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Composable Threading Coming to Julia*

Henry A Gabb, PhD (Editor, The Parallel Universe)

The July 2017 issue of The Parallel Universe ran an article on Julia: A High-Level Language for Supercomputing. My key takeaways from the article were that Julia has built-in primitives for multithreading and distributed computing and was...

OpenMP* SIMD for Inclusive/Exclusive Scans

Publicado em 21 de setembro de 2018, atualizado em 23 de julho de 2019Por Varsha M.

The Intel® C++ Compiler 19.0 and the Intel® Fortran Compiler 19.1 support the OpenMP* SIMD SCAN feature for inclusive and exclusive scans.

Pre-Release Intel® Parallel Studio XE 2020 Composer Edition Technical Preview Release Notes

Publicado em 30 de abril de 2019, atualizado em 27 de junho de 2019Por Ronald W Green

Release Notes for the Pre-Release Intel® Parallel Studio XE 2020 Composer Edition Technical Preview

Managing Volatile Memory

Memorable Persistent Memory Articles from Intel for June

This month manage volatile memory with Memkind and learn more about PMEM's long term strategy. 

Intel® Xeon® Processor Scalable Family Technical Overview

Publicado em 10 de julho de 2017, atualizado em 10 de junho de 2019Por David Mulnix

The new generation, the Intel® Xeon® processor Scalable family (formerly code-named Skylake-SP), is based on 14nm process technology, with many new and enhanced architecture changes including, Skylake Mesh Architecture and Intel® Advanced Vector Extensions 512 (Intel® AVX-512).

Top Ten Intel® Software Developer Stories for June

Get more details on Intel® Optane™ DC persistent memory and learn more about creating deep learning inference applications.

The Best of Modern Code from Intel

Points on pointer aliasing and explicit vector programming this month.

Best of Modern Code for May

Brush up on your vectorization essentials this month.

Top Ten May

Top Ten Intel® Software Developer Stories for May

This month, we explore vision accelerators and vector extensions. Plus, learn about Ben, a self-driving robot.

Accelerating Compute-Intensive Workloads with Intel® Advanced Vector Extensions 512 (Intel® AVX-512) Using Microsoft Visual Studio*

Some examples of how Intel® Advanced Vector Extensions 512 (Intel® AVX-512) provides performance benefits when using Microsoft Visual Studio* 2017

Explicit Vector Programming – Best Known Methods

Publicado em 18 de março de 2014, atualizado em 24 de abril de 2019

Vectorizing improves performance, and achieving high performance can save power. Introduction to tools for vectorizing compute-intensive processing.

Pointer Aliasing and Vectorization

Publicado em 24 de abril de 2019Por Barbara P

Pointer aliasing in C++ inhibits vectorization, other optimizations, and, hence, performance. Read how to help the compiler work smarter.

Best of Modern Code for April

More vectorization, optimization and transformations for you this month.

Code Sample: Intel® AVX512-Deep Learning Boost: Intrinsic Functions

How developers can use to take advantage of the new Intel® AVX512-Deep Learning Boost (Intel® AVX512-DL Boost) instructions.

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Second Generation Intel® Xeon® Processor Scalable Family Technical Overview

New features and enhancements available in the second generation Intel® Xeon® processor Scalable family and how developers can take advantage of them

Using the Intel® MPI Library in Google Cloud Platform*

Publicado em 28 de março de 2019Por Fabio B.

In this article, we describe the process on how to download and install the Intel® MPI library in Google Cloud Platform* (GCP). This allows you to run MPI workloads on the cloud service provider. Furthermore we highlight also the technique and the process to build a...

Cache Blocking Techniques

Publicado em 7 de novembro de 2013, atualizado em 26 de março de 2019Por AmandaS

Cache Blocking Techniques
Overview

An important class of algorithmic changes involves blocking data structures to fit in cache. By organizing data memory accesses, one can load the cache with a small subset of a much larger data set. The idea is then to work on this block of data...

Memory Layout Transformations

Publicado em 25 de novembro de 2013, atualizado em 26 de março de 2019Por AmandaS

Memory Layout Transformations Overview

This chapter examines a useful user code transformation: moving from data organized in an Array of Structures (AoS) to an organization of Stucture of Arrays (SoA). This transformation allows the compiler to access data more efficiently on the processor....

Vectorization Toolkit

Publicado em 14 de maio de 2012, atualizado em 25 de março de 2019Por AmandaS

A toolkit that gives 6 Steps to Increase Performance Through Vectorization in Your Application

Vectorization and Optimization Reports

Publicado em 6 de setembro de 2012, atualizado em 25 de março de 2019Por Ronald W Green

Optimization reports from the Intel® compilers guide the developer with optimization details

Getting the Most out of your Intel® Compiler with the New Optimization Reports

Publicado em 8 de outubro de 2014, atualizado em 25 de março de 2019Por Martyn Corden

Intel compiler optimization reports guide the developer to performance improvements

Optimizing Applications using Intel® Compiler for Intel® Xeon Processors

Publicado em 13 de outubro de 2015, atualizado em 25 de março de 2019Por AmandaS

The key to performance measurement is two-fold, know exactly what you are measuring and collect your baseline data. Next, profile your application and identify a specific and realistic performance goal based on the profiling data. Follow these steps to optimize your software.

Vectorization Essentials

Publicado em 6 de dezembro de 2013, atualizado em 22 de março de 2019

Vectorization essentials to effectively use feature in the Intel® Xeon product family

Memorable Persistent Memory Articles from Intel for March

This month handle memory errors and extend memory capacity. 

Best of Modern Code for March

Learn to pinpoint issues and speed up your application with Intel® Advisor this month.

Random Number Function Vectorization

Publicado em 7 de setembro de 2012, atualizado em 8 de março de 2019

Random number function auto-vectorization supported

Avoid Manual Loop Unrolling

Publicado em 9 de setembro de 2012, atualizado em 8 de março de 2019

Generate efficient vectorized code when a loop structure is not manually unrolled

Use CV to identify flowers

Top Ten Intel® Software Developer Stories for March

See how deep learning is used to match jobs with candidates and how computer vision can identify flowers. Find more in this month's top stories.

Utilizing Full Vectors and Use of Option -qopt-assume-safe-padding

Publicado em 7 de setembro de 2012, atualizado em 6 de março de 2019

Vectorization Essentials: Efficient vectorization involves making full use of the vector-hardware in the kernel-vector loop.

Outer Loop Vectorization

Publicado em 7 de setembro de 2012, atualizado em 5 de março de 2019

Vectorization Essentials: Vectorizing the outer loop can be profitable

Requirements for Vectorizable Loops

Publicado em 2 de agosto de 2012, atualizado em 4 de março de 2019Por Martyn Corden

Vectorization is one of many optimizations that are enabled by default in the latest Intel compilers. In order to be vectorized, loops must obey certain conditions, listed below. Some additional ways to help the compiler to vectorize loops are described.

Fortran Array Data and Arguments and Vectorization

Publicado em 6 de setembro de 2012, atualizado em 4 de março de 2019

Examples of vectorizing Fortran applications

Common Vectorization Tips

Common Vectorization Tips

Publicado em 7 de outubro de 2013, atualizado em 4 de março de 2019Por AmandaS

Get tips for common vectorization functions, such as handling user-defined function calls inside vector loops.

Performance essentials using OpenMP* 4.0 vectorization with C/C++

Última atualização: 1 de março de 2019Duração do vídeo: 55 min

http://intel.com/software/products.  This webinar teaches you about vectorization, what it is and why you should care about it as a software developer.

Episode 4.1 - SIMD Parallelism and Intrinsics

Part 1: SIMD Parallelism and Intrinsics

Última atualização: 28 de fevereiro de 2019Duração do vídeo: 6 min

A discussion of expressing data parallelism.

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