I see that DAAL comes with its own smart pointer implementation (daal::SharedPtr). I have a couple of questions about this:

1. It appears not to be thread-safe - is this correct?

2. Sometimes I have a SharedPtr<NumericTable> that I would like to cast to a SharedPtr<HomogenNumericTable> so that I can use the getArray method (for example). With a C++11 shared_pointer, I would use dynamic_pointer_cast. Is there a way to do something similar here?

3. Are there any plans to make changes to the SharedPtr implementation between the beta and the release?

Many thanks!

In kmeans_types.h, kmeans::Result allocates instances of HomogenNumericTable to hold the results.

Is there an example showing how to get the algorithm to store its result in SOANumericTable instances?

Thanks.

ACS

Intel® Data Analytics Acceleration Library (Intel® DAAL) provides pre-built binary libraries ready to be linked into C++ applications. Linking is supported by all C++ compilers. This article shows linking examples using the Intel C/C++ compiler and GCC. For information on how to use Intel DAAL with Microsoft Visual Studio, refer to the online article Getting Started with Intel Data Analytics Acceleration Library for Windows

We assume the following environment variables are defined:

DAALROOT=<installation location of DAAL>
DAALINCLUDE=$DAALROOT/include
# For Intel64 architectures
DAALLIB=$DAALROOT/lib/intel64
# For IA-32 architectures
DAALLIB=$DAALROOT/lib/ia32

Linking with Intel C/C++ Compiler

Dynamic linking

The easiest way of linking is to use the '-daal' (Linux and OS X) or the '/Qdaal' (Windows) switch. This by default tells the compiler to use threaded (parallel) DAAL, which is equivalent to '-daal=parallel' (Linux and OS X) or '/Qdaal=parallel' (Windows). To specify using the sequential (single-threaded) DAAL, use '-daal=sequential' (Linux and OS X) or '/Qdaal=sequential' (Windows). For example,

• On Linux or OS X
  
  icc -I$DAALINCLUDE daal_test.cpp -daal 
  icc -I$DAALINCLUDE daal_test.cpp -daal=parallel
  icc -I$DAALINCLUDE daal_test.cpp -daal=sequential
• On Windows
  
  icc /I%DAALINCLUDE% daal_test.cpp /Qdaal
  icc /I%DAALINCLUDE% daal_test.cpp /Qdaal=parallel
  icc /I%DAALINCLUDE% daal_test.cpp /Qdaal=sequential

Static linking

On Linux or OS X, Intel DAAL static library files include libdaal_core.a, libdaal_thread.a, and libdaal_sequential.a. On Windows, Intel DAAL static library files include daal_core.lib, daal_thread.lib, and daal_sequential.lib. When linking with parallel Intel DAAL, the runtime libraries of OpenMP (libiomp5) and Intel Threading Building Blocks (libtbb) are also needed

• On Linux or OS X
  
  icc -I$DAALINCLUDE daal_test.cpp $DAALLIB/libdaal_core.a $DAALLIB/libdaal_thread.a -liomp5 -ltbb -lpthread -lm
  icc -I$DAALINCLUDE daal_test.cpp $DAALLIB/libdaal_core.a $DAALLIB/libdaal_sequential.a -lpthread -lm
• On Windows
  
  icc /I%DAALINCLUDE% daal_test.cpp /Qopenmp /Qtbb %DAALLIB%\daal_core.lib %DAALLIB%\daal_thread.lib 
  icc /I%DAALINCLUDE% daal_test.cpp %DAALLIB%\daal_core.lib %DAALLIB%\daal_sequential.lib

​Linking with GCC 

Dynamic linking

• On Linux or OS X
  
  g++ -I$DAALINCLUDE daal_test.cpp -L$DAALLIB -ldaal_core -ldaal_thread -liomp5 -ltbb -lpthread -lm
  g++ -I$DAALINCLUDE daal_test.cpp -L$DAALLIB -ldaal_core -ldaal_sequential -lpthread -lm

​Static linking

• On Linux or OS X
  
  g++ -I$DAALINCLUDE daal_test.cpp $DAALLIB/libdaal_core.a $DAALLIB/libdaal_thread.a -liomp5 -ltbb -lpthread -lm
  g++ -I$DAALINCLUDE daal_test.cpp $DAALLIB/libdaal_core.a $DAALLIB/libdaal_sequential.a -lpthread -lm

Running applications built with Intel DAAL

Regardless of linking method, applications linked with Intel DAAL require threading runtime libraries to run. On Linux or OS X systems, make sure the environment variable LD_LIBRARY_PATH contains architecture specific directories with libiomp5.so and libtbb.so. On Windows systems, make sure the environment variable PATH contains architecture specific directories with libiomp5md.dll and tbb.dll. 

If your application links with dynamic Intel DAAL, then LD_LIBRARY_PATH (Linux and OS X) and PATH (Windows) also need to contain architecture specific directories with DAAL runtime libraries. 

The easiest way of setting up environment properly is to use the scripts provided by Intel DAAL:

• On Linux or OS X:
  
  source $DAALROOT/bin/daalvars.sh ia32  (for IA-32 architectures)
  source $DAALROOT/bin/daalvars.sh intel64 (for Intel64 architectures)
• On Windows:
  %DAALROOT%\bin\daalvars.bat ia32 (for IA-32 architectures)
• %DAALROOT%\bin\daalvars.bat intel64 (for Intel64 architectures)

Intel® Data Analytics Acceleration Library (Intel® DAAL) provides a Java API and the ease-of-use for Java programmers. This article discusses how to build and run applications with the Eclipse IDE (one of the most popular Java IDEs). The procedures outlined in this article should also be applicable to other Java IDEs. 

If you want to build and run Java applications from the command line, see instructions from Getting Started with Intel Data Analytics Acceleration Library.

Import Intel DAAL Java Packages

To make Intel DAAL classes and methods available to your application, we need to import necessary Intel DAAL packages. For example,

import com.intel.daal.algorithms.pca.*;
import com.intel.daal.data_management.data.NumericTable;
import com.intel.daal.data_management.data_source.FileDataSource;
import com.intel.daal.data_management.data_source.DataSource;
import com.intel.daal.services.*;

Configure Java Build Path

Java build path configuration can be accessed from Properties (right-clicking the project name).

• Step 1. Make sure the Intel DAAL Java native library is included as part of source by setting "Native library location" to the directory containing JavaAPI.dll. The picture below shows an example on Windows. On Linux or OS X, set it to the directory containing JavaAPI.so.
   
• Step 2. Make sure the Libraries tab contains daal.jar on the build path, as shown below. 
  
  

Configure Run/Debug Settings

Run/Debug settings are also accessible from project Properties.

• Step 1. Create a new launch configuration and give it a name.
  
  
   
• Step 2. In the Edit Configuration pop-up window, make sure the Main class on the Main tab is properly set.
  
  
   
• Step 3. Still in the Edit Configuration pop-up window, go to the Classpath tab and make sure daal.jar is included. 
  
  
   
• Step 4. At last, go to the Environment tab and add a Path environment variable and set its value to be the directories containing, respectively, DAAL runtime library, OpenMP runtime library, and Intel TBB runtime library. Here's an example on Windows:
  
  C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\redist\intel64_win\daal;
  C:\Program Files (x86)\IntelSWTools\\compilers_and_libraries\windows\redist\intel64_win\compiler;
  C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\redist\intel64_win\tbb\vc_mt 
  
  Note on Linux and OS X, the environment variable to be added should be LD_LIBRARY_PATH; and similarly its value should be the paths containing the DAAL runtime library, the OpenMP runtime library, and the Intel TBB runtime library.
  
  Make sure Append environment to native environment is selected, and click Apply to save the changes.
   

These are the settings specifically necessary for using Intel DAAL in our Java project. We should now be able to build, run, and debug our project from within Eclipse. Refer to the online documentation to learn more about Intel DAAL.

Intel® Data Analytics Acceleration Library (Intel® DAAL) is a new highly optimized library targeting data mining, statistical analysis, and machine learning applications. It provides advanced building blocks supporting all data analysis stages. Intel DAAL supports three processing modes, batch processing, online processing, and distributed processing.

Online processing, a.k.a. streaming, is applicable when data is processed in blocks. This can be helpful if the entire dataset is too big to fit in memory all at once; or if the data is only available piecemeal.

Introduction

Some Intel DAAL algorithms enable processing of data sets in blocks. In the online processing mode, thecompute(), and finalizeCompute() methods of a particular algorithm class are used. This computation mode assumes that the data arrives in blocks i = 1, 2, 3, … nblocks. Call the compute() method each time new input becomes available. When the last block of data arrives, call the finalizeCompute() method to produce final results. If the input data arrives in an asynchronous mode, you can use the getStatus() method for a given data source to check whether a new block of data is available for load.

The following diagram illustrates the computation schema for online processing:

Example

Online processing typically involves a loop. Each iteration of the loop fetches one data block and computes the partial result for the current block. Then, outside the loop, partial results are combined to produce the overall results for all data blocks processed. The code snippet below illustrates the situation:

/* Create algorithm to compute SVD decomposition in online mode */
svd::Online<> algorithm;
Status loadStatus;
while((loadStatus = dataSource.loadDataBlock(nRowsInBlock)) == success)
{
        algorithm.input.set( svd::data, dataSource.getNumericTable() );
        /* Compute SVD algorithm */
        algorithm.compute();
}

/* Finalize the computations and retrieve SVD results */
algorithm.finalizeCompute();
SharedPtr<svd::Result> res = algorithm.getResult();

/* Access results */
printNumericTable(res->get(svd::singularValues), "Singular values:");
printNumericTable(res->get(svd::rightSingularMatrix), "Right orthogonal matrix V:");
printNumericTable(res->get(svd::leftSingularMatrix), "Left orthogonal matrix U:", 10);

Announcement Summary

On August 25^th, Intel Corporation launched Intel® Data Analytics Acceleration Library (Intel® DAAL).  It is a library designed to help software developers reduce development time and boost performance of data analytics and machine learning applications. Intel® DAAL helps applications make better predictions faster, and analyze larger data sets with the available compute resources at hand. Intel® DAAL is designed, built and backed by Intel to help delight data scientists in their quest to efficiently turn Big Data into Information. Intel® DAAL was created by the same team that creates Intel® Math Kernel Library (Intel® MKL) which is renowned for both precision and performance. Those same renowned computational capabilities, that drive Intel® MKL, have been adopted, expanded and outfitted to handle the special needs of Big Data to create this new product Intel® DAAL. For Data Scientists, Intel® MKL and Intel® DAAL make a powerful combination for tackling Big Data.

For more details, see the Release Notes.

There is a webinar series running September 1 through November 10 (all available for replay afterwards) with a series of talks by Intel experts on the new tool capabilities and covering popular usage questions - http://tinyurl.com/webinars-intel2016.  The Intel DAAL talk is scheduled for September 29, 2015.

Availability

Intel® Data Analytics Acceleration Library is available starting August 25^th, 2015.

Available as a standalone product or as part of Intel® Parallel Studio XE 2016

You can obtain as part of Intel fully supported suites, or you may qualify for free versions of such suites if you are an educator, open source developer or researcher, or you can obtain Intel DAAL with a Community License at no charge.

• Intel fully supported suites: Intel DAAL is part of a suite of Intel Software Development Products which are available worldwide for purchase.  These products are  backed by Intel, include Intel® Premier Support (which includes private direct support from Intel and support for past versions). Learn more at Intel Parallel Studio XE Try/Buy
• You may qualify for free versions of such suites if you are an student, educator, open source developer or academic researcher: learn about requirement to qualify at software.intel.com/qualify-for-free-software
• Community Licenses for Intel Performance Libraries: Community licensing for Intel performance libraries comes with no access to Intel Premier support, but (like the regular products) has no royalties, and no restrictions based on company or project size. Learn how to register for Community Licenses at software.intel.com/nest

System Environment

Intel® DAAL version : 2016 Gold Initial Release (w_daal_2016.0.110.exe)

OS : Windows 8.1

IDE : Visual Studio 2013

Overview

 Intel® DAAL is a part of Intel® Parallel Studio XE 2016, a developer toolkit for HPC and technical computing applications. Intel® DAAL is a powerful library for big data developers that turns large data clusters into meaningful information with advanced analytics algorithms.

 In this tutorial, we will see how to build and run Intel® DAAL C++ examples included in the package.
 

Finding the examples

 By default, the examples are located at

c:\Program Files (x86)\intelSWTools\compilers_and_libraries\windows\daal\examples

in 'examples' directory, there are example projects in C++ and Java language respectively. The example data according to three different models, batch, distributed and online, for the projects is located in 'data' directory.

 It is recommended to copy the examples to somewhere else where you don't need the administrator access in order to make it easy to modify, build and run.

Building the examples

  First, open the 'DAALExamples.sln' solution file located in

 <DAAL Examples>\cpp

Select all projects and open up 'Properties' -> Clink 'Configuration Manager' at the upper right corner -> Change active solution platrofm according to your platform. (e.g. Win32 -> x64 )

As the next step, we need to enable DAAL for the projects. Propertise -> Intel Performance Libraries -> Use Intel DAAL -> Choose 'Default Linking Method'

Click the build menu -> Build Solution ( or Ctrl + Shitf + B )

Running the examples

 There is a couple more steps to take before running DAAL applications.

 First, to setup DAAL environment variables, run 'daalvars.bat <arch>'

 Second,set redist runtime dlls ->

PATH=C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2016.0.110\windows\redist\intel64_win\daal;
C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2016.0.110\windows\redist\intel64_win\compiler;
C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2016.0.110\windows\redist\intel64_win\tbb\vc_mt;%PATH%

Now go to <examples>\cpp and run one of the examples

 then the results will be printed out

System Environment

Intel® DAAL version : 2016 Gold Initial Release (w_daal_2016.0.110.exe)

OS : Windows 8.1

Overview

 Intel® DAAL is a part of Intel® Parallel Studio XE 2016, a developer toolkit for HPC and technical computing applications. Intel® DAAL is a powerful library for big data developers that turns large data clusters into meaningful information with advanced analytics algorithms.

 In this tutorial, we will see how to build and run Intel® DAAL Java examples included in the package.

for C++ examples please refer -> Tutorial for Intel® DAAL : Using simple C++ examples

Finding the examples

 By default, the examples are located at

c:\Program Files (x86)\intelSWTools\compilers_and_libraries\windows\daal\examples

in 'examples' directory, there are example projects in C++ and Java language respectively. The example data according to three different models, batch, distributed and online, for the projects is located in 'data' directory.

 It is recommended to copy the examples to somewhere else where you don't need the administrator access in order to make it easy to modify, build and run.

Building the examples

  First, to setup DAAL environment variables, run 'daalvars.bat <arch>'

  Second, we need to setup path for the location of 'javac.exe'. Type

set JAVA_HOME=<Your JDK location >

set PATH=%JAVA_HOME%\bin;%PATH%

 Now (go to <examples>\java ) type

launcher.bat <arch> build

Then .class files will be generated at

<examples>\java\com\intel\daal\examples\association\rules

Running the examples

 To run the examples, set redist runtime dlls ->

PATH=C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2016.0.110\windows\redist\intel64_win\daal;
C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2016.0.110\windows\redist\intel64_win\compiler;
C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2016.0.110\windows\redist\intel64_win\tbb\vc_mt;%PATH%

 Now go to <examples>\java and run one of the examples by typing

launcher.bat <arch> run

The results from the examples will be saved at

<examples>\java\_results

each example will generate a result file respectively, and you can see the result by opening .res file.

e.g.)

Intel MKL is a popular math library used by many to create fast and reliable applications in science, engineering, and finance. Do you know it is now available for free (at no cost)? The community licensing program gives anyone, individuals or organizations, free license for the latest version of Intel MKL. There is no royalty for distributing the library in an application. The only restrictions are:

• Online registration is required.
• No access to Intel Premier Support, but the Intel MKL support forum is open to everyone.
• No access to the previous versions. At any time, only the latest released version is available.

Better yet, this community licensing program is not just for MKL, it is applicable to other Intel performance libraries including:

• Intel Data Analytics Acceleration Library (Intel DAAL)
• Intel Integrated Performance Primitives (Intel IPP)
• Intel Threading Building Blocks (Intel TBB)

Visit https://software.intel.com/sites/campaigns/nest/ to get yourself started and to learn the program's details

More Intel software tools, such as the Intel Compilers, Intel MPI,  and tools for debugging and performance tuning, may also be available for free (at no cost) if you are qualified as an academic researcher, teacher, student, or an open source contributor. See https://software.intel.com/en-us/free_tools_and_libraries.

Do you know Intel Data Analytics Acceleration Library is now available for free (at no cost)? The community licensing program gives anyone, individuals or organizations, free license for the latest version of Intel DAAL. There is no royalty for distributing the library in an application. The only restrictions are:

• Online registration is required.
• No access to Intel Premier Support, but the Intel DAAL support forum is open to everyone.
• No access to the previous versions. At any time, only the latest released version is available.

Better yet, this community licensing program is not just for DAAL, it is applicable to other Intel performance libraries including:

• Intel Math Kernel Library (Intel MKL)
• Intel Integrated Performance Primitives (Intel IPP)
• Intel Threading Building Blocks (Intel TBB)

Visit https://software.intel.com/sites/campaigns/nest/ to get yourself started and to learn the program's details

More Intel software tools, such as the Intel Compilers, Intel MPI,  and tools for debugging and performance tuning, may also be available for free (at no cost) if you are qualified as an academic researcher, teacher, student, or an open source contributor. See https://software.intel.com/en-us/free_tools_and_libraries.

Please join us in this webinar for an introduction to the new Intel® Data Analytics Acceleration Library.

Date: Sept. 29th, 2015

Time: 9:00 AM - 10:00 AM (PDT) 

Registration link: https://attendee.gotowebinar.com/register/4639372089237168897 

Hi, I've been trying to get acclimated with Intel's release of several academic focused compiler and software tools. I'm in the process of messing around with the libraries and I think there is a problem with the integration of the installed tool and Microsoft Visual Studio 2015. Although I do see the options for working with Intel's software tools, etc. it doesn't seem as if I can select that I would like to use the Intel compiler like when it is already specified in the example files.

After messing around with it for some time within visual studio 2015, etc. I decided that there might be something within the installation directory that I could use to configure the compiler and variables if a system variable or something wasn't configured properly. Unfortunately, running the bat file located in C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2016\windows\bin throws an error indicating that the script has trouble determining the location of Visual Studio's Common Tools. I would like to get this configured and running with the version of Visual Studio 2015 I am running (Visual Studio 2015 Community Edition).

If there are some system variables it might be beneficial to set or useful visual studio configurations I would appreciate hearing them.

Intel® Math Kernel Library (Intel® MKL) is a highly optimized, extensively threaded, and thread-safe library of mathematical functions for engineering, scientific, and financial applications that require maximum performance. Intel MKL 11.2 Update 4 packages are now ready for download. Intel MKL is available as part of the Intel® Parallel Studio XE 2015 and Intel® System Studio 2015 . Please visit the Intel® Math Kernel Library Product Page .

Intel® MKL 11.2 Update 4 Bug fixes

New Features in MKL 11.2 Update 4:

• Reduced memory consumption when using Intel MKL in GNU OMP parallel regions
• BLAS
  • Improved parallel and serial performance of ?TRSM on Intel® Advanced Vector Extensions 2 (Intel® AVX2) for the 64-bit Intel MKL
  • Improved serial performance of STRMM for small triangular matrix (dimension less than or equal to 10) on Intel® AVX2 for 64-bit Intel MKL
  • Improved ?SYRK/?HERK/?SYR2K/?HER2K performance for beta=0 on Intel® Advanced Vector Extensions 2 (Intel® AVX2) for the 64-bit Intel MKL
  • Improved performance of BLAS level 3 functions for second generation of Intel Xeon Phi coprocessors

Check out the Release Notes

Contents

• File:  l_mkl_online_11.2.4.223.sh

  Online Installer for Linux

• File: l_mkl_11.2.4.223.tgz

  A File containing the complete product installation for Linux (32-bit/x86-64bit development)

Intel® Math Kernel Library (Intel® MKL) is a highly optimized, extensively threaded, and thread-safe library of mathematical functions for engineering, scientific, and financial applications that require maximum performance. Intel MKL 11.2 Update 4 packages are now ready for download. Intel MKL is available as part of the Intel® Parallel Studio XE 2015 and Intel® System Studio 2015 . Please visit the Intel® Math Kernel Library Product Page .

Intel® MKL 11.2 Update 4 Bug fixes

New Features in MKL 11.2 Update 4:

• Reduced memory consumption when using Intel MKL in GNU OMP parallel regions
• BLAS
  • Improved parallel and serial performance of ?TRSM on Intel® Advanced Vector Extensions 2 (Intel® AVX2) for the 64-bit Intel MKL
  • Improved serial performance of STRMM for small triangular matrix (dimension less than or equal to 10) on Intel® AVX2 for 64-bit Intel MKL
  • Improved ?SYRK/?HERK/?SYR2K/?HER2K performance for beta=0 on Intel® Advanced Vector Extensions 2 (Intel® AVX2) for the 64-bit Intel MKL
  • Improved performance of BLAS level 3 functions for second generation of Intel Xeon Phi coprocessors

Check out the Release Notes

Contents

• File:  w_mkl_11.2.4.280_online.exe

  Online Installer for Windows

• File: w_mkl_11.2.4.280.exe

  A File containing the complete product installation for Windows (32-bit/x86-64bit development)

Intel MKL Users,

We recently discovered that some BLAS, FFT, and sparse BLAS routines produce incorrect results under certain circumstances. We believe the scope of the issues is limited and that they impact few use cases. Please read the description of each issues below to determine if you are impacted. These issues will be addressed in the upcoming Intel MKL 11.3.1 release. If you do not want to wait for the release of Intel MKL 11.3.1, a hotfix is available to replace your current installation by contacting us at intel.mkl@intel.com. Please include your license serial number in all correspondence.

1. {S,D}GEMM may give incorrect results for beta=0 cases.
   1. Scope: Only affects Intel64 architectures with the AVX2 instruction set during multithreaded execution. For DGEMM, this affects matrices with N < 4000 and M/nthreads > 5004. For SGEMM, this affects matrices with N < 4000 and M/nthreads > 10008.
   2. Present in Intel MKL versions: 11.2.1, 11.2.2, 11.2.3, 11.2.4, and 11.3
2. {S,D}SYMM may give incorrect results for beta=0 cases.
   1. Scope: Only affects Intel64 architectures with the AVX2 instruction set for both multithreaded and sequential execution. For DSYMM, this affects matrices with M/nthreads > 5004. For SSYMM, this affects matrices with M/nthreads > 10008.
   2. Present in Intel MKL versions: 11.2.1, 11.2.2, 11.2.3, 11.2.4, and 11.3
3. 2D real-to-complex FFT produces incorrect results for some non-power-of-2 sizes.
   1. Scope: Affects transform sizes 22+8*n for double precision and transform sizes 30+16*n for single precision.
   2. Present in Intel MKL version: 11.3
4. Sparse matrix-vector multiplication using the two-stage, inspector-executor API produces incorrect results.
   1. Scope: Only affects the CSR sparse matrix storage format during multithreaded execution. The problem arises when 'mkl_sparse_?_mv' routine is called after calls of the 'mkl_sparse_optimize' routine.
   2. Present in Intel MKL version: 11.3

If you believe you are affected by any one of these issues and wish to receive the hotfix, please send a message to intel.mkl@intel.com. Please include your license serial number. Follow this link for instructions on finding your license serial number. 

Hi, Guys,

When i use intel mkl library, i met a strange problem.

Preparation:
I built liabaray A (it would call cblas_sgemm function), using libmkl_intel_thread.so.

The routine to get the wrong result:
I built application B which depend on library A, if in the makefile I included libmkl_intel_thread.so as dependency, I ran the application B, it could not get the right result.

The routine to get the wrong right:
I built application B which depend on library A, if in the makefile I included libmkl_sequential.so as dependency. While if i built application B, it could get the right result. But in this way, i could not get the benefit from multi-thread matrix computing.

Thanks~
 

Intel® DAAL 2016 Update 1  (Nov 2015)

Building examples with Microsoft* Visual Studio 2012 and later

On Windows, if you

• Use Microsoft Visual Studio version higher than 2012
• Install DAAL to the default directory
• Converted Microsoft Visual Studio project with Intel DAAL examples to the format of your version of Microsoft Visual Studio

Then, when you build the examples, an issue may occur caused by the long command-line issue (intermediate Visual Studio folder/files).

Workaround: Go to Project->Properties->General and shorten the value of the Intermediate Folder setting. For example: change $(Platform)\$(Configuration)\outlier_detection_multivariate_default_batch\ to $(Platform)\$(Configuration)\o_d_m_d_b\.  

Running samples for Apache Spark* on OS X

Intel DAAL samples for Spark do not work on OS X. 

Workaround: For OS X prior to El Capitan (version 10.11), users can run the following commands before executing the samples:

        $ install_name_tool -change "@rpath/libtbb.dylib""libtbb.dylib" libJavaAPI.dylib

        $ install_name_tool -change "@rpath/libiomp5.dylib""libiomp5.dylib" libJavaAPI.dylib

No workaround exists for OS X El Capitan. This issue will be resolved in the next update release.

Intel MKL Benchmarks package includes Intel® Optimized LINPACK Benchmark,  Intel® Optimized MP LINPACK Benchmark for Clusters, and Intel® Optimized High Performance Conjugate Gradient Benchmark from Intel MKL 11.3. Use the links in the table below to download package for Linux*, Windows* or OS X*.

By downloading any sample package you accept the End User License Agreement