SetNumberOfThreads.java

/* file: SetNumberOfThreads.java */
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/*
 //  Content:
 //     Java example of setting the maximum number of threads
 */

package com.intel.daal.examples.set_number_of_threads;

import com.intel.daal.algorithms.kmeans.Batch;
import com.intel.daal.algorithms.kmeans.InputId;
import com.intel.daal.algorithms.kmeans.Method;
import com.intel.daal.algorithms.kmeans.init.InitBatch;
import com.intel.daal.algorithms.kmeans.init.InitInputId;
import com.intel.daal.algorithms.kmeans.init.InitMethod;
import com.intel.daal.algorithms.kmeans.init.InitResult;
import com.intel.daal.algorithms.kmeans.init.InitResultId;
import com.intel.daal.data_management.data.NumericTable;
import com.intel.daal.data_management.data_source.DataSource;
import com.intel.daal.data_management.data_source.FileDataSource;
import com.intel.daal.services.DaalContext;
import com.intel.daal.services.Environment;

class SetNumberOfThreads {
    /* Input data set parameters */
    private static final String dataset       = "../data/batch/kmeans_dense.csv";
    private static final int    nClusters     = 20;

    /* K-Means algorithm parameters */
    private static final int maxIterations = 5;
    private static final int nThreads = 2;
    private static  int nThreadsInit;
    private static  int nThreadsNew;

    private static DaalContext context = new DaalContext();

    public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {

        /* Get the number of threads that is used by the library by default */
        nThreadsInit = Environment.getNumberOfThreads();

        /* Set the maximum number of threads to be used by the library */
        Environment.setNumberOfThreads(nThreads);

        /* Get the number of threads that is used by the library after changing */
        nThreadsNew = Environment.getNumberOfThreads();

        /* Retrieve the input data */
        FileDataSource dataSource = new FileDataSource(context, dataset,
                                                       DataSource.DictionaryCreationFlag.DoDictionaryFromContext,
                                                       DataSource.NumericTableAllocationFlag.DoAllocateNumericTable);
        dataSource.loadDataBlock();
        NumericTable input = dataSource.getNumericTable();

        /* Calculate initial clusters for K-Means clustering */
        InitBatch init = new InitBatch(context, Double.class, InitMethod.randomDense, nClusters);
        init.input.set(InitInputId.data, input);
        InitResult initResult = init.compute();
        NumericTable inputCentroids = initResult.get(InitResultId.centroids);

        /* Create an algorithm for K-Means clustering */
        Batch algorithm = new Batch(context, Double.class, Method.lloydDense, nClusters, maxIterations);

        /* Set an input object for the algorithm */
        algorithm.input.set(InputId.data, input);
        algorithm.input.set(InputId.inputCentroids, inputCentroids);

        /* Clusterize the data */
        algorithm.compute();

        System.out.println("Initial number of threads:        " + nThreadsInit);
        System.out.println("Number of threads to set:         " + nThreads);
        System.out.println("Number of threads after setting:  " + nThreadsNew);

        context.dispose();
    }
}