Build status for all platforms: 
Commercial support:
This directory contains the JavaCPP Presets module for:
- MKL 2021.4 https://software.intel.com/mkl
Please refer to the parent README.md file for more detailed information about the JavaCPP Presets.
Java API documentation is available here:
∗ MKL also gets used by the JavaCPP Presets for OpenBLAS, the JavaCPP Presets for MKL-DNN, or any other library that depends on one of them.
Here is a simple example of MKL ported to Java from the dgemm_example.c sample file included in mkl_c_samples_022017_0.zip available at:
We can use Maven 3 to download and install automatically all the class files as well as the native binaries. To run this sample code, after creating the pom.xml and DGEMMExample.java source files below, simply execute on the command line:
$ mvn compile exec:java<project>
<modelVersion>4.0.0</modelVersion>
<groupId>org.bytedeco.mkl</groupId>
<artifactId>mkl</artifactId>
<version>1.5.7-SNAPSHOT</version>
<properties>
<exec.mainClass>DGEMMExample</exec.mainClass>
</properties>
<dependencies>
<dependency>
<groupId>org.bytedeco</groupId>
<artifactId>mkl-platform</artifactId>
<version>2021.4-1.5.7-SNAPSHOT</version>
</dependency>
<!-- Additional dependencies to use bundled full version of MKL -->
<dependency>
<groupId>org.bytedeco</groupId>
<artifactId>mkl-platform-redist</artifactId>
<version>2021.4-1.5.7-SNAPSHOT</version>
</dependency>
</dependencies>
<build>
<sourceDirectory>.</sourceDirectory>
</build>
</project>//==============================================================
//
// SAMPLE SOURCE CODE - SUBJECT TO THE TERMS OF SAMPLE CODE LICENSE AGREEMENT,
// http://software.intel.com/en-us/articles/intel-sample-source-code-license-agreement/
//
// Copyright 2016-2017 Intel Corporation
//
// THIS FILE IS PROVIDED "AS IS" WITH NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT
// NOT LIMITED TO ANY IMPLIED WARRANTY OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
// PURPOSE, NON-INFRINGEMENT OF INTELLECTUAL PROPERTY RIGHTS.
//
// =============================================================
/*******************************************************************************
* This example computes real matrix C=alpha*A*B+beta*C using Intel(R) MKL
* function dgemm, where A, B, and C are matrices and alpha and beta are
* scalars in double precision.
*
* In this simple example, practices such as memory management, data alignment,
* and I/O that are necessary for good programming style and high MKL
* performance are omitted to improve readability.
********************************************************************************/
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.indexer.*;
import static org.bytedeco.mkl.global.mkl_rt.*;
public class DGEMMExample {
public static void main(String[] args) throws Exception {
System.out.println("\n This example computes real matrix C=alpha*A*B+beta*C using \n"
+ " Intel(R) MKL function dgemm, where A, B, and C are matrices and \n"
+ " alpha and beta are double precision scalars\n");
int m = 2000, p = 200, n = 1000;
System.out.printf(" Initializing data for matrix multiplication C=A*B for matrix \n"
+ " A(%dx%d) and matrix B(%dx%d)\n\n", m, p, p, n);
double alpha = 1.0, beta = 0.0;
System.out.println(" Allocating memory for matrices aligned on 64-byte boundary for better \n"
+ " performance \n");
DoublePointer A = new DoublePointer(MKL_malloc(m * p * Double.BYTES, 64));
DoublePointer B = new DoublePointer(MKL_malloc(p * n * Double.BYTES, 64));
DoublePointer C = new DoublePointer(MKL_malloc(m * n * Double.BYTES, 64));
if (A.isNull() || B.isNull() || C.isNull()) {
System.out.println( "\n ERROR: Can't allocate memory for matrices. Aborting... \n");
MKL_free(A);
MKL_free(B);
MKL_free(C);
System.exit(1);
}
System.out.println(" Intializing matrix data \n");
DoubleIndexer Aidx = DoubleIndexer.create(A.capacity(m * p));
for (int i = 0; i < m * p; i++) {
A.put(i, (double)(i + 1));
}
DoubleIndexer Bidx = DoubleIndexer.create(B.capacity(p * n));
for (int i = 0; i < p * n; i++) {
B.put(i, (double)(-i - 1));
}
DoubleIndexer Cidx = DoubleIndexer.create(C.capacity(m * n));
for (int i = 0; i < m * n; i++) {
C.put(i, 0.0);
}
System.out.println(" Computing matrix product using Intel(R) MKL dgemm function via CBLAS interface \n");
cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
m, n, p, alpha, A, p, B, n, beta, C, n);
System.out.println("\n Computations completed.\n");
System.out.println(" Top left corner of matrix A: ");
for (int i = 0; i < Math.min(m, 6); i++) {
for (int j = 0; j< Math.min(p, 6); j++) {
System.out.printf("%12.0f", Aidx.get(j + i * p));
}
System.out.println();
}
System.out.println("\n Top left corner of matrix B: ");
for (int i = 0; i < Math.min(p, 6); i++) {
for (int j = 0; j < Math.min(n, 6); j++) {
System.out.printf("%12.0f", Bidx.get(j + i * n));
}
System.out.println();
}
System.out.println("\n Top left corner of matrix C: ");
for (int i = 0; i < Math.min(m, 6); i++) {
for (int j = 0; j < Math.min(n, 6); j++) {
System.out.printf("%12.5G", Cidx.get(j + i * n));
}
System.out.println();
}
System.out.println("\n Deallocating memory \n");
MKL_free(A);
MKL_free(B);
MKL_free(C);
System.out.println(" Example completed. \n");
System.exit(0);
}
}