum + v01;
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// RI[i + 2] = old_sum + v01 + v2;
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// RI[i + 3] = old_sum + v0123;
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// }
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//}
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//@Benchmark
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//public void scanAddI_VectorAPI_shift_blend_add() {
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// // Using Naive Parallel Algorithm: Hills and Steele
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// int sum = 0;
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// for (int i = 0; i < SPECIES_I512.loopBound(AI.length); i += SPECIES_I512.length()) {
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// IntVector v = IntVector.fromArray(SPECIES_I512, AI, i);
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// v = v.add(v.lanewise(VectorOperators.LSHL, 1 ).blend(0, VectorMask.fromLong(SPECIES_I512, 0b1111111111111110)));
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// v = v.add(v.lanewise(VectorOperators.LSHL, 2 ).blend(0, VectorMask.fromLong(SPECIES_I512, 0b1111111111111100)));
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// v = v.add(v.lanewise(VectorOperators.LSHL, 4 ).blend(0, VectorMask.fromLong(SPECIES_I512, 0b1111111111110000)));
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// v = v.add(v.lanewise(VectorOperators.LSHL, 8 ).blend(0, VectorMask.fromLong(SPECIES_I512, 0b1111111100000000)));
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// v = v.add(sum);
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// v.intoArray(RI, i);
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// sum = v.lane(SPECIES_I512.length() - 1);
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// }
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//}
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//@Benchmark
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//public void scanAddI_VectorAPI_permute_add() {
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// // Using Naive Parallel Algorithm: Hills and Steele
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// int sum = 0;
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// var shf1 = VectorShuffle.fromArray(SPECIES_I512, new int[]{-1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}, 0);
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// var shf2 = VectorShuffle.fromArray(SPECIES_I512, new int[]{-1, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13}, 0);
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// var shf3 = VectorShuffle.fromArray(SPECIES_I512, new int[]{-1, -1, -1, -1, 0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12}, 0);
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// var shf4 = VectorShuffle.fromArray(SPECIES_I512, new int[]{-1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, 3, 4, 6, 7, 8}, 0);
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// var mask1 = VectorMask.fromLong(SPECIES_I512, 0b1111111111111110);
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// var mask2 = VectorMask.fromLong(SPECIES_I512, 0b1111111111111100);
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// var mask3 = VectorMask.fromLong(SPECIES_I512, 0b1111111111110000);
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// var mask4 = VectorMask.fromLong(SPECIES_I512, 0b1111111100000000);
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// for (int i = 0; i < SPECIES_I512.loopBound(AI.length); i += SPECIES_I512.length()) {
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// IntVector v = IntVector.fromArray(SPECIES_I512, AI, i);
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// v = v.add(v.rearrange(shf1), mask1);
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// v = v.add(v.rearrange(shf2), mask2);
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// v = v.add(v.rearrange(shf3), mask3);
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// v = v.add(v.rearrange(shf4), mask4);
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// v = v.add(sum);
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// v.intoArray(RI, i);
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// sum = v.lane(SPECIES_I512.length() - 1);
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// }
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//}
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}
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