Adding benchmark

Author: benwtrent

benchmarks/src/main/java/org/elasticsearch/benchmark/vector/OptimizedScalarQuantizerBenchmark.java

@@ -0,0 +1,78 @@
+/*
+ * 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 = 4, time = 1)
+@Measurement(iterations = 5, time = 1)
+@Fork(value = 1)
+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;
+    }
+}