Mensajes en el blog

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.

Autor gaston-hillar (Blackbelt) Última actualización 06/07/2019 - 17:00
Article

Palestra: Como otimizar seu código sem ser um "ninja" em Computação Paralela

Não perca a palestra "Como otimizar seu código sem ser um "ninja" em Computação Paralela" da Intel que será ministrada durante a Semana sobre Programação Massivamente Paralela em Petrópolis, RJ, no Laboratório Nacional de Computação Científica. Data: 02/02/2016 - 11h30 Local: LNCC - Av. Getúlio Vargas, 333 - Quitandinha - Petrópolis/RJ
Autor Igor F. (Intel) Última actualización 06/07/2019 - 16:40
Article

Using Intel® Data Analytics Acceleration Library to Improve the Performance of Naïve Bayes Algorithm in Python*

This article discusses machine learning and describes a machine learning method/algorithm called Naïve Bayes (NB) [2]. It also describes how to use Intel® Data Analytics Acceleration Library (Intel® DAAL) [3] to improve the performance of an NB algorithm.
Autor Nguyen, Khang T (Intel) Última actualización 06/07/2019 - 16:40
Article

Caffe* Optimized for Intel® Architecture: Applying Modern Code Techniques

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
Autor Última actualización 06/07/2019 - 16:40
Article

Introducing DNN primitives in Intel® Math Kernel Library

Please notes: Deep Neural Network(DNN) component in MKL is deprecated since intel® MKL ​2019 and will be removed in the next intel® MKL Release.

Autor Vadim Pirogov (Intel) Última actualización 21/03/2019 - 12:00
Article

面向英特尔® 架构优化的 Caffe*:使用现代代码技巧

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
Autor Última actualización 06/07/2019 - 16:40
Article

利用英特尔® 数据分析加速库提高 Python* 语言中朴素贝叶斯算法的性能

This article discusses machine learning and describes a machine learning method/algorithm called Naïve Bayes (NB) [2]. It also describes how to use Intel® Data Analytics Acceleration Library (Intel® DAAL) [3] to improve the performance of an NB algorithm.
Autor Nguyen, Khang T (Intel) Última actualización 06/07/2019 - 16:30
Article

在英特尔® 数学核心函数库中引入 DNN 基元

    深度神经网络 (DNN) 处于机器学习领域的前沿。这些算法在 20 世纪 90 年代后期得到了行业的广泛采用,最初应用于诸如银行支票手写识别等任务。深度神经网络在这一任务领域已得到广泛运用,达到甚至超过了人类能力。如今,DNN 已用于图像识别、视频和自然语言处理以及解决复杂的视觉理解问题,如自主驾驶等。DNN 在计算资源及其必须处理的数据量方面要求非常苛刻。

Autor Vadim Pirogov (Intel) Última actualización 21/03/2019 - 12:08
Article

Vector API Developer Program for Java* Software

This article introduces Vector API to Java* developers. It shows how to start using the API in Java programs, and provides examples of vector algorithms. It provides step-by-step details on how to build the Vector API and build Java applications using it. It provides the location for downloadable binaries for Project Panama binaries.
Autor Neil V. (Intel) Última actualización 06/07/2019 - 16:30
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

Autor JON J K. (Intel) Última actualización 27/08/2019 - 13:50