Getting Started with Intel® Data Analytics Acceleration Library for macOS*

Intel® Data Analytics Acceleration Library (Intel® DAAL) is the library of Intel® architecture optimized building blocks covering all stages of data analytics: data acquisition from a data source, preprocessing, transformation, data mining, modeling, validation, and decision making.

Intel DAAL is installed standalone and as part of the following suites:

Intel DAAL is also provided as a standalone package under the Community Licensing Program.


System Requirements.

Install Intel DAAL on Your System

Intel DAAL installs in the directory <install dir>/daal.

By default, <install dir> is /opt/intel/

For installation details, refer to Intel DAAL Installation Guide.

Set Environment Variables

  1. Run the <install dir>/daal/bin/ script as appropriate to your target architecture:

    • IA-32 architecture: ia32

    • Intel® 64 architecture: intel64

  2. Optionally: Specify the Java* compiler different from the default compiler:


    export PATH=$JAVA_HOME/bin:$PATH

C++ Language

Step 1: Choose the Compiler Option for Automatic Linking of Your Application with Intel DAAL

Decide on the variant of the the -daal option of the Intel® C++ Compiler 16 or higher or configure your project in the Integrated Development Environment (IDE):

Compiler Option

IDE Equivalent

daal or ‑daal=parallel

Tells the compiler to link with standard threaded Intel DAAL.


  1. Go to Project > Build Settings > ICC InteL C++ Compiler XE yy.y > Performance Library Build Components > Use Intel Data Analytics Acceleration Library.
  2. Select Use threaded Intel Data Analytics Acceleration Library or Use non-threaded Intel Data Analytics Acceleration Library, as appropriate.


Tells the compiler to link with sequential version of Intel DAAL.

For more information on the daal compiler option, see the Intel® Compiler User and Reference Guide.

Step 2: Create and Run Your First Application with Intel DAAL

This short application computes Cholesky decomposition with Intel DAAL.

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!  Content:
!    Cholesky decomposition sample program.

#include "daal.h"
#include <iostream>

using namespace daal;
using namespace daal::algorithms;
using namespace daal::data_management;
using namespace daal::services;

const size_t dimension = 3;
double inputArray[dimension *dimension] =
    1.0,  2.0,  4.0,
    2.0, 13.0, 23.0,
    4.0, 23.0, 77.0

int main(int argc, char *argv[])
    /* Create input numeric table from array */
    SharedPtr<NumericTable> inputData = SharedPtr<NumericTable>(new Matrix<double>(dimension, dimension, inputArray));

    /*  Create the algorithm object for computation of the Cholesky decomposition using the default method */
    cholesky::Batch<> algorithm;

    /* Set input for the algorithm */
    algorithm.input.set(cholesky::data, inputData);

    /* Compute Cholesky decomposition */

    /* Get pointer to Cholesky factor */
    SharedPtr<Matrix<double> > factor =
        staticPointerCast<Matrix<double>, NumericTable>(algorithm.getResult()->get(cholesky::choleskyFactor));

    /* Print the first element of the Cholesky factor */
    std::cout << "The first element of the Cholesky factor: " << (*factor)[0][0];

    return 0;
  1. Paste the application code into the editor of your choice.

  2. Save the file as my_first_daal_program.cpp.

  3. Compile with the following command, providing the selected variant of the ‑daal compiler option, for example, ‑daal=parallel:

    icc my_first_daal_program.cpp -daal=parallel -o my_first_daal_program

  4. Run the application.

Step 3 (Optional): Build Your Application with Different Compilers

List the following Intel DAAL libraries on a link line, depending on Intel DAAL threading mode and linking method:

Single-threaded (non-threaded) Intel DAAL

Multi-threaded (internally threaded) Intel DAAL

Static linking





Dynamic linking





These libraries are located in the directory <install dir>/daal/lib.

Regardless of the linking method, also add to your link line the library on which Intel DAAL libraries depend:

  • Intel® Threading Building Blocks run-time library of the Intel® compiler libtbb.dylib

For example, to build your application by statically linking with multi-threaded Intel DAAL:

icc my_first_daal_program.cpp ‑o my_first_daal_program
$DAALROOT/lib/libdaal_core.a $DAALROOT/lib/libdaal_thread.a ‑ltbb -ldl

Step 4: Build and Run Intel DAAL Code Examples

  1. Build an example:

    Go to the C++ examples directory and execute the make command:

    cd <install dir>/daal/examples/cpp

    make {libia32|dylibia32|libintel64|dylibintel64}





    Among the {libia32|dylibia32|libintel64|dylibintel64} parameters, choose the one that matches the architecture parameter you provided to the script and has the prefix that matches the type of executables you want to build: lib for static and dylib for dynamic executables.

    The names of the examples are available in the daal.lst file.

    The command creates a directory for the chosen compiler, architecture, and library extension (a or dylib). For example: _results/intel_intel64_a.

  2. Run an example:

    Go to the C++ examples directory and execute the make command in the run mode. For example, if you ran the daalvars script with the intel64 target:

    cd <install dir>/daal/examples/cpp

    make libintel64 example=cholesky_batch.cpp mode=run

    The make command builds the static library for the Intel 64 architecture and cholesky_batch.cpp example with Intel® compiler, assumed by default, and runs the executable.

Java* Language

Build and Run Intel DAAL Code Examples

To build and run Java code examples, use the version of the Java Virtual Machine* corresponding to the architecture parameter you provided to the script during setting environment variables.

  1. Free 4 gigabytes of memory on your system.
  2. Build examples:

    Go to the Java examples directory and execute the launcher command with the build parameter:

    cd <install dir>/daal/examples/java build $PATH_TO_JAVAC

    The command builds executables *.class (for example, CholeskyBatch.class) in the

    <install dir>/daal/examples/java/com/intel/daal/examples/<example name> directory.

  3. Run examples:

    Go to the Java examples directory and execute the launcher command with the run parameter:

    cd <install dir>/daal/examples/java {ia32|intel64} run $PATH_TO_JAVAC

    Choose the same architecture parameter as you provided to the script.

    The output for each example is written to the file <example name>.res located in the ./_results/ia32 or ./_results/intel64 directory, depending on the specified architecture.

Python* Language

Step 1: Set Up the Build Environment

Set up the C++ build environment as explained under C++ Language.

Step 2: Install Intel DAAL for Python

Go to the directory with Python sources of Intel DAAL and run the install script:

cd <install dir>/pydaal_sources

<python home>/python install

This script compiles code using Intel DAAL for C++. It builds and installs the pyDAAL package for using Intel DAAL in Python programs.

Step 3: Run Intel DAAL Code Examples

To run Intel DAAL code examples, use the same version of Python as you used to install pyDAAL.

  • Go to the directory with Intel DAAL Python examples:

    cd <install dir>/examples/python

  • To run all the examples, execute the command:

    <python home>/python

    The output for each example is written to the ./_results/intel64/<example name>.res file.

  • To run one specific example, execute the command:

    <python home>/python <algorithm name>/<example name>.py

    For example: /usr/local/bin/python3.5.1/python cholesky/

    This command prints the output to your console.

Training and Documentation

To learn more about the product, see the following resources:



Online Training

Get access to Intel DAAL in-depth webinars and featured articles.

Developer Guide for Intel® Data Analytics Acceleration Library:

Find recommendations on programming with Intel DAAL, including performance tips.

Intel® Data Analytics Acceleration Library API Reference

View detailed Application Programming Interface (API) descriptions for the following programming languages:

  • C++
  • Java*
  • Python*

Intel® Data Analytics Acceleration Library Installation Guide

Learn about installation options available for the product and get installation instructions.

Intel® Data Analytics Acceleration Library Release Notes

Learn about:

  • New features of the product
  • Directory layout
  • Hardware and software requirements

<install dir>/daal/examples folder

Get access to the collection of programs that demonstrate usage of Intel DAAL application programming interfaces.

Intel® Data Analytics Acceleration Library code samples

Get access to the collection of code samples for various algorithms that you can include in your program and immediately use with Hadoop*, Spark*, message-passing interface (MPI), or MySQL*.

Intel® Software Documentation Library

View full documentation library for this and other Intel software products.


You can also download an offline version of the documentation from the Intel® Software Development Products Registration Center>Product List><suite name> Documentation.

Optimization Notice

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804

For more complete information about compiler optimizations, see our Optimization Notice.