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Article

利用英特尔高级矢量扩展指令集进行 Wiener 过滤

Wiener filtering (also known as Least Mean Square filtering) is a technique for removing unwanted noise from an image. This article discusses Wiener filtering, and includes an example of code that has been optimized using Intel® AVX
Автор: Последнее обновление: 11.12.2018 - 13:12
Article

Pointer Checker to Debug Buffer Overruns and Dangling Pointers (Part 1)

Article Topic

Pointer Checker to debug buffer overruns and dangling pointers

Автор: Последнее обновление: 27.03.2019 - 15:08
Article

Threading Intel® Integrated Performance Primitives Image Resize with Intel® Threading Building Blocks

Threading Intel® IPP Image Resize with Intel® TBB.pdf (157.18 KB) :
Автор: Jeffrey M. (Intel) Последнее обновление: 31.07.2019 - 15:05
Блоги

Processing Arrays of Bits with Intel® Advanced Vector Extensions 512 (Intel® AVX-512)

As announced last week by James, future Intel

Автор: Thomas Willhalm (Intel) Последнее обновление: 04.07.2019 - 19:30
Article

Reference Implementations for Intel® Architecture Approximation Instructions VRCP14, VRSQRT14, VRCP28, VRSQRT28, and VEXP2

We are providing source files containing reference implementations for the scalar versions of 10 approximation instructions introduced in the "Intel® Architecture Instruction Set Extensions Programming Reference" document
Автор: админ Последнее обновление: 08.05.2019 - 17:32
Article

Putting Your Data and Code in Order: Data and layout - Part 2

Apply the concepts of parallelism and distributed memory computing to your code to improve software performance. This paper expands on concepts discussed in Part 1, to consider parallelism, both vectorization (single instruction multiple data SIMD) as well as shared memory parallelism (threading), and distributed memory computing.
Автор: David M. Последнее обновление: 06.07.2019 - 16:40
Article

Приводим данные и код в порядок: данные и разметка, часть 2

In this pair of articles on performance and memory covers basic concepts to provide guidance to developers seeking to improve software performance. This paper expands on concepts discussed in Part 1, to consider parallelism, both vectorization (single instruction multiple data SIMD) as well as shared memory parallelism (threading), and distributed memory computing.
Автор: David M. Последнее обновление: 06.07.2019 - 16:40
Article

Recognize and Measure Vectorization Performance

Get a background on vectorization and learn different techniques to evaluate its effectiveness.
Автор: David M. Последнее обновление: 06.07.2019 - 16:40
Article

Virtual Vector Function Supported in Intel® C++ Compiler 17.0

Intel® C++ Compiler 17.0 starts supporting virtual vector functions.

Автор: Chen, Yuan (Intel) Последнее обновление: 01.06.2017 - 11:32
Article

Set Up Intel® Software Optimization for Theano* and Supporting Tools

Get recipes for installing development tools and libraries on various platforms for the Python library.
Автор: Sunny G. (Intel) Последнее обновление: 08.05.2018 - 10:50