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使用Intel® C++ Compiler编译浮点应用时的相关因素权衡

 

    总的来说,浮点应用有以下几个考量目标

  • 精度:应用最后计算出的结果与理论结果一致.
  • 可重复性及可移植性:应用在多个平台或架构上,多次运行的结果依然保持一致相同.
  • 性能:应用计算所需的运行时间.

    用户在编写含浮点计算的应用时,应在以上的考量目标中做适当的折中。例如,在开发3D图形引擎的情况下,性能可能是要考虑的最重要的因素,并且可重现性及精度可能是相对次要的考量因素。

  • Разработчики
  • Linux*
  • Microsoft Windows* (XP, Vista, 7)
  • C/C++
  • Intel® C++ Composer XE
  • Intel® Atom™ processors support in Intel® Math Kernel Library


    Intel® Math Kernel Library (Intel® MKL) 11.2 includes support for the latest 32-bit and 64-bit Intel® Atom™ processors.

    Intel® Atom™ processors supporting Supplemental Streaming SIMD Extensions 3 (Intel® SSSE3), Intel® Streaming SIMD Extensions 4.1 (Intel® SSE4.1) or Intel® Streaming SIMD Extensions 4.2 (Intel® SSE4.2) instruction sets will run out-of-the-box with Intel® MKL. Intel® MKL’s internal dispatching mechanism identifies which instruction sets the Intel® Atom™ processor supports and automatically selects the optimal code path to execute during run time.

  • C/C++
  • MKL
  • Using Intel® MKL in math intensive Java applications on Intel® Xeon Phi

    This document explaints how to use Intel MKL with Java* to speed up computation by making use of the Intel® Xeon Phi coprocessors.
  • Linux*
  • C/C++
  • Java*
  • Библиотека Intel® Math Kernel Library
  • java mkl
  • mkl with java on xeon phi
  • java and mkl on mic
  • Improve Intel MKL Performance for Small Problems: The Use of MKL_DIRECT_CALL

    One of the big new features introduced in the Intel MKL 11.2 is the greatly improved performance for small problem sizes. In 11.2, this improvement focuses on xGEMM functions (matrix multiplication). Out of the box, there is already a version-to-version improvement (from Intel MKL 11.1 to Intel MKL 11.2). But on top of it, Intel MKL introduces a new control that can lead to further significant performance boost for small matrices. Users can enable this control when linking with Intel MKL by specifying "-DMKL_DIRECT_CALL" or "-DMKL_DIRECT_CALL_SEQ".

  • Разработчики
  • Профессорский состав
  • Apple OS X*
  • Linux*
  • Microsoft Windows* (XP, Vista, 7)
  • Microsoft Windows* 8
  • Unix*
  • Сервер
  • C/C++
  • Fortran
  • Продвинутый
  • Начинающий
  • Средний
  • Библиотека Intel® Math Kernel Library
  • small matrix
  • performance
  • Оптимизация
  • Significant performance improvement of symmetric eigensolvers and SVD in Intel MKL 11.2

     

    Intel MKL 11.2 contains a number of optimizations for Symmetric Eigensolvers and SVD. These mostly related to large matrices N>4000, 6000, and on but speedups are significant comparing to the previous MKL 11.1.  SVD brings up to 6 times (or even higher on large thread counts and matrix sizes), similarly for eigensolvers, several times could be observed.

    List of related optimizations present in MKL 11.2 are:

  • Библиотека Intel® Math Kernel Library
  • SVD performance in MKL
  • 英特尔®XDK “视界”:Cordova第三方插件和Web Service

    欢迎各位朋友来到新一期的《视界》,我们每一期的《视界》都会跟随XDK的更新以及开发者朋友们的反馈,不定期地为大家带来视频教程。这一期的《视界》栏目,我们将通过两段视频来讲讲如何为你的应用添加Cordova第三方插件以及如何在Service tab里配置Web Service。

    为你的应用添加第三方Cordova插件

    英特尔XDK已经内置了大多数常用的Cordova插件。但有时你需要将一些特别的功能加入到你的应用里,这时你就需要添加第三方的Cordova插件了。添加第三方插件的方式有两种,一种是从本地导入,另一种是从网上的Cordova插件库或者其他地址导入。接下来看看这段简短的视频,了解一下如何使用Projects tab下的Cordova插件选项来添加第三方插件:

  • Разработчики
  • Студенты
  • Android*
  • Apple iOS*
  • Apple OS X*
  • Linux*
  • Microsoft Windows* (XP, Vista, 7)
  • Microsoft Windows* 8
  • HTML5
  • HTML5
  • JavaScript*
  • Intel® XDK
  • html5
  • web services
  • Cordova* Plugins
  • Инструменты для разработки
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