Case Study

借助英特尔集成显卡,优化提升PC版 Halo War*2 性能

当来自英国的顶级工作室 Creative Assembly* 开始开发 Halo Wars* 2 时,他们的目标很宏伟,他们希望游戏在 DirectX* 12 支持的各种设置上运行,在各个硬件层面上都具有较强的可玩性——包括高级台式机 PC 配置和笔记本电脑。 尽管通过一系列的优化,配有独立显卡的高端系统的游戏体验在不断增强,他们团队仍将进一步探索针对英特尔集成显卡和多核处理功能如何进行游戏优化。
  • Game Development
  • Graphics
  • Optimizations Enhance Halo Wars* 2 For PCs with Intel Integrated Graphics

    When top UK-based studio Creative Assembly* began their ambitious work on Halo Wars* 2, they wanted the game to run on a variety of settings supported by DirectX* 12, and to be playable up and down the hardware ladder. While many of the optimizations also enhanced the game for high-end systems with discrete graphics cards, this white paper will explore the team’s efforts for Intel® Integrated Graphics and multicore processing functions.
  • Game Development
  • Graphics
  • Why threading matters for Ashes of the Singularity*

    By building a new engine and using Direct3D* 12, Oxide made it possible for Ashes of the Singularity to use all available processor cores. It runs great on a typical gaming system and scales up to run even better on systems with more cores. You can use these same techniques in your game to get the best performance from your CPU.
  • Game Development
  • Graphics
  • 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。

     

  • Professional
  • Wind River* Linux*
  • Yocto Project
  • Internet of Things
  • Python*
  • Intermediate
  • Cloud Computing
  • Microcontrollers
  • Security
  • Sensors
  • License Agreement: 

    应用蚁群优化算法 (ACO) 实施交通网络扩展

    In this article an OpenMP* based implementation of the Ant Colony Optimization algorithm was analyzed for bottlenecks with Intel® VTune™ Amplifier XE 2016 together with improvements using hybrid MPI-OpenMP and Intel® Threading Building Blocks were introduced to achieve efficient scaling across a four-socket Intel® Xeon® processor E7-8890 v4 processor-based system.
  • Professional
  • Professors
  • Students
  • Linux*
  • Artificial Intelligence
  • Modern Code
  • Server
  • C/C++
  • Intermediate
  • Message Passing Interface (MPI)
  • OpenMP*
  • Ant Colony Optimization (ACO)
  • Cluster Computing
  • Development Tools
  • Machine Learning
  • Parallel Computing
  • Threading
  • Case Study – Using the Intel® Deep Learning SDK for Training Image Recognition Models

    In this case study, we explore LeNet*,one of the prominent image recognition topologies for handwritten digit recognition, and show how the training tool can be used to visually set up, tune, and train the Mixed National Institute of Standards and Technology (MNIST) dataset on Caffe* optimized for Intel® architecture. Data scientists are the intended audience.
  • Students
  • Artificial Intelligence
  • Deep Learning Training Tool from Intel
  • Machine Learning
  • 经验和启示:Duskers 荣获英特尔® 进阶游戏大赛“其他类游戏”大奖

    One of the greatest strengths in the independent game-development industry is the universal belief in trying something radically different. When they succeed, “indie” games don’t just tweak the edges—they blow up the boundaries, and go where their vision takes them, no matter what.
  • Game Development
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