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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.

作者: gaston-hillar (Blackbelt) 最后更新时间: 2019/07/06 - 17:00
Article

A Walk-Through of Online 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. Intel DAAL supports three processing modes, batch processing, online processing, and distributed processing. Online processing, a.k.a. streaming, is...
作者: Zhang, Zhang (Intel) 最后更新时间: 2017/06/07 - 10:33
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.
作者: Zhang, Zhang (Intel) 最后更新时间: 2019/07/06 - 10:53
博客

Intel® Data Analytics Acceleration Library

The Intel® Data Analytics Acceleration Library (Intel® DAAL) helps speed big data analytics by providing highly optimized algorithmic building blocks for all data analysis stages (Pre-processing, Transformation, Analysis, Modeling, Validation, and Decision Making) for offline, streaming and distributed analytics usages. It’s designed for use with popular data platforms including Hadoop*, Spark*,...
作者: James R. (Blackbelt) 最后更新时间: 2018/12/12 - 18:00
Article

Tutorial for Intel® DAAL: Using Simple C++ Examples

System Environment

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

OS : Windows* 8.1

IDE : Visual Studio 2013

 

作者: JON J K. (Intel) 最后更新时间: 2019/07/03 - 10:17
博客

The JITter Conundrum - Just in Time for Your Traffic Jam

In interpreted languages, it just takes longer to get stuff done - I earlier gave the example where the Python source code a = b + c would result in a BINARY_ADD byte code which takes 78 machine instructions to do the add, but it's a single native ADD instruction if run in compiled language like C or C++. How can we speed this up? Or as the performance expert would say, how do I decrease...
作者: David S. (Blackbelt) 最后更新时间: 2019/07/04 - 20:00
视频

Faster Big Data Analytics Using New Intel® Data Analytics Acceleration Library

Big data is BIG. There’s just more of it. And you need information faster.

作者: Zhang, Zhang (Intel) 最后更新时间: 2019/06/10 - 09:40
Article

Caffe* Training on Multi-node Distributed-memory Systems Based on Intel® Xeon® Processor E5 Family

Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for image recognition. Caffe is often used as a benchmark together with AlexNet*, a neural network topology for image recognition, and ImageNet*, a database of labeled images.
作者: Gennady F. (Blackbelt) 最后更新时间: 2019/07/05 - 14:54
Article

基于英特尔® 至强™ 处理器 E5 产品家族的多节点分布式内存系统上的 Caffe* 培训

Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for image recognition. Caffe is often used as a benchmark together with AlexNet*, a neural network topology for image recognition, and ImageNet*, a database of labeled images.
作者: Gennady F. (Blackbelt) 最后更新时间: 2019/07/05 - 14:55