Multiplicación de matrices en Java

1. Información general

En este tutorial, veremos cómo podemos multiplicar dos matrices en Java.

Como el concepto de matriz no existe de forma nativa en el lenguaje, lo implementaremos nosotros mismos y también trabajaremos con algunas bibliotecas para ver cómo manejan la multiplicación de matrices.

Al final, haremos una pequeña evaluación comparativa de las diferentes soluciones que exploramos para determinar la más rápida.

2. El ejemplo

Comencemos por configurar un ejemplo al que podremos hacer referencia a lo largo de este tutorial.

Primero, imaginaremos una matriz de 3 × 2:

Imaginemos ahora una segunda matriz, esta vez dos filas por cuatro columnas:

Luego, la multiplicación de la primera matriz por la segunda matriz, lo que resultará en una matriz de 3 × 4:

Como recordatorio, este resultado se obtiene calculando cada celda de la matriz resultante con esta fórmula :

Donde r es el número de filas de la matriz A , c es el número de columnas de la matriz B y n es el número de columnas de la matriz A , que debe coincidir con el número de filas de la matriz B .

3. Multiplicación de matrices

3.1. Implementación propia

Comencemos con nuestra propia implementación de matrices.

Lo haremos simple y solo usaremos matrices dobles bidimensionales :

double[][] firstMatrix = { new double[]{1d, 5d}, new double[]{2d, 3d}, new double[]{1d, 7d} }; double[][] secondMatrix = { new double[]{1d, 2d, 3d, 7d}, new double[]{5d, 2d, 8d, 1d} };

Esas son las dos matrices de nuestro ejemplo. Creemos el esperado como resultado de su multiplicación:

double[][] expected = { new double[]{26d, 12d, 43d, 12d}, new double[]{17d, 10d, 30d, 17d}, new double[]{36d, 16d, 59d, 14d} };

Ahora que todo está configurado, implementemos el algoritmo de multiplicación. Primero crearemos una matriz de resultados vacía e iteraremos a través de sus celdas para almacenar el valor esperado en cada una de ellas:

double[][] multiplyMatrices(double[][] firstMatrix, double[][] secondMatrix) { double[][] result = new double[firstMatrix.length][secondMatrix[0].length]; for (int row = 0; row < result.length; row++) { for (int col = 0; col < result[row].length; col++) { result[row][col] = multiplyMatricesCell(firstMatrix, secondMatrix, row, col); } } return result; }

Finalmente, implementemos el cálculo de una sola celda. Para lograrlo, usaremos la fórmula mostrada anteriormente en la presentación del ejemplo :

double multiplyMatricesCell(double[][] firstMatrix, double[][] secondMatrix, int row, int col) { double cell = 0; for (int i = 0; i < secondMatrix.length; i++) { cell += firstMatrix[row][i] * secondMatrix[i][col]; } return cell; }

Finalmente, verifiquemos que el resultado del algoritmo coincida con nuestro resultado esperado:

double[][] actual = multiplyMatrices(firstMatrix, secondMatrix); assertThat(actual).isEqualTo(expected);

3.2. EJML

La primera biblioteca que veremos es EJML, que significa Efficient Java Matrix Library. En el momento de escribir este tutorial, es una de las bibliotecas de matrices Java actualizadas más recientemente . Su propósito es ser lo más eficiente posible con respecto al cálculo y al uso de la memoria.

Tendremos que agregar la dependencia a la biblioteca en nuestro pom.xml :

 org.ejml ejml-all 0.38 

Usaremos prácticamente el mismo patrón que antes: creando dos matrices de acuerdo con nuestro ejemplo y verificaremos que el resultado de su multiplicación sea el que calculamos anteriormente.

Entonces, creemos nuestras matrices usando EJML. Para lograr esto, usaremos la clase SimpleMatrix que ofrece la biblioteca .

Puede tomar una matriz doble de dos dimensiones como entrada para su constructor:

SimpleMatrix firstMatrix = new SimpleMatrix( new double[][] { new double[] {1d, 5d}, new double[] {2d, 3d}, new double[] {1d ,7d} } ); SimpleMatrix secondMatrix = new SimpleMatrix( new double[][] { new double[] {1d, 2d, 3d, 7d}, new double[] {5d, 2d, 8d, 1d} } );

Y ahora, definamos nuestra matriz esperada para la multiplicación:

SimpleMatrix expected = new SimpleMatrix( new double[][] { new double[] {26d, 12d, 43d, 12d}, new double[] {17d, 10d, 30d, 17d}, new double[] {36d, 16d, 59d, 14d} } );

Ahora que estamos listos, veamos cómo multiplicar las dos matrices juntas. La clase SimpleMatrix ofrece un método mult () que toma otra SimpleMatrix como parámetro y devuelve la multiplicación de las dos matrices:

SimpleMatrix actual = firstMatrix.mult(secondMatrix);

Comprobemos si el resultado obtenido coincide con el esperado.

Como SimpleMatrix no anula el método equals () , no podemos confiar en él para realizar la verificación. Pero ofrece una alternativa: el método isIdentical () que toma no solo otro parámetro de matriz sino también uno de tolerancia a doble falla para ignorar pequeñas diferencias debido a la doble precisión:

assertThat(actual).matches(m -> m.isIdentical(expected, 0d));

Eso concluye la multiplicación de matrices con la biblioteca EJML. Veamos qué ofrecen los demás.

3.3. ND4J

Probemos ahora la biblioteca ND4J. ND4J es una biblioteca de cálculo y es parte del proyecto deeplearning4j. Entre otras cosas, ND4J ofrece funciones de cálculo de matrices.

En primer lugar, tenemos que obtener la dependencia de la biblioteca:

 org.nd4j nd4j-native 1.0.0-beta4 

Tenga en cuenta que estamos usando la versión beta aquí porque parece haber algunos errores con la versión GA.

For the sake of brevity, we won't rewrite the two dimensions double arrays and just focus on how they are used with each library. Thus, with ND4J, we must create an INDArray. In order to do that, we'll call the Nd4j.create() factory method and pass it a double array representing our matrix:

INDArray matrix = Nd4j.create(/* a two dimensions double array */);

As in the previous section, we'll create three matrices: the two we're going to multiply together and the one being the expected result.

After that, we want to actually do the multiplication between the first two matrices using the INDArray.mmul() method:

INDArray actual = firstMatrix.mmul(secondMatrix);

Then, we check again that the actual result matches the expected one. This time we can rely on an equality check:

assertThat(actual).isEqualTo(expected);

This demonstrates how the ND4J library can be used to do matrix calculations.

3.4. Apache Commons

Let's now talk about the Apache Commons Math3 module, which provides us with mathematic computations including matrices manipulations.

Again, we'll have to specify the dependency in our pom.xml:

 org.apache.commons commons-math3 3.6.1 

Once set up, we can use the RealMatrix interface and its Array2DRowRealMatrix implementation to create our usual matrices. The constructor of the implementation class takes a two-dimensional double array as its parameter:

RealMatrix matrix = new Array2DRowRealMatrix(/* a two dimensions double array */);

As for matrices multiplication, the RealMatrix interface offers a multiply() method taking another RealMatrix parameter:

RealMatrix actual = firstMatrix.multiply(secondMatrix);

We can finally verify that the result is equal to what we're expecting:

assertThat(actual).isEqualTo(expected);

Let's see the next library!

3.5. LA4J

This one's named LA4J, which stands for Linear Algebra for Java.

Let's add the dependency for this one as well:

 org.la4j la4j 0.6.0 

Now, LA4J works pretty much like the other libraries. It offers a Matrix interface with a Basic2DMatrix implementation that takes a two-dimensional double array as input:

Matrix matrix = new Basic2DMatrix(/* a two dimensions double array */);

As in the Apache Commons Math3 module, the multiplication method is multiply() and takes another Matrix as its parameter:

Matrix actual = firstMatrix.multiply(secondMatrix);

Once again, we can check that the result matches our expectations:

assertThat(actual).isEqualTo(expected);

Let's now have a look at our last library: Colt.

3.6. Colt

Colt is a library developed by CERN. It provides features enabling high performance scientific and technical computing.

As with the previous libraries, we must get the right dependency:

 colt colt 1.2.0 

In order to create matrices with Colt, we must make use of the DoubleFactory2D class. It comes with three factory instances: dense, sparse and rowCompressed. Each is optimized to create the matching kind of matrix.

For our purpose, we'll use the dense instance. This time, the method to call is make() and it takes a two-dimensional double array again, producing a DoubleMatrix2D object:

DoubleMatrix2D matrix = doubleFactory2D.make(/* a two dimensions double array */);

Once our matrices are instantiated, we'll want to multiply them. This time, there's no method on the matrix object to do that. We've got to create an instance of the Algebra class which has a mult() method taking two matrices for parameters:

Algebra algebra = new Algebra(); DoubleMatrix2D actual = algebra.mult(firstMatrix, secondMatrix);

Then, we can compare the actual result to the expected one:

assertThat(actual).isEqualTo(expected);

4. Benchmarking

Now that we're done with exploring the different possibilities of matrix multiplication, let's check which are the most performant.

4.1. Small Matrices

Let's begin with small matrices. Here, a 3×2 and a 2×4 matrices.

In order to implement the performance test, we'll use the JMH benchmarking library. Let's configure a benchmarking class with the following options:

public static void main(String[] args) throws Exception { Options opt = new OptionsBuilder() .include(MatrixMultiplicationBenchmarking.class.getSimpleName()) .mode(Mode.AverageTime) .forks(2) .warmupIterations(5) .measurementIterations(10) .timeUnit(TimeUnit.MICROSECONDS) .build(); new Runner(opt).run(); }

This way, JMH will make two full runs for each method annotated with @Benchmark, each with five warmup iterations (not taken into the average computation) and ten measurement ones. As for the measurements, it'll gather the average time of execution of the different libraries, in microseconds.

We then have to create a state object containing our arrays:

@State(Scope.Benchmark) public class MatrixProvider { private double[][] firstMatrix; private double[][] secondMatrix; public MatrixProvider() { firstMatrix = new double[][] { new double[] {1d, 5d}, new double[] {2d, 3d}, new double[] {1d ,7d} }; secondMatrix = new double[][] { new double[] {1d, 2d, 3d, 7d}, new double[] {5d, 2d, 8d, 1d} }; } }

That way, we make sure arrays initialization is not part of the benchmarking. After that, we still have to create methods that do the matrices multiplication, using the MatrixProvider object as the data source. We won't repeat the code here as we saw each library earlier.

Finally, we'll run the benchmarking process using our main method. This gives us the following result:

Benchmark Mode Cnt Score Error Units MatrixMultiplicationBenchmarking.apacheCommonsMatrixMultiplication avgt 20 1,008 ± 0,032 us/op MatrixMultiplicationBenchmarking.coltMatrixMultiplication avgt 20 0,219 ± 0,014 us/op MatrixMultiplicationBenchmarking.ejmlMatrixMultiplication avgt 20 0,226 ± 0,013 us/op MatrixMultiplicationBenchmarking.homemadeMatrixMultiplication avgt 20 0,389 ± 0,045 us/op MatrixMultiplicationBenchmarking.la4jMatrixMultiplication avgt 20 0,427 ± 0,016 us/op MatrixMultiplicationBenchmarking.nd4jMatrixMultiplication avgt 20 12,670 ± 2,582 us/op

As we can see, EJML and Colt are performing really well with about a fifth of a microsecond per operation, where ND4j is less performant with a bit more than ten microseconds per operation. The other libraries have performances situated in between.

Also, it's worth noting that when increasing the number of warmup iterations from 5 to 10, performance is increasing for all the libraries.

4.2. Large Matrices

Now, what happens if we take larger matrices, like 3000×3000? To check what happens, let's first create another state class providing generated matrices of that size:

@State(Scope.Benchmark) public class BigMatrixProvider { private double[][] firstMatrix; private double[][] secondMatrix; public BigMatrixProvider() {} @Setup public void setup(BenchmarkParams parameters) { firstMatrix = createMatrix(); secondMatrix = createMatrix(); } private double[][] createMatrix() { Random random = new Random(); double[][] result = new double[3000][3000]; for (int row = 0; row < result.length; row++) { for (int col = 0; col < result[row].length; col++) { result[row][col] = random.nextDouble(); } } return result; } }

As we can see, we'll create 3000×3000 two-dimensions double arrays filled with random real numbers.

Let's now create the benchmarking class:

public class BigMatrixMultiplicationBenchmarking { public static void main(String[] args) throws Exception { Map parameters = parseParameters(args); ChainedOptionsBuilder builder = new OptionsBuilder() .include(BigMatrixMultiplicationBenchmarking.class.getSimpleName()) .mode(Mode.AverageTime) .forks(2) .warmupIterations(10) .measurementIterations(10) .timeUnit(TimeUnit.SECONDS); new Runner(builder.build()).run(); } @Benchmark public Object homemadeMatrixMultiplication(BigMatrixProvider matrixProvider) { return HomemadeMatrix .multiplyMatrices(matrixProvider.getFirstMatrix(), matrixProvider.getSecondMatrix()); } @Benchmark public Object ejmlMatrixMultiplication(BigMatrixProvider matrixProvider) { SimpleMatrix firstMatrix = new SimpleMatrix(matrixProvider.getFirstMatrix()); SimpleMatrix secondMatrix = new SimpleMatrix(matrixProvider.getSecondMatrix()); return firstMatrix.mult(secondMatrix); } @Benchmark public Object apacheCommonsMatrixMultiplication(BigMatrixProvider matrixProvider) { RealMatrix firstMatrix = new Array2DRowRealMatrix(matrixProvider.getFirstMatrix()); RealMatrix secondMatrix = new Array2DRowRealMatrix(matrixProvider.getSecondMatrix()); return firstMatrix.multiply(secondMatrix); } @Benchmark public Object la4jMatrixMultiplication(BigMatrixProvider matrixProvider) { Matrix firstMatrix = new Basic2DMatrix(matrixProvider.getFirstMatrix()); Matrix secondMatrix = new Basic2DMatrix(matrixProvider.getSecondMatrix()); return firstMatrix.multiply(secondMatrix); } @Benchmark public Object nd4jMatrixMultiplication(BigMatrixProvider matrixProvider) { INDArray firstMatrix = Nd4j.create(matrixProvider.getFirstMatrix()); INDArray secondMatrix = Nd4j.create(matrixProvider.getSecondMatrix()); return firstMatrix.mmul(secondMatrix); } @Benchmark public Object coltMatrixMultiplication(BigMatrixProvider matrixProvider) { DoubleFactory2D doubleFactory2D = DoubleFactory2D.dense; DoubleMatrix2D firstMatrix = doubleFactory2D.make(matrixProvider.getFirstMatrix()); DoubleMatrix2D secondMatrix = doubleFactory2D.make(matrixProvider.getSecondMatrix()); Algebra algebra = new Algebra(); return algebra.mult(firstMatrix, secondMatrix); } }

When we run this benchmarking, we obtain completely different results:

Benchmark Mode Cnt Score Error Units BigMatrixMultiplicationBenchmarking.apacheCommonsMatrixMultiplication avgt 20 511.140 ± 13.535 s/op BigMatrixMultiplicationBenchmarking.coltMatrixMultiplication avgt 20 197.914 ± 2.453 s/op BigMatrixMultiplicationBenchmarking.ejmlMatrixMultiplication avgt 20 25.830 ± 0.059 s/op BigMatrixMultiplicationBenchmarking.homemadeMatrixMultiplication avgt 20 497.493 ± 2.121 s/op BigMatrixMultiplicationBenchmarking.la4jMatrixMultiplication avgt 20 35.523 ± 0.102 s/op BigMatrixMultiplicationBenchmarking.nd4jMatrixMultiplication avgt 20 0.548 ± 0.006 s/op

As we can see, the homemade implementations and the Apache library are now way worse than before, taking nearly 10 minutes to perform the multiplication of the two matrices.

Colt is taking a bit more than 3 minutes, which is better but still very long. EJML and LA4J are performing pretty well as they run in nearly 30 seconds. But, it's ND4J which wins this benchmarking performing in under a second on a CPU backend.

4.3. Analysis

That shows us that the benchmarking results really depend on the matrices' characteristics and therefore it's tricky to point out a single winner.

5. Conclusion

In this article, we've learned how to multiply matrices in Java, either by ourselves or with external libraries. After exploring all solutions, we did a benchmark of all of them and saw that, except for ND4J, they all performed pretty well on small matrices. On the other hand, on larger matrices, ND4J is taking the lead.

As usual, the full code for this article can be found over on GitHub.