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Vectorization

vectorization

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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
by Espen Harlinn
A fast, reference counted, copy-on-write string class
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.
by Dávid Kocsis
A new general purpose language that aims to be fast, high level and simple to use.

Latest Articles

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
by Espen Harlinn
A fast, reference counted, copy-on-write string class
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.
by Dávid Kocsis
A new general purpose language that aims to be fast, high level and simple to use.

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vectorization 

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
by Espen Harlinn
A fast, reference counted, copy-on-write string class
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.
by Dávid Kocsis
A new general purpose language that aims to be fast, high level and simple to use.
by Kumar_Shiv, Rahul Kandu
Boosting Java Performance in Big Data Applications
by Mario Mulansky
Using Boost.odeint together with Boost.SIMD to gain a factor three performance improvements.
by MehreenTahir
This article will help you build different types of basic recommendation systems using Python.
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.
by Intel
Using the Latest Intel® Software Development Tools to Make More Efficient Use of Hardware
by Matt Scarpino
This article explains how to perform mathematical SIMD processing in C/C++ with Intel's Advanced Vector Extensions (AVX) intrinsic functions.
by Android on Intel
Development and Optimization for NDK-based Android Game Application on Platforms based on Intel® Architecture
by Rama Kishan Malladi
Optimizing SPECFEM3D_GLOBE Performance on Intel® Architecture
by Intel
Optimizing SPECFEM3D_GLOBE Performance on Intel® Architecture
by Intel
Getting Good Data to Make Code Tuning Decisions
by r.stropek
Graphic in XAML and WPF
by Intel
Making Parallel Programming Accessible to C/C++ and Fortran Programmers―and Providing a Software Path to Exascale Computation
by Intel
Gain Insights into How Well Your Application is Vectorized Using Intel® Advisor
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.
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.
by Intel
How to Improve Scalability for Intel® Xeon and Intel® Xeon Phi™ Processors Using New Intel® VTune™ Amplifier Memory Analysis
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.
by Tolga Birdal
This article demonstrates the utilization of C# for basic image processing algorithms
by Intel
Installing the Intel® Distribution for Python and Intel® Performance Libraries with pip and PyPI
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.
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.
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.
by Intel
A New Way to Visualize Performance Optimization Trade-Offs
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.
by Intel
The JuliaProject Continues to Break New Boundaries in Scientific Computing
by Intel
Get Results with the Intel® Data Analytics Acceleration Library and the Latest Intel® Xeon Phi™ Processor
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.
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
by Intel
Let’s take a look at benchmarking the Louvain algorithm.
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
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
by Intel
Dispelling the Myths with Tools to Achieve Parallelism
by Intel
In this article, we’ll explore how to achieve parallelism through Numba.
by Intel
In this article, we’ll explore how to refactor Python code to take advantage of NumExpr’s capabilities.
by Android on Intel
Thanks to an x86-based Android tablet Intel loaned me for testing, PhonoPaper has been improved and optimized.
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.
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.
by Intel
Understanding How Your Program is Accessing Memory Helps You Get More from Your Hardware
by tugrulGtx
A small tool for writing various algorithms as if they were CUDA/OpenCL kernels
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.
by Shao Voon Wong
Using SSE2 to speed up alphablending.
by Intel
Step by Step Performance Optimization with Intel® C++ Compiler
by ed welch
How well does XCode's auto vectorization work in practice?
by Intel
How the Gold Standard Parallel Programming Language Has Improved with Each New Version
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.
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.
by Intel
Best Practices for Taking Advantage of the Latest Architectural Features
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
by Intel
This article presents use cases and provides examples which make use of the Intel® Math Kernel Library (Intel® MKL)
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.
by Intel
Open Source Code WARP3DExemplifies Renewed Interest in Vectorization
by Intel
Examples of How Intel® Compilers Can Vectorize and Speed Up Loops
by Shao Voon Wong
Benchmark between OpenMP, Parallel Patterns Library, Auto-Parallelizer and C++17 Parallel for_each
by Marius Bancila
In this article, I will enumerate and discuss some of the new or improved features for native development (but not all).
by Intel
I'm working on a connectivity library for IoT devices. A serious part of every communication protocol is the data integrity check.
by Member 4201813
Jump forward/backward procedures for XorShift RNG explained step by step