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BigDL:一种面向 Apache Spark 的分布式深度学习库

As the leading framework for Distributed ML, the addition of deep learning to the super-popular Spark framework is important, because it allows Spark developers to perform a wide range of data analysis tasks—including data wrangling, interactive queries, and stream processing—within a single framework. Three important features offered by BigDL are rich deep learning support, High Single Node Xeon Performance, and Efficient scale-out leveraging Spark architecture.
  • Artificial Intelligence
  • Code Samples
  • Big Data
  • Machine Learning
  • Recipe: Building and Running MILC on Intel® Xeon® Processors and Intel® Xeon Phi™ Processors

    MILC software represents a set of codes written by the MIMD Lattice Computation collaboration used to study quantum chromodynamics, the theory of the strong interactions of subatomic physics. This article provides instructions for code access, build, and run directions for the “ks_imp_rhmc” application on Intel® Xeon® processors and Intel® Xeon Phi™ processors.
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  • Linux*
  • Modern Code
  • Intel® MPI Library
  • Intel® Omni-Path Host Fabric Interface
  • Intel® Advanced Vector Extensions (Intel® AVX)
  • OpenMP*
  • Intel® Many Integrated Core Architecture
  • Optimization
  • Vectorization
  • Installing and Building MXNet with Intel® MKL

    The latest version of MXNet includes built-in support for the Intel® Math Kernel Library (Intel® MKL) 2017. The latest version of the Intel MKL includes optimizations for Intel® Advanced Vector Extensions 2 (Intel® AVX2) and AVX-512 instructions which are supported in Intel® Xeon® processor and Intel® Xeon Phi™ processors.
  • Artificial Intelligence
  • Intel® Math Kernel Library
  • Code Samples
  • Machine Learning
  • BigDL: Distributed Deep Learning on Apache Spark*

    As the leading framework for Distributed ML, the addition of deep learning to the super-popular Spark framework is important, because it allows Spark developers to perform a wide range of data analysis tasks—including data wrangling, interactive queries, and stream processing—within a single framework. Three important features offered by BigDL are rich deep learning support, High Single Node Xeon Performance, and Efficient scale-out leveraging Spark architecture.
  • Artificial Intelligence
  • Code Samples
  • Big Data
  • Machine Learning
  • What to Do When Auto-Vectorization Fails?

    This article completes an analysis of a problem erroneously reported on the Intel® Developer Zone forum: Vectorization failed because of unsigned integer? It provides a more detailed examination showing that unsigned integer is not impacting compiler vectorization but what methodology to use when a modern C/C++ compiler fails to auto-vectorize for-loops.
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  • Linux*
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  • Microsoft Windows* 8.x
  • Modern Code
  • Server
  • C/C++
  • Advanced
  • Intel® C++ Compiler
  • Debugging
  • Optimization
  • Parallel Computing
  • Vectorization
  • Unreal Engine* 4: 设置 Destructive Mesh

    The following is a quick guide on getting a PhysX* Destructible Mesh (DM) working setup in an Unreal Engine* 4 (UE4*) project. This guide is primarily based on personal trial and error; other methods may exist that work better for your project. See official documentation for tutorials on fracturing and troubleshooting if you would like to go more in depth with Destructive Mesh capabilities.
  • Game Development
  • Unreal Engine* 4: 制定布料模拟 CPU 优化蓝图

    Realistic cloth movement can bring a great amount of visual immersion into a game. Using PhysX* Clothing* is one way to do this without the need of hand animating. Incorporating these simulations into Unreal Engine* 4 is easy, but as it is a taxing process on the CPU, it’s good to understand their performance characteristics and how to optimize them.
  • Game Development
  • Resetting the lowest n set bits

    Already a couple of years ago, the Bit Manipulation Instruction Set 1 (BMI1) introduced the instruction BLSR, which resets the lowest bit that is set. (The corresponding intrinsic _blsr_u32/64 wraps this instruction with some nice C/C++ function call syntax.) However, what are your options when you not only want to delete one bit, but a given number of bits n? This blog presents multiple variations of this theme including a performant implementation.

    使用英特尔® Edison 模块控制机器人

    介绍

    未来的愿景正在逐步成为现实。 或许,机器人将会有一天接管整个世界,但是现在,我们仍然需要通过互联网对其进行远程控制。 在本文中,我们将创建一个 HTML 页面,以便帮助用户使用 MQTT 发送命令,进而控制机器人移动,并通过机器人身上的网络摄像头查看周围环境。  这种远程控制功能为我们在未来添加更多的特性奠定了基础。

    The DFRobot Devastator Robot fully assembled

    图 1: 完全组装的 DFRobot Devastator Robot

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  • Arduino
  • Yocto Project
  • Internet of Things
  • C/C++
  • HTML5
  • JavaScript*
  • Beginner
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  • MQTT
  • html
  • DFRobot Devastator
  • Remote Control
  • robotics
  • paho
  • Microcontrollers
  • Sensors
  • 更智能的安全摄像头: 利用英特尔® 物联网网关进行概念验证 (PoC)

    简介

    物联网 (IoT) 给我们的生活带来了新鲜有趣的体验,但挑战也随之而来,例如,如何分析、理解这些不断生成的数据流。 多个安全摄像头(用于监控)的使用是物联网在家庭领域的一个趋势,这些摄像头拍摄图像和视频时生成了大量的数据。 例如,一个家庭安装了 12 个摄像头,每天拍摄 180,000 张图像,便会生成 5 GB 的数据。 面对如此多的数据,人工分析变得不切实际。 一些摄像头安装了内置的运动传感器,只有检测到变化时才会拍照。尽管减少了数据,但是仍会捕捉到光线变化和其他微不足道的移动,并存储数据。 为了更智能地监控家庭,OpenCV* 提供了理想的解决方案。 本文旨在分辨人和面部。 OpenCV* 包含许多预先定义的算法,可以搜索面部、人和物体的图像,也可以通过训练识别新的图像。 

    本文是一篇概念验证,探索了借助英特尔® 物联网网关的计算能力,快速构建边缘分析解决方案的原型,创建更智能的安全摄像头。

     

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  • Cloud Computing
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