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Optimizing Big Data processing with Haswell 256-bit Integer SIMD instructions

Big Data requires processing huge amounts of data. Intel Advanced Vector Extensions 2 (aka AVX2) promoted most Intel AVX 128-bits integer SIMD instruction sets to 256-bits.

Authored by gaston-hillar (Blackbelt) Last updated on 07/06/2019 - 17:00
Article

What is Intel® DAAL?

The Intel® Data Analytics Acceleration Library (Intel® DAAL) is the library of Intel® Architecture optimized building blocks covering all data analytics stages: data acquisition from a data source, preprocessing, transformation, data mining, modeling, validation, and decision making. To achieve best performance on a range of Intel® processors, Intel DAAL uses optimized algorithms from the Intel®...
Authored by Vipin Kumar E K (Intel) Last updated on 10/08/2018 - 06:30
Article

A Walk-Through of Distributed Processing Using 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 (preprocessing, transformation, analysis, modeling, decision making) for offline, streaming and distributed analytics usages. Intel DAAL support...
Authored by Ying H. (Intel) Last updated on 10/04/2018 - 04:16
Article

A Tutorial on the C++ API of Intel® Data Analytics Acceleration Library

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.
Authored by Zhang, Zhang (Intel) Last updated on 07/06/2019 - 10:53
Article

A Tutorial on the Java API of Intel® Data Analytics Acceleration Library

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.
Authored by Zhang, Zhang (Intel) Last updated on 07/06/2019 - 10:54
Article

Tutorial for Intel® DAAL : Using Simple Java* Examples

System Environment

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

OS : Windows 8.1

Authored by JON J K. (Intel) Last updated on 07/06/2019 - 11:41
Article

IDF'15 Webcast: Data Analytics and Machine Learning

This Technology Insight will demonstrate how to optimize data analytics and machine learning workloads for Intel® Architecture based data center platforms. Speaker: Pradeep Dubey Intel Fellow, Intel Labs Director, Parallel Computing Lab, Intel Corporation
Authored by Mike P. (Intel) Last updated on 07/06/2019 - 16:40
Article

Performance Comparison of OpenBLAS* and Intel® Math Kernel Library in R

Today, scientific and business industries collect large amounts of data, analyze them, and make decisions based on the outcome of the analysis. This paper compares the performance of Basic Linear Algebra Subprograms (BLAS), libraries OpenBLAS, and the Intel® Math Kernel Library (Intel® MKL).
Authored by Nguyen, Khang T (Intel) Last updated on 07/06/2019 - 16:40
Article

Live Webinar: Boost Python* Performance with Intel® Math Kernel Library

Python* is a popular open-source scripting language known for its easy-to-learn syntax and active developer community.
Authored by Mike P. (Intel) Last updated on 06/07/2017 - 10:28
Article

How to Install the Python* Version of Intel® Data Analytics Acceleration Library (Intel® DAAL) in Linux*

The Intel® Data Analytics Acceleration Library (Intel® DAAL) 1, 2 is a software solution for data analytics. It provides building blocks for data preprocessing, transformation, modeling, predicting, and so on.
Authored by Nguyen, Khang T (Intel) Last updated on 07/05/2019 - 19:05