Vectorization

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6 Tools from Intel to Speed up Deep Neural Networks

by Stephan Ofosuhene

This article takes a look at a variety of tools available from Intel: Intel® Movidius™ Neural Compute Stick, Intel® Python Distribution for Python™, Intel® Math Kernel DNN Library, Intel® Data Analytics Acceleration Library, Intel Distribution of OpenVINO™ Toolkit

A C++ String Class

by Espen Harlinn

A fast, reference counted, copy-on-write string class

Benchmarking .NET Core SIMD Performance vs. Intel ISPC

by Allister Beharry

.NET SIMD programs using the Vector types show performance comparable to Intel ISPC and open source C++ SIMD libraries while satisfying the same goal of SIMD developer productivity in a high-level language.

Bird Programming Language: Part 1

by Dávid Kocsis

A new general purpose language that aims to be fast, high level and simple to use.

Latest Articles

6 Tools from Intel to Speed up Deep Neural Networks

by Stephan Ofosuhene

A C++ String Class

by Espen Harlinn

A fast, reference counted, copy-on-write string class

Benchmarking .NET Core SIMD Performance vs. Intel ISPC

by Allister Beharry

Bird Programming Language: Part 1

by Dávid Kocsis

A new general purpose language that aims to be fast, high level and simple to use.

All Articles

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vectorization

6 Tools from Intel to Speed up Deep Neural Networks

by Stephan Ofosuhene

A C++ String Class

by Espen Harlinn

A fast, reference counted, copy-on-write string class

Benchmarking .NET Core SIMD Performance vs. Intel ISPC

by Allister Beharry

Bird Programming Language: Part 1

by Dávid Kocsis

A new general purpose language that aims to be fast, high level and simple to use.

Boosting Java Performance in Big Data Applications

by Kumar_Shiv, Rahul Kandu

Boosting Java Performance in Big Data Applications

Boosting ODE simulations with Boost.odeint and Boost.SIMD

by Mario Mulansky

Using Boost.odeint together with Boost.SIMD to gain a factor three performance improvements.

Building Recommendation Systems using Python

by MehreenTahir

This article will help you build different types of basic recommendation systems using Python.

Caffe Optimized for Intel® Architecture: Applying Modern Code Techniques

by Intel

This paper demonstrates a special version of Caffe — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.

Code Modernization in Action: Thread, Memory, and Vector Optimizations

by Intel

Using the Latest Intel® Software Development Tools to Make More Efficient Use of Hardware

Crunching Numbers with AVX and AVX2

by Matt Scarpino

This article explains how to perform mathematical SIMD processing in C/C++ with Intel's Advanced Vector Extensions (AVX) intrinsic functions.

Development and Optimization for NDK-based Android Game Application on Platforms based on Intel® Architecture

by Android on Intel

Development and Optimization for NDK-based Android Game Application on Platforms based on Intel® Architecture

Effectively Using Your Whole Cluster

by Rama Kishan Malladi

Optimizing SPECFEM3D_GLOBE Performance on Intel® Architecture

Effectively Using Your Whole Cluster

by Intel

Optimizing SPECFEM3D_GLOBE Performance on Intel® Architecture

Gaining Performance Insights Using the Intel® Advisor Python API

by Intel

Getting Good Data to Make Code Tuning Decisions

Graphic in XAML and WPF

by r.stropek

Graphic in XAML and WPF

Happy 20th Birthday, OpenMP

by Intel

Making Parallel Programming Accessible to C/C++ and Fortran Programmers―and Providing a Software Path to Exascale Computation

How Effective is Your Vectorization?

by Intel

Gain Insights into How Well Your Application is Vectorized Using Intel® Advisor

How the Lustre Developer Community is Advancing ZFS as a Lustre Back-end File System

by Intel

Several open source projects that are being integrated into open source Lustre are designed to improve reliability, flexibility, and performance, align the enterprise-grade features built into ZFS with Lustre, and enhance functionality that eases Lustre deployments on ZFS.

HPC with R: The Basics

by Intel

We’ll begin with a little history and some basics that everyone who picks up R and cares about performance ought to know.

Identify Scalability Problems in Parallel Applications

by Intel

How to Improve Scalability for Intel® Xeon and Intel® Xeon Phi™ Processors Using New Intel® VTune™ Amplifier Memory Analysis

Illusionary XAML: Reinventing Benham's Top

by Sergey Alexandrovich Kryukov

The mystery of Benham’s top invented in 1895, as well as Fechner color effect, remains not fully uncovered so far. WPF and XAML help to accelerate the research greatly.

Image Processing Basics in C#

by Tolga Birdal

This article demonstrates the utilization of C# for basic image processing algorithms

Installing the Intel® Distribution for Python and Intel® Performance Libraries with pip and PyPI

by Intel

Installing the Intel® Distribution for Python and Intel® Performance Libraries with pip and PyPI

Intel Advisor - Roofline Analysis of Vectorized Methods and Recommendations

by Jeffrey T. Fritz

In this video, Jeff analyzes a simple set of loops and arrays with Intel's Advisor. We generate a roofline analysis chart and examine the recommendations to improve the CPU and memory usage patterns in our code.

Intel AVX: New Frontiers in Performance Improvements and Energy Efficiency

by Intel Software Network

This paper will provide a brief background on ISA, and then give an overview of the new instructions and capabilities of the Intel AVX and advantages of these innovative instructions across various applications and programming models.

Intel® Advisor Review

by theonemule

To fully take advantage of parallelization features, developers have to change how they code. But a great deal of optimizations can be made through Intel’s parallelization tool, Intel Advisor.

Intel® Advisor Roofline Analysis

by Intel

A New Way to Visualize Performance Optimization Trade-Offs

Intel® System Studio 2017 Beta

by Android on Intel

Intel® System Studio 2017 Beta has been released. This is the Beta program page which guides you further on Intel® System Studio 2017 Beta new features and enhanced usability experience.

Julia: A High-Level Language for Supercomputing

by Intel

The JuliaProject Continues to Break New Boundaries in Scientific Computing

Lightning-Fast R Machine Learning Algorithms

by Intel

Get Results with the Intel® Data Analytics Acceleration Library and the Latest Intel® Xeon Phi™ Processor

Measuring Graph Analytics Performance

by Intel, Henry Gabb

A graph is a good way to represent a set of objects and the relations between them. Graph analytics is the set of techniques to extract information from connections between entities.

Modernize Your Code for Intel® Xeon Phi™ Processors

by Intel

The new Intel® Xeon Phi™ processor (code-named Knights Landing, or KNL) is Intel’s first processor to deliver the performance of an accelerator with the benefits you’ve come to expect from a standard CPU

More Adventures in Graph Analytics Benchmarking

by Intel

Let’s take a look at benchmarking the Louvain algorithm.

More Efficient Numerical Simulation

by Intel

Novosibirsk State University boosts a simulation tool’s performance by 3X with Intel® Parallel Studio, Intel® Advisor, and Intel® Trace Analyzer and Collector

Optimizing Application Performance with Roofline Analysis

by Intel

NERSC boosts the performance of its scientific applications on Intel® Xeon Phi™ processors up to 35% using Intel® Parallel Studio and Intel® Advisor

Parallelism in Python

by Intel

Dispelling the Myths with Tools to Achieve Parallelism

Parallelism in Python Using Numba

by Intel

In this article, we’ll explore how to achieve parallelism through Numba.

Parallelism in Python: Directing Vectorization withNumExpr

by Intel

In this article, we’ll explore how to refactor Python code to take advantage of NumExpr’s capabilities.

PhonoPaper Optimization using Intel® tools

by Android on Intel

Thanks to an x86-based Android tablet Intel loaned me for testing, PhonoPaper has been improved and optimized.

Python Code Analysis

by Intel

Explore performance analysis options provided by the Intel® VTune Amplifier for Python applications to identify the most time-consuming code sections and critical call paths.

Real-time End-to-End H.265/HEVC Solution for Intel® Architecture-based Platforms

by Android on Intel

In this paper, we will investigate the HEVC codec characters and optimize the CPU-based software video trans-coding technologies，which provide the best video quality and the most flexible programming model.

Remove Memory Bottlenecks Using Intel® Advisor

by Intel

Understanding How Your Program is Accessing Memory Helps You Get More from Your Hardware

Simulating CUDA/OpenCL on CPU, with Auto-vectorization and Speedup against Serial Scalar Code

by tugrulGtx

A small tool for writing various algorithms as if they were CUDA/OpenCL kernels

Speed System & IoT Device Application Development with New Intel® System Studio 2018

by Intel

Intel just released Intel® System Studio 2018, an all-in-one, cross-platform, comprehensive tool suite for system and IoT device application development.

SSE2 Vectorization of Alphablend Code

by Shao Voon Wong

Using SSE2 to speed up alphablending.

Step by Step Performance Optimization with Intel® C++ Compiler

by Intel

Step by Step Performance Optimization with Intel® C++ Compiler

Testing Xcode's auto-vectorization

by ed welch

How well does XCode's auto vectorization work in practice?

The Present and Future of the OpenMP API Specification

by Intel

How the Gold Standard Parallel Programming Language Has Improved with Each New Version

Tips and Tricks to Optimize Android Apps on x86

by Android on Intel

This article will focus on optimizing NDK based Apps. These may be solely C/C++ code or may include 3rd party libraries and/or assembly code.

Trusted Tools in the New Android* World: Optimization Techniques - from Intel® SSE Intrinsics to Intel® Cilk™ Plus

by Android on Intel

This paper will discuss optimization needs and approaches on Android and walk through a case study of how to optimize a multimedia and augmented reality application.

Tuning for Success with the Latest SIMD Extensions and Intel® Advanced Vector Extensions 512

by Intel

Best Practices for Taking Advantage of the Latest Architectural Features

Unleash The Power Of Big Data Analytics And Machine Learning

by Intel

To help innovators tackle the complexities of machine learning, we are making performance optimizations available to developers through familiar Intel® software tools, specifically through the Intel® Data Analytics Acceleration Library (Intel® DAAL) and enhancements to the Intel® Math Kernel Library

Using Intel® Math Kernel Library with Arduino Create

by Intel

This article presents use cases and provides examples which make use of the Intel® Math Kernel Library (Intel® MKL)

Using the Emscripten Compiler with the Intel® XDK

by Android on Intel

Intel(R) XDK is an HTML5 Cross-platform Development Tool and provides an easy and fast way to get your apps to market. Emscripten Compiler and Intel XDK now gives you another option to publish apps using C and C++ as part of the application.

Vectorization Becomes Important―Again

by Intel

Open Source Code WARP3DExemplifies Renewed Interest in Vectorization

Vectorization Opportunities for Improved Performance with Intel® AVX-512

by Intel

Examples of How Intel® Compilers Can Vectorize and Speed Up Loops

Visual C++ 11 Beta Benchmark of Parallel Loops

by Shao Voon Wong

Benchmark between OpenMP, Parallel Patterns Library, Auto-Parallelizer and C++17 Parallel for_each

What's New in VS11 for Native Development

by Marius Bancila

In this article, I will enumerate and discuss some of the new or improved features for native development (but not all).

What's Wrong With My CRC?

by Intel

I'm working on a connectivity library for IoT devices. A serious part of every communication protocol is the data integrity check.

XorShift Jump 101, Part 2: Polynomial Arithmetic

by Member 4201813

Jump forward/backward procedures for XorShift RNG explained step by step