Add and update benchmark

1 files changed, 90 insertions, 0 deletions

diff --git a/vespajlib/src/test/java/com/yahoo/tensor/MatrixDotProductBenchmark.java b/vespajlib/src/test/java/com/yahoo/tensor/MatrixDotProductBenchmark.java
new file mode 100644
index 00000000000..439aac5578a
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+++ b/vespajlib/src/test/java/com/yahoo/tensor/MatrixDotProductBenchmark.java
@@ -0,0 +1,90 @@
+// Copyright 2017 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+package com.yahoo.tensor;
+
+import com.yahoo.tensor.evaluation.MapEvaluationContext;
+import com.yahoo.tensor.evaluation.VariableTensor;
+import com.yahoo.tensor.functions.ConstantTensor;
+import com.yahoo.tensor.functions.Join;
+import com.yahoo.tensor.functions.Reduce;
+import com.yahoo.tensor.functions.TensorFunction;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+import java.util.Random;
+import java.util.stream.Collectors;
+
+/**
+ * Microbenchmark of a "dot product" of two mapped rank 2 tensors
+ *
+ * @author bratseth
+ */
+public class MatrixDotProductBenchmark {
+
+    private final static Random random = new Random();
+
+    public double benchmark(int iterations, List<Tensor> modelMatrixes, TensorType.Dimension.Type dimensionType) {
+        Tensor queryMatrix = matrix(1, 20, dimensionType).get(0);
+        dotProduct(queryMatrix, modelMatrixes, Math.max(iterations/10, 10)); // warmup
+        System.gc();
+        long startTime = System.currentTimeMillis();
+        dotProduct(queryMatrix, modelMatrixes, iterations);
+        long totalTime = System.currentTimeMillis() - startTime;
+        return (double)totalTime / (double)iterations;
+    }
+
+    private double dotProduct(Tensor tensor, List<Tensor> tensors, int iterations) {
+        double result = 0;
+        for (int i = 0 ; i < iterations; i++)
+            result = dotProduct(tensor, tensors);
+        return result;
+    }
+
+    private double dotProduct(Tensor tensor, List<Tensor> tensors) {
+        double largest = Double.MIN_VALUE;
+        TensorFunction dotProductFunction = new Reduce(new Join(new ConstantTensor(tensor),
+                                                                new VariableTensor("argument"), (a, b) -> a * b),
+                                                       Reduce.Aggregator.sum).toPrimitive();
+        MapEvaluationContext context = new MapEvaluationContext();
+
+        for (Tensor tensorElement : tensors) { // tensors.size() = 1 for larger tensor
+            context.put("argument", tensorElement);
+            double dotProduct = dotProductFunction.evaluate(context).asDouble();
+            if (dotProduct > largest) {
+                largest = dotProduct;
+            }
+        }
+        return largest;
+    }
+
+    private static List<Tensor> matrix(int dimension1Size, int dimension2Size, TensorType.Dimension.Type dimensionType) {
+        TensorType.Builder typeBuilder = new TensorType.Builder();
+        addDimension(typeBuilder, "i", dimensionType, dimension1Size);
+        addDimension(typeBuilder, "j", dimensionType, dimension2Size);
+        Tensor.Builder builder = Tensor.Builder.of(typeBuilder.build());
+        for (int i = 0; i < dimension1Size; i++) {
+            for (int j = 0; j < dimension2Size; j++) {
+                builder.cell()
+                        .label("i", String.valueOf("label" + i))
+                        .label("j", String.valueOf("label" + j))
+                        .value(random.nextDouble());
+            }
+        }
+        return Collections.singletonList(builder.build());
+    }
+
+    private static void addDimension(TensorType.Builder builder, String name, TensorType.Dimension.Type type, int size) {
+        switch (type) {
+            case mapped: builder.mapped(name); break;
+            case indexedUnbound: builder.indexed(name); break;
+            case indexedBound: builder.indexed(name, size); break;
+            default: throw new IllegalArgumentException("Dimension type " + type + " not supported");
+        }
+    }
+
+    public static void main(String[] args) {
+        double time = new MatrixDotProductBenchmark().benchmark(10000, matrix(10, 55, TensorType.Dimension.Type.mapped), TensorType.Dimension.Type.mapped);
+        System.out.printf("Matrixes, 10*55 size matrixes. Time per sum(join): %1$8.3f ms\n", time);
+    }
+
+}