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

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Pointer Checker to debug buffer overruns and dangling pointers

Автор: Последнее обновление: 27.03.2019 - 15:08
Article

Pointer Checker to detect buffer overflows and dangling pointers (part 2)

Overview
Автор: Последнее обновление: 27.03.2019 - 15:08
Article

Improve Intel® MKL Performance for Small Problems: The Use of MKL_DIRECT_CALL

One of the big new features introduced in the Intel® Math Kernel Library (Intel® MKL) 11.2 is the greatly improved performance for small problem sizes.

Автор: Zhang, Zhang (Intel) Последнее обновление: 07.07.2019 - 10:35
Article

Implementing a Masked SVML-like Function Explicitly in User-Defined Way

The Intel® Compiler provides SIMD intrinsics APIs for short vector math library (SVML) and starting with Intel® Advanced Vector Extensions

Автор: Последнее обновление: 16.07.2019 - 08:37
Article

Quick Analysis of Vectorization Using Intel® Advisor

Find out how to use the command-line interface in Intel® Advisor 2017 for a quick, initial analysis of loop performance that gives an overview of the hotspots in your code.
Автор: Alberto V. (Intel) Последнее обновление: 30.09.2019 - 17:28
Article

Using Intel® MPI Library on Intel® Xeon Phi™ Product Family

This document is designed to help users get started writing code and running MPI applications using the Intel® MPI Library on a development platform that includes the Intel® Xeon Phi™ processor.
Автор: Nguyen, Loc Q (Intel) Последнее обновление: 15.10.2019 - 15:04
Article

How to use the MPI-3 Shared Memory in Intel® Xeon Phi™ Processors

Code Sample included: Learn how to use MPI-3 shared memory feature using the corresponding APIs on the Intel® Xeon Phi™ processor.
Автор: Nguyen, Loc Q (Intel) Последнее обновление: 15.10.2019 - 15:30
Article

Performance of Classic Matrix Multiplication Algorithm on Intel® Xeon Phi™ Processor System

Matrix multiplication (MM) of two matrices is one of the most fundamental operations in linear algebra. The algorithm for MM is very simple, it could be easily implemented in any programming language. This paper shows that performance significantly improves when different optimization techniques are applied.
Автор: Последнее обновление: 15.10.2019 - 15:30
Article

Recognize and Measure Vectorization Performance

Get a background on vectorization and learn different techniques to evaluate its effectiveness.
Автор: David M. Последнее обновление: 15.10.2019 - 15:30
Article

Getting Started with Intel® Optimization for PyTorch* on Second Generation Intel® Xeon® Scalable Processors

Accelerate deep learning PyTorch* code on second generation Intel® Xeon® Scalable processor with Intel® Deep Learning Boost.
Автор: Nathan Greeneltch (Intel) Последнее обновление: 15.10.2019 - 16:50