Artigo técnico

Fluid Simulation for Video Games (Part 20)

This article presented a fluid simulation that combines integral and differential numerical techniques to achieve an algorithm that takes time linear in the number of grid points or particles. The overall simulation can’t be faster than that because each particle has to be accessed to be rendered. It also provides better results than the treecode because the latter uses approximations everywhere in the computational domain that the Poisson solver does not, and the Poisson solver has an inherently smoother and more globally accurate solution.
  • Desenvolvimento de jogos
  • Windows*
  • fluid simulation
  • numerical methods
  • algorithm
  • grid points
  • treecode
  • Poisson Solver
  • An Introduction to Intel® Active Management Technology Wireless Connections

    With the introduction of wireless-only platforms starting with Intel Active Management Technology (Intel® AMT) 10, it is even more important for an ISV to integrate support for wireless management of AMT devices. This article will address the Intel AMT wireless configuration and describe how to handle the various aspects that are important for a clean integration.
  • Microsoft Windows* (XP, Vista, 7)
  • Microsoft Windows* 10
  • Microsoft Windows* 8.x
  • Cliente empresarial
  • Windows*
  • Kit de desenvolvimento de software Intel® AMT
  • Intel® Active Management Technology
  • Gerenciamento de dispositivo
  • Tecnologia Intel® vPro™
  • Comparison of Projection Methods for Rendering Virtual Reality

    Virtual reality is rapidly gaining popularity, and may soon become a common way of viewing 3D environments. While stereo rendering has been performed on consumer grade graphics processors for a while now, the new wave of virtual reality display devices have two properties that typical applications have not needed to consider before. Pixels no longer appear on regular grids and the displays subtend a wide field-of-view. In this paper, we evaluate several techniques designed to efficiently render for head-mounted displays with such properties. We show that the amount of rendered pixels can be reduced down to 36% without compromising visual fidelity compared to traditional rendering, by rendering multiple optimized sub-projections.
  • Professores
  • Estudantes
  • Desenvolvimento de jogos
  • Gráficos
  • Renderização
  • Efficient SIMD in Animation with SIMD Data Layout Templates (SDLT) and Data Preconditioning

    In this paper, we walk through a 3D Animation algorithm example and describe some techniques and methodologies that may benefit your next vectorization endeavors. We also integrate the algorithm with SIMD Data Layout Templates (SDLT), which is a feature of Intel® C++ Compiler, to improve data layout and SIMD efficiency. Includes code sample.
  • Modernização de código
  • C/C++
  • Vetorização
  • Recipe: Building and Running MASNUM WAVE for Intel® Xeon Phi™ Processors

    This article provides a recipe for how to obtain, compile, and run an optimized version of MASNUM WAVE (0.2 degree high resolution) workload on Intel® Xeon® processors and Intel® Xeon Phi™ processors.
  • Modernização de código
  • KNL
  • Arquitetura Intel® Many Integrated Core
  • Introducing the Intel® Software Guard Extensions Tutorial Series

    Announcing a new multi-part tutorial series to help software developers integrate Intel® Software Guard Extensions (Intel® SGX) into their applications. The series will guide you through building an Intel SGX application, beginning at application design and running through development, testing, packaging, and deployment. This in-depth look at enabling Intel SGX in a single application provides developers with a hands-on and holistic view of the technology as it is woven into a real-world application.
  • Microsoft Windows* (XP, Vista, 7)
  • Microsoft Windows* 10
  • Microsoft Windows* 8.x
  • Cliente empresarial
  • Windows*
  • Software Guard Extensions
  • Why Efficient Use of the Memory Subsystem Is Critical to Performance

    The cores and vector processors on modern multi-core processors are voracious consumers of data. If you do not organize your application to supply data at high-enough bandwidth to the cores, any work you do to vectorize or parallelize your code will be wasted because the cores will stall while waiting for data. The system will look busy, but not fast.

    The following chart, which reflects the numbers for a hypothetical 4-processor system with 18 cores per processor, shows the:

  • Modernização de código
  • Training and Deploying Deep Learning Networks with Caffe* Optimized for Intel® Architecture

    Caffe* is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC). Caffe optimized for Intel architecture is currently integrated with the latest release of Intel® Math Kernel Library (Intel® MKL) 2017 optimized for Advanced Vector Extensions (AVX)-2 and AVX-512 instructions which are supported in Intel® Xeon® and Intel® Xeon Phi™ processors (among others). This article describes how to build Caffe optimized for Intel architecture, train deep network models using one or more compute nodes, and deploy networks. In addition, various functionalities of Caffe are explored in detail including how to fine-tune, extract and view features of different models, and use the Caffe Python API.
  • Aprendizado de máquina
  • C/C++
  • Python*
  • Principiante
  • Intermediário
  • Caffe
  • deep learning
  • Knights Landing
  • Big Data
  • Otimização
  • Computação paralela
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