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英特尔® 至强® 处理器和英特尔® 至强融核™ 协处理器利用通用代码实现多线程方阵转置

In-place matrix transposition, a standard operation in linear algebra, is a memory bandwidth-bound operation. The theoretical maximum performance of transposition is the memory copy bandwidth. However, due to non-contiguous memory access in the transposition operation, practical performance is usually lower. The ratio of the transposition rate to the memory copy bandwidth is a measure of the transposition algorithm efficiency.
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  • Intel® Software Guard Extensions Tutorial Series: Part 8, GUI Integration

    In Part 8 we integrate the GUI with the back end. We examine implications of mixing managed code with enclaves and how to mitigate the potential for undermining security gained from Intel® SGX.
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    在英特尔® 至强融核™ 协处理器上微调矢量化和内存流量:对小型矩阵进行 LU 分解

    Common techniques for fine-tuning the performance of automatically vectorized loops in applications for Intel® Xeon Phi™ coprocessors are discussed. These techniques include strength reduction, regularizing the vectorization pattern, data alignment and aligned data hint, and pointer disambiguation.
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  • BigDL:一种面向 Apache Spark* 的分布式深度学习库

    深度学习作为分布式机器学习的主要框架,将其添加至颇为常用的 Spark 框架具有重要意义,有助于 Spark 开发人员在单个框架内处理各种数据分析任务—包括数据处理、交互式查询和数据流处理。BigDL 提供三个重要特性,分别是丰富的深度学习支持、较高的单节点至强性能以及利用 spark 架构实现高效的横向扩展。
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  • 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. This article provides instructions for code access, build and run directions for the “ks_imp_rhmc” application on Intel® Xeon® Gold and Intel® Xeon Phi™ processors for better performance on a single node.
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  • Installing and Building MXNet with Intel® MKL

    The latest version of MXNet includes built-in support for the Intel® Math Kernel Library (Intel® MKL) 2018. 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. The Intel® Math Kernel Library for Deep Neural Networks(Intel® MKL-DNN) is a new open source library designed to accelerate Deep Learning (DL) applications on Intel® architecture. It includes functionality similar to Intel® Math Kernel Library (Intel® MKL) with additional optimizations for Deep Learning workloads.
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  • 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.
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  • 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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  • 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.
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