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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.
Authored by Gennady F. (Blackbelt) Last updated on 07/05/2019 - 14:54
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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
Authored by Igor F. (Intel) Last updated on 07/06/2019 - 16:40
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Free access to Intel® Compilers, Performance libraries, Analysis tools and more...

Intel® Parallel Studio XE is a very popular product from Intel that includes the Intel® Compilers, Intel® Performance Libraries, tools for analysis, debugging and tuning, tools for MPI and the Intel® MPI Library. Did you know that some of these are available for free? Here is a guide to “what is available free” from the Intel Parallel Studio XE suites.
Authored by admin Last updated on 03/21/2019 - 12:00
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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.
Authored by Last updated on 07/06/2019 - 16:40
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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.

Authored by Vadim Pirogov (Intel) Last updated on 03/21/2019 - 12:00
Blog post

Big Datasets from Small Experiments

Authored by Andrey Vladimirov Last updated on 07/04/2019 - 18:46