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Panama vector accelerated optimized scalar quantization (#127118)

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* Adds accelerates optimized scalar quantization with vectorized functions

* Adding benchmark

* Update docs/changelog/127118.yaml

* adjusting benchmark and delta
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Benjamin Trent 2025-04-23 12:51:04 -04:00 • committed by GitHub
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commit 059f91c90c
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benchmarks/src/main/java/org/elasticsearch/benchmark/vector/OptimizedScalarQuantizerBenchmark.java Normal file
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/*
* Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
* or more contributor license agreements. Licensed under the "Elastic License
* 2.0", the "GNU Affero General Public License v3.0 only", and the "Server Side
* Public License v 1"; you may not use this file except in compliance with, at
* your election, the "Elastic License 2.0", the "GNU Affero General Public
* License v3.0 only", or the "Server Side Public License, v 1".
*/
package org.elasticsearch.benchmark.vector;
import org.apache.lucene.index.VectorSimilarityFunction;
import org.elasticsearch.common.logging.LogConfigurator;
import org.elasticsearch.index.codec.vectors.OptimizedScalarQuantizer;
import org.openjdk.jmh.annotations.Benchmark;
import org.openjdk.jmh.annotations.BenchmarkMode;
import org.openjdk.jmh.annotations.Fork;
import org.openjdk.jmh.annotations.Level;
import org.openjdk.jmh.annotations.Measurement;
import org.openjdk.jmh.annotations.Mode;
import org.openjdk.jmh.annotations.OutputTimeUnit;
import org.openjdk.jmh.annotations.Param;
import org.openjdk.jmh.annotations.Scope;
import org.openjdk.jmh.annotations.Setup;
import org.openjdk.jmh.annotations.State;
import org.openjdk.jmh.annotations.Warmup;
import java.util.concurrent.ThreadLocalRandom;
import java.util.concurrent.TimeUnit;
@BenchmarkMode(Mode.Throughput)
@OutputTimeUnit(TimeUnit.MILLISECONDS)
@State(Scope.Benchmark)
@Warmup(iterations = 3, time = 1)
@Measurement(iterations = 5, time = 1)
@Fork(value = 3)
public class OptimizedScalarQuantizerBenchmark {
static {
LogConfigurator.configureESLogging(); // native access requires logging to be initialized
}
@Param({ "384", "702", "1024" })
int dims;
float[] vector;
float[] centroid;
byte[] destination;
@Param({ "1", "4", "7" })
byte bits;
OptimizedScalarQuantizer osq = new OptimizedScalarQuantizer(VectorSimilarityFunction.DOT_PRODUCT);
@Setup(Level.Iteration)
public void init() {
ThreadLocalRandom random = ThreadLocalRandom.current();
// random byte arrays for binary methods
destination = new byte[dims];
vector = new float[dims];
centroid = new float[dims];
for (int i = 0; i < dims; ++i) {
vector[i] = random.nextFloat();
centroid[i] = random.nextFloat();
}
}
@Benchmark
public byte[] scalar() {
osq.scalarQuantize(vector, destination, bits, centroid);
return destination;
}
@Benchmark
@Fork(jvmArgsPrepend = { "--add-modules=jdk.incubator.vector" })
public byte[] vector() {
osq.scalarQuantize(vector, destination, bits, centroid);
return destination;
}
}