Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

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, Float.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, Float.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();
}
}

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