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.
Prerequisites
Install Intel DAAL on Your System
Intel DAAL installs in the directory <install dir>/daal.
By default, <install dir> is /opt/intel/compilers_and_libraries_2018.x.xxx/mac.
For installation details, refer to Intel DAAL Installation Guide.
Set Environment Variables
Run the <install dir>/daal/bin/daalvars.sh script as appropriate to your target architecture:
IA-32 architecture:
daalvars.sh ia32
Intel® 64 architecture:
daalvars.sh intel64
Optionally: Specify the Java* compiler different from the default compiler:
export JAVA_HOME=$PATH_TO_JAVA_SDK
export PATH=$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. | Xcode*:
|
‑daal=sequential | 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.
/*******************************************************************************
! Copyright(C) 2014-2017 Intel Corporation. All Rights Reserved.
!
! The source code, information and material ("Material") contained herein is
! owned by Intel Corporation or its suppliers or licensors, and title to such
! Material remains with Intel Corporation or its suppliers or licensors. The
! Material contains proprietary information of Intel or its suppliers and
! licensors. The Material is protected by worldwide copyright laws and treaty
! provisions. No part of the Material may be used, copied, reproduced,
! modified, published, uploaded, posted, transmitted, distributed or disclosed
! in any way without Intel's prior express written permission. No license
! under any patent, copyright or other intellectual property rights in the
! Material is granted to or conferred upon you, either expressly, by
! implication, inducement, estoppel or otherwise. Any license under such
! intellectual property rights must be express and approved by Intel in
! writing.
!
! *Third Party trademarks are the property of their respective owners.
!
! Unless otherwise agreed by Intel in writing, you may not remove or alter
! this notice or any other notice embedded in Materials by Intel or Intel's
! suppliers or licensors in any way.
!
!*******************************************************************************
! 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 */
algorithm.compute();
/* 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;
}
Paste the application code into the editor of your choice.
Save the file as my_first_daal_program.cpp.
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
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 | libdaal_core.a libdaal_sequential.a | libdaal_core.a libdaal_thread.a |
Dynamic linking | libdaal_core.dylib libdaal_sequential.dylib | libdaal_core.dylib libdaal_thread.dylib |
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
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}
example=<example_name>
compiler={intel|gnu|clang}
threading={parallel|sequential}
mode=build
Among the {libia32|dylibia32|libintel64|dylibintel64} parameters, choose the one that matches the architecture parameter you provided to the daalvars.sh 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.
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 daalvars.sh script during setting environment variables.
- Free 4 gigabytes of memory on your system.
Build examples:
Go to the Java examples directory and execute the launcher command with the build parameter:
cd <install dir>/daal/examples/java
launcher.sh 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.
Run examples:
Go to the Java examples directory and execute the launcher command with the run parameter:
cd <install dir>/daal/examples/java
launcher.sh {ia32|intel64} run $PATH_TO_JAVAC
Choose the same architecture parameter as you provided to the daalvars.sh 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 setup.py 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 run_examples.py
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/cholesky_batch.py
This command prints the output to your console.
Training and Documentation
To learn more about the product, see the following resources:
Resource | Description |
|---|---|
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. |
View detailed Application Programming Interface (API) descriptions for the following programming languages:
| |
Intel® Data Analytics Acceleration Library Installation Guide | Learn about installation options available for the product and get installation instructions. |
Learn about:
| |
<install dir>/daal/examples folder | Get access to the collection of programs that demonstrate usage of Intel DAAL application programming interfaces. |
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*. | |
View full documentation library for this and other Intel software products. |
Legal Information
Intel, and the Intel logo are trademarks of Intel Corporation in the U.S. and/or other countries.
*Other names and brands may be claimed as the property of others.
Microsoft, Windows, and the Windows logo are trademarks, or registered trademarks of Microsoft Corporation in the United States and/or other countries.
Java is a registered trademark of Oracle and/or its affiliates.
Copyright 2014-2018 Intel Corporation.
This software and the related documents are Intel copyrighted materials, and your use of them is governed by the express license under which they were provided to you (License). Unless the License provides otherwise, you may not use, modify, copy, publish, distribute, disclose or transmit this software or the related documents without Intel's prior written permission.
This software and the related documents are provided as is, with no express or implied warranties, other than those that are expressly stated in the License.
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 |
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Alex Pothen is a professor of computer science at Purdue University. He led the founding of the Combinatorial Scientific Computing (CSC) research community, which now holds biennial conferences organized through the Society for Industrial and Applied Mathematics (SIAM). He directed the CSCAPES Institute, a pioneering multi-institutional research center for developing parallel graph algorithms on leadership-class supercomputers. He is currently involved in the Exascale Computing Project of the U.S. Department of Energy. He is an editor of the Journal of the ACM, and was editor of SIAM Review and SIAM Books. He has mentored more than twenty PhD students and postdoctoral scholars. He is a Fellow of SIAM.
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